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FIGURE 2 in Non-destructive analysis of in situ ammonoid jaws by synchrotron radiation X-ray micro-computed tomography
FIGURE 2. Reconstructed tomographic images of the specimen (1) and its internal structure in median section (2). The lower and upper jaws are enlarged in (3) and (4), respectively.
FIGURE 5 in Non-destructive analysis of in situ ammonoid jaws by synchrotron radiation X-ray micro-computed tomography
FIGURE 5. Three-dimensional reconstruction of the upper and lower jaws preserved in the body chamber of the specimen. The reconstructed parts are inside the specimen (1). The jaws are preserved close to each other (2).
FIGURE 1 in Non-destructive analysis of in situ ammonoid jaws by synchrotron radiation X-ray micro-computed tomography
FIGURE 1. Left lateral (1), dorsal (2) and ventral (3) views of Phyllopachyceras ezoensis with preserved upper and lower jaws in situ within the body chamber. UMUT MM 27831 (modified from Tanabe et al., 2013).
FIGURE 7 in Non-destructive analysis of in situ ammonoid jaws by synchrotron radiation X-ray micro-computed tomography
FIGURE 7. Result of segmentation of the upper jaw of the specimen, from frontal (1), rear (2), left-lateral (3) views and the transverse section of the area (4) indicated as a square in (3). The three-dimensional reconstruction (5) shows areal distributions of the "chitinous" lamellae and the calcareous covering. The reconstruction of the transverse section (6), which corresponds to (4), shows the architecture of the outer lamella. The abbreviations are indicated in (5).
FIGURE 6 in Non-destructive analysis of in situ ammonoid jaws by synchrotron radiation X-ray micro-computed tomography
FIGURE 6. Result of segmentation of the lower jaw of the specimen, from lateral view which is restricted to its anterior and posterior portion (1). Three-dimensional reconstruction (2) suggests a wide distribution of calcareous material. The outer calcareous layer on the outer "chitinous" layer is partly taken off in (2). The transverse section of the area indicated as a square in (1) shows that the calcareous covering of the lower jaw also covers the internal surface of the "chitinous" lamella (3). The abbreviation is indicated in (2).
FIGURE 4 in Non-destructive analysis of in situ ammonoid jaws by synchrotron radiation X-ray micro-computed tomography
FIGURE 4. Linear absorption coefficient (LAC) of the internal portions of the specimen estimated by their mean luminance values in the tomographic images. The numbers (1)-(10) correspond to the materials in Table 1. The dashed lines indicate the known values for the materials (Chantler et al., 2005) that could be expected to be observed in the specimen. Note that glycine is the most dominant amino acid in jaws of Octopus vulgaris (Hunt and Nixon, 1981). The relationship between LAC values and luminance values is based on the assumption that the LAC values for the surrounding air are zero and that the crystals precipitated in the phragmocone are calcite.
FIGURE 3 in Non-destructive analysis of in situ ammonoid jaws by synchrotron radiation X-ray micro-computed tomography
FIGURE 3. Serial cross-sections of the body chamber portion of the specimen cut from the venter (1) to the dorsum (4), in which sectioned images of the upper jaw are shown. Note that the vertical stripes are due to the separated scanning.
FIGURE 3 in Using X-ray computed tomography analysis tools to compare the skeletal element morphology of fossil and modern frog (Anura) species
FIGURE 3. Morphological bone-to-bone comparison between the 'nominal' male (CP001) and 'actual' female (CP002) Xenopus laevis. The differences are colour-coded and show female (CP002) variance relative to the nominal bone of the male (CP001) which is depicted in the figure.
FIGURE 4 in Using X-ray computed tomography analysis tools to compare the skeletal element morphology of fossil and modern frog (Anura) species
FIGURE 4. Morphological bone-to-bone comparison between the 'nominal' Xenopus laevis (CP001) with the 'actual' fossil Xenopus sp. (ZM 71336)
FIGURE 2 in Using X-ray computed tomography analysis tools to compare the skeletal element morphology of fossil and modern frog (Anura) species
FIGURE 2. Bone cortex thickness analysis on a male Xenopus laevis (CP001) (A) and a fossil Xenopus sp. (ZM 71336) (B) depicted side by side in slice view from top view (1) and side view (3) and in a 3D colour-coded analysis (2 and 3).
FIGURE 1. A in Using X-ray computed tomography analysis tools to compare the skeletal element morphology of fossil and modern frog (Anura) species
FIGURE 1. A complete Breviceps montanus (Catalogue number ZR-050053) CT scan with segmentation of humerus and femur demonstrated.
Supporting dataset for "Influence of urban forms on -long-duration urban flooding: laboratory experiments and computational analysis"
<p>In this dataset, we provide two parts of data:</p> <p>(1) Figures in format .fig that are included in the main text and supplementary material</p> <p>(2) Experimental datasets for the five configurations, including flow depth, discharge partition, and the flow surface velocity,</p> <p>- the data is written in a .h5 file that can be read by different languages (ex. Python),</p> <p>- a document PDF and a text file are available to visualize the data structure</p> <p>- a code of Python for reading the data in the .h5 file </p> <p> </p>
Phantom imaging data and analysis macros for the article "Monochromatic computed tomography using laboratory-scale setup"
<p>The raw and processed data and analysis macros of the article <em>A.-P.</em> <em>Honkanen et S. J. Huotari, Monochromatic computed tomography using laboratory-scale setup, Scientific Reports (2023), doi:<a href="http://doi.org/10.1038/s41598-023-27409-6">10.1038/s41598-023-27409-6</a></em></p> <p>The data set consists of the raw and reconstructed computed tomography projection data taken of an PMMA phantom embedded with three different chemical species of selenium taken with a monochromatic X-ray imaging setup based on a laboratory-scale Johann-type crystal X-ray spectrometer. In addition to the imaging data, the set contains also the Jupyter Notebooks used to process and analyse the data. The details of the instrument and the analysis are presented in the article.</p> <p>The dataset is licensed under Creative Commons Attribution 4.0 International License <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>
Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"
<p>Software repository for the reproduction of the test cases from</p> <p><strong>Thomas Richter, Rolf Ulrich, Markus Janczyk:</strong> <em>Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"</em></p> <p>This software is used in particular for the reproducibility of the results.</p> <p>However, the algorithms can also be used directly for own purposes. If you have any questions about possibly necessary adaptations, please contact thomas.richter@ovgu.de.</p> <p>Parts of this repository</p> <p>General setup</p> <p><strong>Python</strong> collects all Python script. Here, <strong>Python/PythonTools</strong> are several internal functions, e.g. the realizations of KFE and random walks. <strong>Python/results</strong> and <strong>Python/pics</strong> are the directories where the results (figures and text-files) are put.</p> <p><strong>C++</strong> collects the C++ scripts.</p> <p>Case I</p> <p>Reproduces Case I of the paper (time-independent)</p> <ul> <li>Python/TestCase1.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE. It produces output in <strong>Python/pics</strong> and <strong>Python/results</strong>. These results will be used in <strong>C++/testcase1.cc</strong> (as reference solution) and by <strong>Python/TestCase1-Plot.py</strong></p> <ul> <li>C++/testcase1.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase1.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase1.py</strong> for computing errors.</p> <ul> <li>Python/TestCase1-Plot.py</li> </ul> <p>produces Fig. 6 of the paper. It requires the outputs of <strong>Python/TestCase1.py</strong> and <strong>C++/testcase1.cc</strong></p> <p>Case II</p> <p>Reproduces Case II of the paper (time-dependent thresholds and drift)</p> <ul> <li>Python/TestCase2.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE. It produces output in <strong>Python/pics</strong> and <strong>Python/results</strong>. These results will be used in <strong>C++/testcase2.cc</strong> (as reference solution) and by <strong>Python/TestCase2-Plot.py</strong></p> <ul> <li>C++/testcase2.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase2.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase2.py</strong> for computing errors.</p> <ul> <li>Python/TestCase2-Plot.py</li> </ul> <p>produces Fig. 7 of the paper. It requires the outputs of <strong>Python/TestCase2.py</strong> and <strong>C++/testcase2.cc</strong></p> <ul> <li>Python/TestCase2-AdjustRandomWalks.py</li> </ul> <p>runs simulations to reproduce Fig. 11 of the paper and implements the modification of the random walk strategy to limit oscillations.</p> <p>Case III</p> <p>Reproduces Case III of the paper (dependency of the accuracy on the derivative of the drift)</p> <ul> <li>Python/TestCase3.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE for a fixed discretization but with different values of the drift tau. It produces first part of Fig. 8.</p> <ul> <li>C++/testcase3.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase3.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase3.py</strong> for computing errors.</p> <ul> <li>Python/TestCase3-Plot.py</li> </ul> <p>produces second part of Fig. 8. Depends on the output of <strong>Python/TestCase3.py</strong></p> <p>Case IV</p> <p>Reproduces Case IV of the paper (accuracy and efficiency for Dirac initial data)</p> <ul> <li>Python/TestCase4.py</li> </ul> <p>runs the test-case with random walks, integral equation and with KFE for a refined discretizations.</p> <ul> <li>Python/TestCase4-Plot.py</li> </ul> <p>produces Fig. 9. Depends on the output of <strong>Python/TestCase4.py</strong></p> <ul> <li>Python/TestCase4-showsolution.py</li> </ul> <p>Solves with the KFE and plots the solution as surface plot over time and space variable. This skript is used to create Fig. 10 of the paper. Problem parameters and discretization can be adjusted at the top of the script. To test the different stabilization strategies, one can either adjust the value of theta, or one activates Rannacher time-marching by commenting in the marked lines in the skript PythonTools/kfe.py, here in kfe_ale(..)</p> <p>Data Fitting</p> <p>Python scripts to fit the KFE model to the Data published by Rolf Ulrich et al. in</p> <p><strong>R. Ulrich, H. Schröter, H. Leuthold, T. Birngruber</strong> <em>Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions.</em>Cognitive Psychology, 78 , 148–174</p> <ul> <li>Python/DataFitting-Simon.py</li> </ul> <p>runs the parameter fitting for the Simon task and produces data for Fig. 9 and Table 1.</p> <ul> <li>Python/Eriksen-Fletcher.py</li> </ul> <p>runs the parameter fitting for the Eriksen Fletcher task and produces data for Fig. 9 and Table 2.</p> <p>Installation & running the examples</p> <p>Python</p> <p>The python skripts can just be started. Just note that they depend on each other, i.e.: <strong>Python/TestCase1.py</strong> produces a reference solution that is required by <strong>C++/testcase1.cc</strong> and the results of both are needed in <strong>Python/TestCase1-Plot.py</strong></p> <p>The scripts only depend on standard packages like numpy or scipy and all Python environments should work. One suggestion is to use Spyder as part of Anaconda.</p> <p>C++</p> <p>The C++-programs are not intended for performing the simulations in a stand-alone application. Instead, the SDE is simulated for a given number of trials <strong>N_tr</strong> and a given time step <strong>dt</strong> and this simulation is repeated <strong>64</strong> times in order to estimate the average error. It should however be simple to use the scripts as basis for an efficient parallel simulation tool that uses multithreading.</p> <p>Configuration</p> <p>The C++ test cases must be compiled. The test cases are set up to use <strong>cmake</strong>. We suggest the following (in a Linux-environment or on a Mac using homebrew or MacPorts):</p> <ol> <li>Create a directory for compilation, e.g. <strong>C++/bin</strong> now called the <strong>bin-dir</strong></li> <li>In the <strong>bin-dir</strong> calls cmake by <strong>cmake ..</strong> (adjust the path, if the <strong>bin-dir</strong> is not a subdirectory of the <strong>C++-dir</strong>.</li> <li>Several options can be adjusted. In <strong>C++/bin</strong> call <strong>ccmake .</strong> to make all necessary changes.</li> </ol> <p>If you change the location of the <strong>bin-dir</strong> you will have to modify the run-scripts <strong>run-testcase[123].sh</strong>.</p> <p>Compilation</p> <p>Initially and whenever you change the code, the programs must be re-compiled</p> <ol> <li>In <strong>C++/bin</strong> just call <strong>make</strong></li> </ol> <p>Running the examples</p> <p>The programs are started in <strong>C++</strong>. For each of the test-case there is a skript to start the program.</p> <ol> <li>In <strong>C++</strong> call <strong>sh ./run-testcase1.sh</strong> (or <strong>sh ./run-testcase2.sh</strong>, etc.)</li> </ol> <p>Each script will start the programs several times. For <strong>Case I</strong>, <strong>Case II</strong> and <strong>Case IV</strong> the simulation is started on a sequence of finer and finer discretizations, for <strong>Case III</strong> the value of <em>tau</em> will be changed.</p> <p>The scripts store the output in <strong>C++/results</strong>. Old outputs will be overwritten! Further, the scripts read information about the reference solution from <strong>Python/resuts</strong>.</p> <p>The C++ programs use multithreading the OpenMP. If you do not specify the number of threads to be used, all available threads are taken including all hyperthreads. This is usually not efficient it is therefore advisable to set the number of threads by hand, e.g. by calling</p> <p><strong>export OMP_NUM_THREADS=8</strong></p> <p>before calling the run-scripts.</p> <p>License Information</p> <p>Initially the software has been written Thomas Richter, Otto-von-Guericke University Magdeburg, Germany in 2022, 2023 (thomas.richter@ovgu.de)</p> <p>You are free to use the scripts under the <em>Creative Commons Attribution 4.0 License</em>.</p>
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>
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>
Dataset for "Computer vision assisted decomposition analysis of atom probe tomography data"
<p>Dataset for the article "Computer vision assisted decomposition analysis of atom probe tomography data". APT measurements were performed by Marcus Hans at Materials Chemistry (RWTH Aachen University) using a CAMECA LEAP 4000X HR. Training data was created by Janis A. Sälker.</p> <p>Content:</p> <p>- 13 (V,Al)N and 3 (Ti,Al)N APT reconstructions (.epos file format) and the corresponding range file (.rrng file format).</p> <p>- Training data (images & masks) for 9 labeled (V,Al)N APT samples (h5 file format). Image data with key "image" of shape (2, number_of_slices, 608, 192), where 2 corresponds to the V- and Al-contribution/channel and 608/192 to the height/width of the images. Masks/labels with key "label" of shape (number_of_slices, 608, 192)</p> <p> </p>
Assessing Computational Notebook Understandability through Code Metrics Analysis
<p>Computational notebooks have become the primary coding environment for data scientists. Despite their popularity, research on the code quality of these notebooks is still in its infancy, and the code shared in these notebooks is often of poor quality. Considering the importance of maintenance and reusability, it is crucial to pay attention to the comprehension of the notebook code and identify the notebook metrics that play a significant role in their comprehension. The level of code comprehension is a qualitative variable closely associated with the user's opinion about the code. Previous studies have typically employed two approaches to measure it. One approach involves using limited questionnaire methods to review a small number of code pieces. Another approach relies solely on metadata, such as the number of likes and user votes for a project in the software repository. In our approach, we enhanced the measurement of the understandability level of notebook code by leveraging user comments within a software repository. As a case study, we started with 248,761 Kaggle Jupyter notebooks introduced in previous studies and their relevant metadata. To identify user comments associated with code comprehension within the notebooks, we utilized a fine-tuned DistillBERT transformer. We established a \emph{user comment based criterion} for measuring code understandability by considering the number of code understandability-related comments, the upvotes on those comments, the total views of the notebook, and the total upvotes received by the notebook. This criterion has proven to be more effective than alternative methods, making it the ground truth for evaluating the code comprehension of our notebook set. In addition, we collected a total of 34 metrics for 10,857 notebooks, categorized as script-based and notebook-based metrics. These metrics were utilized as features in our dataset. Using the Random Forest classifier, our predictive model achieved 85% accuracy in predicting code comprehension levels in computational notebooks, identifying developer expertise and markdown-based metrics as key factors.</p> <p> </p>
Data from: Hydrodynamic analysis of bioinspired vortical cross-step filtration by computational modelling
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Mining the health disparities and minority health bibliome: A computational scoping review and gap analysis of 200,000+ articles
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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.