Find research datasets worth reusing

opencc-by-4.0Mar 2024View details →

Slice-by-Slice X-ray Tomography dataset of Dog Toy

This submission contains a dataset used in the paper "Ajinkya Kadu, Felix Lucka, and K. Joost Batenburg. "Single-shot Tomography of Discrete Dynamic Objects." arXiv preprint <a href="https://arxiv.org/abs/2311.05269">arXiv:2311.05269</a> (2023)." The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by <a href="https://info.tescan.com/micro-ct">TESCAN-XRE NV,</a> located in the FleX-ray Lab at the <a href="https://www.cwi.nl/en/">Centrum Wiskunde & Informatica (CWI)</a> in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT). To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector, which results in 956 detector pixel with an effective length of 149.6 μm each. Between source and detector there is a rotation stage, upon which the sample was mounted. The sample that we imaged was a dog toy in a shape of a bone made of a rubber. The X-ray tube voltage was 90kV and a copper filter was used to block the low-energy part of the spectrum to limit beam-hardening artifacts. The source-to-detector distance was 487.9 mm, while the source-to-origin of the sample was 374.5 mm in a fan-beam geometry. We acquired 673 z-slices with 0.25 mm distance between slices. Further information about the technical details of X-ray CT can be found in the <a href="https://arxiv.org/abs/2311.05269">above paper</a> and in  Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka “2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", <a href="https://doi.org/10.1038/s41597-023-02484-6">Sci Data 10, 576 (2023)</a> or  <a href="https://arxiv.org/abs/2306.05907">arXiv:2306.05907 (2023)</a> The upload consists of two files, namely: <ol> <li>GrayBone90kV4Filter.zip: contains the raw measurement data.</li> <li>GrayBone90kV4FilterPreprocessed.mat: contains preprocessed data to be used in the MATLAB script provided to do pseudo-dynamic tomography. It also contains reference reconstruction obtained via Filtered Back Projection (FBP) algorithm. </li> </ol> In the Github repository <a href="https://github.com/ajinkyakadu/DynamicXRayCT">https://github.com/ajinkyakadu/DynamicXRayCT</a>, we provide the scripts to read and process the raw data. The Github repository also contains all the scripts to reconstruct the dynamic solution using advanced algorithms. Furthermore, the raw data formats are described in great details in <a href="https://www.nature.com/articles/s41597-023-02484-6">Kiss et al 2023</a> paper referenced above.

opencc-by-4.0Jan 2022View details →

Static Stack-Preserving Intra-Procedural Slicing of WebAssembly Binaries

# About this artifact This artifact contains the implementation and the results of the evaluation of a static slicer for WebAssembly described in the ICSE 2022 paper titled "Static Stack-Preserving Intra-Procedural Slicing of WebAssembly Binaries". The artifact contains a docker image (`wassail-eval.tar.xz`) that contains everything necessary to reproduce our evaluation, and the actual data resulting from our evaluation: 1. The implementation of our slicer (presented in Section 4.1) is included in    the docker machine, and is available publicly here:    https://github.com/acieroid/wassail/tree/icse2022 2. Test cases used for our evaluation of RQ1 are included in the docker machine    and in the `rq1.tar.xz` archive. 3. The dataset used in RQ2, RQ3, and RQ4 is included in the docker machine. 4. The code needed to run our evaluation of RQ2, RQ3, and RQ4 is included in the    docker machine. 5. The scripts used to generate the statistics and graphs that are included in    the paper for RQ2, RQ3, and RQ4 are included in the docker machine and as the    `*.py` files in this artifact. 6. The data of RQ5 that has been used in our manual investigation is included in    the docker machine and in the `rq5.tar.xz` archive, along with    `rq5-manual.txt` detailing our manual analysis findings. # How to obtain it Our artifact is available on Zenodo at the following URL: https://zenodo.org/record/5821007 # Setting up the Docker image ## Downloading The Artifact The artifact is available at the following URL: https://zenodo.org/record/5821007 ## Loading The Docker Image Once the artifact is downloaded in the file `icse2022slicing.tar.xz`, it can be extracted and loaded into Docker as follows (this takes a few minutes): ``` docker import icse2022slicing.tar.xz ``` To simplify further commands, you can tag the image using the printed sha256 hash of the image: if the `docker import` command resulted in the hash `54aa9416a379a6c71b1c325985add8bf931752d754c8fb17872c05f4e4b52ea2`, you can run: ``` docker tag 54aa9416a379a6c71b1c325985add8bf931752d754c8fb17872c05f4e4b52ea2 wassail-eval ``` Once the Docker image has been loaded, you can run the following commands to obtain a shell in the appropriate environment: ``` docker volume create result docker run -it -v result:/tmp/out/ wassail-eval bash su - opam ``` # Reproducing results of RQ1 Our manual translations of the "classical" examples are included in the `rq1/` directory (available in the docker image and in `rq1.tar.xz`). We include the slices computed by our implementation in the `rq1/out/` directory. A slice can be produced for each example in the docker image as follows, where the first argument is the name of the program being sliced, the second the function index being sliced, the third the slicing criterion (indicated as the instruction index, where instructions start at 1), and the last argument is the output file for the slice: ``` cd rq1/ wassail slice scam-mug.wat 5 8 scam-mug-slice.wat wassail slice montreal-boat.wat 5 19 montreal-boat-slice.wat wassail slice word-count.wat 1 41 word-count-slice1.wat wassail slice word-count.wat 1 43 word-count-slice2.wat wassail slice word-count.wat 1 39 word-count-slice3.wat wassail slice word-count.wat 1 45 word-count-slice4.wat wassail slice word-count.wat 1 37 word-count-slice5.wat wassail slice agrawal-fig-3.wat 3 38 agrawal-fig-3-slice.wat wassail slice agrawal-fig-5.wat 3 37 agrawal-fig-5-slice.wat ``` The slice results can then be inspected manually, and compared with the original version of the .wat program to see which instructions have been removed, or with the expected solutions in the `out/` directory, e.g. by running: ``` diff word-count-slice1.wat out/word-count-slice1.wat ``` (No output is expected if the slice is correct) # Reproducing results of RQ2, RQ3, and RQ4 For these RQ, we include the data resulting from our evaluation, but we also allow reviewers to rerun the full evaluation if needed. However, such an evaluation requires a heavy machine and takes quite some time (4-5 days to run to completion with a 4 hours timeout). In our case, we used a machine with 256 GB of RAM and a 64-core processor with HyperThreading enabled, allowing us to run 128 slicing jobs in parallel. ## Runnig the Evaluation We explain how to run the full evaluation, or only a partial evaluation below. One can directly skip to the next section and reuse our raw evaluation results, provided alongside this artifact. ### Running the Full Evaluation In order to reproduce our evaluation, you can run the following commands in the docker image. It is recommended to run them in a tmux session if one wants to inspect other elements in parallel (tmux is installed in the docker image). The timeout (set to 4 hours per binary, like in the paper) can be decreased by editing the `evaluate.sh` script (vim is installed in the docker image). This is expected to take 2-3 days of time, on a machine with 128 cores. In order to produce only partial results, see the next section. ``` cd filtered cat ../supported.txt | parallel --bar -j 128 sh ../evaluate.sh {} ``` The results are outputted in the `/tmp/out/` directory. ### Running a Partial Evaluation If one does not have access to a high-end machine with 128 cores nor the time to run the full evaluation, it is possible to produce partial results. To do so, the following commands can be run. This will run the evaluation on the full dataset in a random order, which can be stopped early to represent a partial view of our evaluation, on a random subset of the data. In order to gather more datapoints, it is also advised to decrease the timeout in the `evaluate.sh` file, for example to 20 minutes by setting `TIMEOUT=20m` with `nano evaluate.sh`. The number of slicing jobs running in parallel can also be decreased to match the number of processors on the machine running the experiments (the `-j 128` argument in the following command runs 128 parallel jobs) ``` sudo chown opam:opam /tmp/out/ cd filtered shuf ../supported.txt | parallel --bar -j 128 sh ../evaluate.sh {} ``` The evaluation results will be stored in the `/tmp/out/` directory. ### Skipping the Evaluation Run Instead of rerunning the evaluation, one can rely on our full results included in the `data.txt.xz` and `error.txt.xz` archives. These can simply be downloaded from within the Docker machine and extracted in `/tmp/out/`: ``` cd /tmp/out/ wget https://zenodo.org/record/5821007/files/data.txt.xz wget https://zenodo.org/record/5821007/files/error.txt.xz unxz data.txt.7z unxz error.txt.7z ``` ## Processing the data In order to process this data, we included multiple python script. These require around 100GB of RAM to load the full dataset in memory. The scripts should be run with Python 3. When running this in the docker image, first run `cd /tmp/out/ && cp /home/opam/*.py ./` - To count the number of functions sliced, run `cut -d, -f 1,2 data.txt | sort   -u | wc -l`. This takes around 6 minutes to run on the full dataset. - To count the total number of slices encountered, run `wc -l data.txt   error.txt`. This takes around 15 seconds to run. - To count the number of errors encountered, run `wc -l error.txt`. This takes   around 1 second to run. - To produce data and graphs regarding the sizes and timing, run `python3   statistics-and-plots.py`. This will output the statistics presented in the   paper, along with Figure 2 (rq2-sizes.pdf) and Figure 3 (rq2-times.pdf). This   script takes around 35 minutes to run. - To find the executable slices that are larger than the original programs, run   `python3 larger-slices.py > larger.txt`. This script takes around 2h30 to   run. It will list the slice using the notation `filename function-sliced   slicing-criterion` in the larger.txt file, from which the slice can be   recomputed by running `wassail slice function-sliced slicing-criterion   output.wat` in the docker image. It will also output statistics regarding   these slices, which you can easily inspect by running `tail larger.txt`. - To investigate slices that could not be computed, run:   ```   sed -i error.txt -e 's/annotation,/annotation./'   python3 errors.py   ```   This will take a few seconds to run and will print a summary of the errors   encountered during the slicing process, and requires some manual sorting to map   to the categories we discuss in the paper. Here is a summary of the errors   encountered and their root cause: ### Root Cause: Unsupported Usage of br_table Error: (Failure"Invalid vstack when popping 2 values") Error: (Failure"Spec_inference.drop: not enough elements in stack") Error: (Failure"Spec_inference.take: not enough element in var list") Error: (Failure"unsupported in spec_inference: incompatible stack lengths (probably due to mismatches in br_table branches)") ### Root Cause: Unreachable Code Error: (Failure"Unsupported in slicing: cannot find an instruction. It probably is part of unreachable code.") Error: (Failure"bottom annotation") Error: (Failure"bottom annotation. this an unreachable instruction") # RQ5: Comparison to Slicing C Programs For this RQ, we include the following data in the `rq5.7z` archive, and in the `rq5/` directory in the docker image: - The slicing subjects in their C and textual wasm form in `rq5/subjects/` - The CodeSurfer slices in their C and textual wasm form in `rq5/codesurfer/` - Our slices in their wasm form in `rq5/wasm-slices/` As this RQ requires heavy manual comparison, we do not expect the reviewers to reproduce all of our results. We include a summary of our manual investigation in `rq5-manual.txt`. In order to validate these manual findings, one can for example inspect a specific slice. For example, the following line in `rq5-manual.txt`: ``` adpcm_apl1_565_expr.c.wat INTERPROCEDURAL ``` can be validated as follows: ``` cd ~/ # This generates a trimmed down version of the CodeSurfer slice, only containing the function of interest wassail count-in-slice rq5/codesurfer/adpcm_slices/adpcm_apl1_565_expr.c.wat slice.wat # This compares the CodeSurfer slice with our slice diff --side-by-side slice.wat rq5/adpcm_apl1_565_expr.c.wat ``` In this case, most extraneous instructions are present in the CodeSurfer slices, at the end of the function. This indicates that these are present in order to preserve interprocedural behavior, which corresponds to the `INTERPROCEDURAL` tag in the `rq5-manual.txt`

opencc-by-4.0Jun 2022View details →

Cortical slice labelled with anti GFP and VAMP2 antibodies - sample image for software testing of "Contacting synapse" protocol

Image 1.tif is a Brain slice. This 16 bits confocal stack of pictures ((801x711 pixels x33 z slices - pixel size 78.17 nm) of a brain slice has been taken at 93x (LeicaHC PL APO CS2 93x/1.30 GLYC) in sequential mode with two channels : one dedicated to the GFP detection, and the other one to synpatic boutons labelled with VAMP2 protein. VAMP2 protein are expressed at glutamatergic presynaptic sites and is usually found apposed to Post Synaptic Density. This is a good sample to test "contacting synapse" software. Here GFP cells were electroporated with various plasmid. The aim of the software is to identify if expression of those plasmid within the GFP labelled cell, influence the density of synapse contacting this GFP cells. Here presynaptic contact are identified through the use of antibodies to VAMP2 proteins.

opencc-by-4.0Sep 2022View details →

MicroCT scans of a hybrid poplar leaf dehydrating, with annotated slices for model training

Dataset of a leaf segment of a hybrid poplar (P. maximowiczii x P. nigra ‘Max3’) leaf scanned using microcomputed tomography (microCT) over time as it dehydrates.   Data acquisition methodology Plants were brought to the TOMCAT tomographic beamline of the Swiss Light Source at the Paul Scherrer Institute (Villigen, Switzerland). Before microCT scanning, a young fully expanded leaf was detached from the plant and a short strip (0.4 x 1.5 cm) was cut between second-order veins. The base of the strip was wrapped in polyimide tape and inserted into a styrofoam block fixed on a sample holder. The strip was immediately scanned by imaging 1801 projections of 100 ms under a beam energy of 21 keV and a magnification of 40x, yielding a final voxel size of 0.1625 µm (field of view: ~416x416x312 µm). The leaf was left to dehydrate in the holder and additional scans were taken 10, 20, 25, and 30 minutes after the initial scan. Scanned projections were reconstructed to a transverse view using both absorption (gridrec; Marone et al. 2012) and phase contrast enhancement (Paganin et al. 2002) reconstruction.   Dataset description On the reconstructed images a region of interest was identified using a paradermal view (i.e. top to bottom of the leaf) and used to manually align the scans of each time step. Thereafter, all images were cropped to that ROI, ensuring that the same region of the leaf was present in all image stacks. For all stacks, files start with: DEHYDRATION_small_Leaf4_time_N_ where N is the time point, with values from 1 to 5 equaling 0, 10, 20, 25, and 30 minutes. Following this prefix is either GRID (gridrec reconstruction), PAGANIN (phase contrast enhancement reconstruction), or LABELLED (hand labelled slices or ground truth). For GRID and PAGANIN, 8-bit grayscale stacks are provided. The AOI suffix indicates the region of interest. Stacks have been hand labelled over three orientations (for visual examples of the orientations see <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_Sections_order_time1.png?versionId=26fc15aa-702e-4052-b162-702cc567634c">Labeled_Sections_order_time1.png</a> and <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_Sections_order_time2.png">Labeled_Sections_order_time2.png</a>): <ol> <li>CROSS (cross sectional, or transverse, view)</li> <li>LONGI (longitudinal view: similar to cross sectional view but starting normal to it, i.e. along the depth of the stack starting from the left of the cross-sectional view)</li> <li>PARADERMAL (top to bottom view: starting at the upper epidermis)</li> </ol> A general idea of the slice range within one LABELLED stack is presented after the orientation, as: STARTtoENDbyRANGE The exact position of the labelled slices for each time point can be found in the <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_slices_positions.txt?versionId=93d7e22f-9f07-4f98-8c49-d93e9a2e1ce5">Labeled_slices_positions.txt </a>file. Note that one-based indexing is used (as in ImageJ), not zero-based indexing (as in e.g. Python).   References Marone F, Stampanoni M. 2012. Regridding reconstruction algorithm for realtime tomographic imaging. Journal of Synchrotron Radiation 19: 1029–1037. Paganin D, Mayo SC, Gureyev TE, Miller PR, Wilkins SW. 2002. Simultaneous phase and amplitude extraction from a single defocused image of a homogeneous object. Journal of Microscopy 206: 33–40.

opencc-by-4.0Jul 2023View details →

EDoS_Network_Slicing_K8S

The dataset includes raw resource usage and service performance metrics collected from CNFs of vCDN slices deployed on a cloud-native platform (kubernetes). The The dataset serves to train ML models for mitigating EDoS attacks by detecting anomalous resource scaling operations caused by application-layer DDoS attacks. The raw resource usage and performance metrics data have been recorded from two vCDN slices over a period of 5 days. A time series for each resource usage and performance metric was recorded using a data sampling period of 300s. The training data were collected during the first 4 days of attack-free activity. The 5th day served to create the testing dataset which includes data for normal activity as well as anomalous activity caused by application-layer DDoS attacks (specifically Hulk and Slowloris attacks) executed on different periods of the day. A training dataset and a testing dataset, containing the raw multivariate time series data, are generated for each vCDN slice's CNF with a total of  5401 and 2701 samples, respectively. For each slice, we provide the anomaly labels, which correspond to the attack periods.

Data for: Epicardial slices: an innovative 3D organotypic model to study epicardial cell physiology and activation.

Raw data set for Npj Regenerative Medicine article: Epicardial slices: an innovative 3D organotypic model to study epicardial cell physiology and activation.

opencc-by-4.0Oct 2022View details →

Multiple Particle Tracking Data from Neonatal Organotypic Rat Brain Slices

The data includes statistical features generated from raw multiple particle tracking data from videos collected during three independent experiments: (1) 5 different brain regions, (2) 3 different treatment conditions in the brain, and (3) 5 different brain ages.    <table> <thead> <tr> <th scope="col">Feature</th> <th scope="col">Model Abbreviation</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>alpha</td> <td>alpha</td> <td>Exponent of the anomalous diffusion equation.</td> </tr> <tr> <td>Effective diffusion coefficient</td> <td>D_fit</td> <td>Coefficient of the anomalous diffusion equation</td> </tr> <tr> <td>Kurtosis</td> <td>kurtosis</td> <td>The fourth moment of the projected positions on the dominant eigenvector of the radius gyration tensor (T).</td> </tr> <tr> <td>Asymmetry1</td> <td>asymmetry1</td> <td>Characterizes the asymmetry of the trajectory. Asymmetry1 equals 0 for circularly symmetric trajectories and 1 for linear trajectories.</td> </tr> <tr> <td>Asymmetry2</td> <td>asymmetry2</td> <td>The ratio of the smaller to larger principal radius of gyration.</td> </tr> <tr> <td>Asymmetry3</td> <td>asymmetry3</td> <td>An asymmetry feature that accounts for non-cylindrically symmetric point distributions.</td> </tr> <tr> <td>Aspect ratio</td> <td>AR</td> <td>The ratio of the kong and short side of the trajectory's minimum bounding rectangle. Perfectly symmetric trajectories have an aspect ratio of 1, and aspect ratio increases as trajectories become more elongated. </td> </tr> <tr> <td>Elongation</td> <td>elongation</td> <td>An estimation of amount of extension of the trajectory from its centroid. </td> </tr> <tr> <td>Boundedness</td> <td>boundedness</td> <td>Boundedness quantifies how much a particle with diffusion coefficient Deff is restricted by a circular confinement of radius r when diffusing for a period of time \(N\Delta t \)</td> </tr> <tr> <td>Fractal Dimension</td> <td>fractal_dim</td> <td>Fractal dimension is a measure of how "complicated" a self similar figure is. </td> </tr> <tr> <td>Trappedness</td> <td>trappedness</td> <td>The probability (\(\textit{P}_{\textit{t}} \)) that a particle with duffusion coefficient Deff is trapped in a region (r0) for a period of time \(N\Delta t \). </td> </tr> <tr> <td>Efficiency</td> <td>efficiency</td> <td>The ratio of the squared net displacement to the sum of step lengths. </td> </tr> <tr> <td>Straightness</td> <td>straightness</td> <td>The ratio of the net displacement to the sum of step lengths. </td> </tr> <tr> <td>MSD Ratio</td> <td>MSD_ratio</td> <td>MSD ratio characterizes the shape of the MSD curve. For Brownian motion, it is 0; For restricted motion it is < 0; For directed motion it is > 0. </td> </tr> <tr> <td>Frames</td> <td>frames</td> <td>The total number of frames the trajectory spans. </td> </tr> <tr> <td>Effective Diffusion Coefficient 1</td> <td>Deff1</td> <td>Effective diffusion coefficient at 0.33 s.</td> </tr> <tr> <td>Effective Diffusion Coefficient 2</td> <td>Deff2</td> <td>Effective diffusion coefficient at 3.3s. </td> </tr> </tbody> </table> Mean values were calculated based on surrounding datapoints for alpha, D_fit, kurtosis, asymmetry1, asymmetry2, asymmetry3, AR, elongation, boundedness, fractal_dim, trappedness, efficiency, straightness, MSD_ratio, Deff2, and Deff2.

opencc-by-sa-4.0Jul 2024View details →

MicroCT slices of reproductions of Minoan ceramic cups

MicroCT data have been acquired at the Multidisciplinary Laboratory of the Abdus Salam International Centre for Theoretical Physics. The microCT scans were carried out by using a sealed X-ray source (Hamamatsu L8121–03) with a focal spot size of 5 μm and a flat panel detector (Hamamatsu C7942SK-25; pixel size of 50μm) according to the following parameters: 110kV, 90μA, exposure time/projection of 2 s, 1440 projections of the samples over 360°. The X-ray beam was filtered by a 0.01 mm-thick copper absorber. The final slices were reconstructed using the commercial software DigiXCT (Digisens) in 32-bit format at an isotropic voxel size of 40 μm. The uploaded slices have been converted in 8-bit format.

opencc-by-4.0Jul 2023View details →

Dataset of CT scans, slice photographs, and visual browning scores of 120 'Kanzi' apples

SummaryThis dataset is a collection of CT scans, slice photographs, and visual browning scores of 120 'Kanzi' apples. DescriptionSample informationIn 2022, 120 'Kanzi' apples that had been stored under CA conditions (4 °C, 1 kPa O2, 1.5 kPa CO2) for 8 months were obtained from FruitMasters, The Netherlands. The fruit was grown in orchards surrounding Geldermalsen, the Netherlands, and harvested at physiological maturity in 2021.CT acquisitionThe dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists of a cone-beam microfocus polychromatic X-ray point source, and a 1944x1536 pixel, 14-bit, flat detector panel (Dexela1512NDT). Full details can be found in [Coban 2020].  A cone beam geometry with a circular trajectory was used to acquire 1440 projection images at an exposure time of 100ms, a tube peak voltage of 90kV, a current of 550uA, and 2 times binning, halving the detector resolution. Volumes were reconstructed with the FDK algorithm and a voxel size of 129.3um. Beam hardening correction was used from the FleXbox package [Kostenko 2020]. To make sure that the grey values could be compared between scans the spectral sensitivity of the scanner was first estimated for each scan individually and the average of these estimates was used for beam hardening correction on all CT scans. All apples were scanned with the stem side on top. Moreover, a line was drawn on all apples from the stem to the calyx. The apples were put in the CT scanner so that the line was facing the X-ray source.The CT volumes are saved as .tiff stacks. All volumes have been cropped to remove the background.Slicing and photograph acquisitionOne day after CT scanning, the apples were sliced using a modified meat-slicing machine (CaterChef, house brand of EMGA, Mijdrecht, The Netherlands), which is illustrated in the file slicing_machine_labels.png. The sliding surface of the meat-slicing machine was replaced by a transparent acrylic sheet, and a camera was placed behind the slicing surface. While in the machine, each apple was kept in place by a suction cup so that it could not rotate during the slicing. All apples were sliced from the stem end to the calyx end, with a slice thickness of roughly 4mm. Every time before slicing, a picture was taken of the remaining part of the apple through the transparent sliding surface. To ensure that all apples were roughly aligned to the CT scans, the apples were oriented so that the line drawn earlier was on top.The slice photographs are saved as .png files. All photographs have been cropped to remove the background and to center the apple in the image.Visual browning scoresAfter each apple was sliced it was also visually inspected, and a score from one to ten was given to describe the amount of browning in the apple.Related paperWhen using this dataset please consider citing the following paper. It explains how the dataset was collected and used for the first time:Dirk Elias Schut, Rachael Maree Wood, Anna Katharina Trull, Rob Schouten, Robert van Liere, Tristan van Leeuwen, Kees Joost Batenburg, "Detecting internal disorders in fruit by CT. Part 1: Joint 2D to 3D image registration workflow for comparing multiple slice photographs and CT scans of apple fruit", 2023, <a href="https://arxiv.org/abs/2310.01987">arXiv preprint arXiv:2310.01987</a> Research group This dataset was produced in a collaboration between the Computational Imaging group at Centrum Wiskunde & Informatica (CWI), and GREEFA.<a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</a> <a href="https://www.greefa.com/nl/">https://www.greefa.com/nl/</a>Contact details dirk [dot] schut [at] cwi [dot] nlAcknowledgments This work was funded by the Dutch Research Council (NWO) through the UTOPIA project (ENWSS.2018.003). The authors also acknowledge TESCAN-XRE NV for their collaboration and support of the FleX-ray laboratory.

opencc-by-4.0Aug 2020View details →

Raw data for "Multi-slice ptychography enables high-resolution in situ measurements in extended chemical reactors"

Raw data used in "Multi-slice ptychography enables high-resolution in situ measurements in extended chemical reactors" by M. Kahnt, L. Grote, D. Brückner, M. Seyrich, F. Wittwer, D. Koziej and C.G. Schroer.

opencc-by-4.0Sep 2020View details →

Example data set for 2D adaptive slice-specific z-shimming

Input data and corresponding results for the scripts provided on github (https://github.com/neuroimaging-mug/R2s-mapping) for adpative slice-specific z-shimming in presence of macroscopic field variations. Please unzip all file in the repository path of ‘R2s-mapping’.

opencc-by-4.0Nov 2020View details →

A Slice of the Research Cake: The Impact of Open Science in Africa

Episode Summary:  In this episode we talk to Joy Owango, Founding Director at Training Centre in Communication. We discussed how Open Science is democratising research and access to data and publishing in Africa, the importance of ownership in research, and the challenges inherent in widespread change.  Originally we interviewed another two guests who are involved in the Open Science movement on the African continent: Osman Aldirdiri, founding director of Open Sudan initiative and Jo Havemann, co-founder of the AfricArXiv. Due to technical difficulties we can only publish the audio from Joy Owango. However, we do have a <a href="https://drive.google.com/file/d/1sOhAaiWjAUwQlDMuaqd5YCC3SOhXDFSP/view?usp=sharing">transcript of the full interview</a> with all three guests. Episode Links: <ul> <li><a href="https://info.africarxiv.org/">AfricArXiv</a> <a href="https://twitter.com/AfricArxiv">Twitter</a> </li> <li>Joy Owango <ul> <li><a href="https://www.tcc-africa.org/">Training Centre in Communication</a></li> <li><a href="https://twitter.com/JoyOwango">Twitter</a></li> </ul> </li> <li>Osman Aldirdiri <ul> <li><a href="https://blog.okfn.org/2017/03/30/open-data-day-sudan-2017-openness-for-advancing-research-and-discovery/">Open Sudan's Open Data Day</a></li> <li><a href="https://twitter.com/aldirdiri">Twitter</a></li> </ul> </li> <li>Jo Havemann <ul> <li><a href="https://access2perspectives.com/team/jo-havemann/">Access2Perspectives </a></li> </ul> </li> </ul>

opencc-by-4.0Feb 2024View details →

Generation of a network slicing dataset: the foundations for AI-based B5G resource management

This paper introduces a comprehensive network slicing dataset designed to empower artificial intelligence (AI), and other data-based resource management and network performance prediction applications, in 5G and beyond (B5G) networks. The dataset, generated through a packet-level simulator, captures the complexities of network slicing considering the three main network slice types defined by 3GPP: Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Internet of Things (mIoT). It includes a wide range of network scenarios with varying topologies, slice instances, and traffic flows. The included scenarios consist of transport networks, excluding the RAN infrastructure. Each sample consists of pairs of (network scenario, performance metrics). The network configuration includes network topology, traffic characteristics, routing configurations, while the performance metrics are the delay, jitter, and loss for each flow. The dataset is generated with a custom network slicing admission control module, enabling the simulation of realistic scenarios without violating SLAs. This network slicing dataset is a valuable asset for the research community, unlocking opportunities for innovations in 5G and B5G networks.

opencc-by-4.0Sep 2024View details →

Experimental data for: "Multi-slice electron ptychographic tomography for three-dimensional phase-contrast microscopy beyond the depth of field limits"

This is the raw experimental data for the paper: "Multi-slice electron ptychographic tomography for three-dimensional phase-contrast microscopy beyond the depth of field limits" Now also including code to recreate figures, and data from alignment and multi-slice ptychography reconstructions. The data is in zarr format and can be read with the zarr python library. It also contains metadata in a dictionary.

opencc-by-4.0Feb 2022View details →

Supporting data for manuscript describing Slice and Dice method to measure NMR relaxation with nested experiments

This is a supporting dataset for the manuscript "Slice and Dice: Nested Spin-lattice Relaxation Measurements" by W. Trent Franks, Jacqueline Tognetti and Józef R. Lewandowski. <ul> <li>NMR_data.zip : Raw NMR data in the Bruker format for the experiments presented in the manuscript. The file expands to a directory called "Raw NMR Data" that contains: <ul> <li>ReadMe_NMR_data.txt - describing the datasets included in the file.</li> <li>Record 1: 13C\(^\alpha\) individual experiment. Pulse program name: hRCH_CT1</li> <li>Record 2: 13C' individual experiment. Pulse program name: hCOcaH_SP_T1</li> <li>Record 3: 15N individual experiment. Pulse program name: hRNH_NT1b</li> <li>Record 10: 13C\(^\alpha\) + 13C' + 15N Slice & Dice experiment. Pulse program name: hR[COca,Ca,N]Ha_T10818 corresponding to the final sequence: hR[N,COca,Ca]HR_T1</li> </ul> </li> <li>Pulse_program.zip: The pulse program and include file for the Slice and Dice experiment described in the manuscript. The pulse program in Bruker format (war.hR[COca,Ca,N]H_T1 - this is a text file that can be opened with any text editor) was tested on a Bruker Avance III HD console. Both the pulse program file, war.hR[COca,Ca,N]H_T1, and include file, HCN_defs.incl, need to be placed in the pulse program directory (/opt/topspinXX/exp/stan/nmr/lists/pp/user where XX is replaced with the version of Topspin). The file expands to a directory "Pulse_program_incl" that contains: <ul> <li>war.hR[COca,Ca,N]H_T1 - pulse program</li> <li>HCN_defs.incl - include file</li> <li>ReadMe_SliceDice_pp.txt - details on how to set up the experiment.</li> </ul> </li> <li>HowToProcessSliceAndDice.pdf : Instructions on how to process Slice and Dice experiment in Topspin.</li> <li>MultiR1list.zip: A program written in Python 3 required to calculate delay lists for the nested experiment to be included in the pulse program. The file expands to a directory MultiT1list directory that contains: <ul> <li>MultiT1list.py - the program</li> <li>ReadMe_MultiT1list.txt - instructions on how to use the program</li> </ul> </li> <li>SNDProcguide.py.zip: A program written in Python 2 (SNDProcguideV2.py), which generates macro for processing and sorting 2D planes in Topspin. The script also provides some tips on setting parameters for different 2Ds and sorted lists of relaxation delays. Example output of the script is also included. The parameters in the script are set for the supplied example data.</li> <li>HowToProcess.mp4 - a video working through an example of processing Slice and Dice data.</li> </ul>

Text-fig. 2. CT slices on Block 2. Details of other skeletal parts (a). The familiar shape of an ammonite (a, c). Holes, cracks and empty cavities in both the limestone matrix and within the vertebrate fossil (b, c). Heterogeneity of the 'tuffeau' limestone, the more porous areas of the matrix clearly distinguishable from the more compact ones (c). Ferric nodules (c). in Hidden Treasures Uncovered: Successful Detection Of Fossils Below The Surface In Large Limestone Blocks Using A Standard Medical X-Ray Ct Scanner

Text-fig. 2. CT slices on Block 2. Details of other skeletal parts (a). The familiar shape of an ammonite (a, c). Holes, cracks and empty cavities in both the limestone matrix and within the vertebrate fossil (b, c). Heterogeneity of the 'tuffeau' limestone, the more porous areas of the matrix clearly distinguishable from the more compact ones (c). Ferric nodules (c).

Text-fig. 1. CT slices on Block 1. Details of the internal bone structure (a, b), teeth (b, c). Invertebrate imprints (a, c). Holes, cracks and empty cavities in both the limestone matrix and within the vertebrate fossil (b). in Hidden Treasures Uncovered: Successful Detection Of Fossils Below The Surface In Large Limestone Blocks Using A Standard Medical X-Ray Ct Scanner

Text-fig. 1. CT slices on Block 1. Details of the internal bone structure (a, b), teeth (b, c). Invertebrate imprints (a, c). Holes, cracks and empty cavities in both the limestone matrix and within the vertebrate fossil (b).

Text-fig. 3. CT slices on Block 3. Invertebrate moulds (a, c) and remains of their hard skeletons (a, b). Large areas of limestone matrix hold either only a few scattered invertebrates or no fossil at all (b, c). Ring artefacts seen close to the isocentre of the scan (b, c) are a well-known phenomenon caused by the X-ray beams traversing the block at an insufficient radiation dose (as expected in such a large block of dense material), and are not part of any physical structure present therein (Triche et al. 2019). in Hidden Treasures Uncovered: Successful Detection Of Fossils Below The Surface In Large Limestone Blocks Using A Standard Medical X-Ray Ct Scanner

Text-fig. 3. CT slices on Block 3. Invertebrate moulds (a, c) and remains of their hard skeletons (a, b). Large areas of limestone matrix hold either only a few scattered invertebrates or no fossil at all (b, c). Ring artefacts seen close to the isocentre of the scan (b, c) are a well-known phenomenon caused by the X-ray beams traversing the block at an insufficient radiation dose (as expected in such a large block of dense material), and are not part of any physical structure present therein (Triche et al. 2019).

opencc-by-4.0Mar 2024View details →

Confocal dataset of Purkinjie cells from clarified cerebellum slices from L7GFP mouse

<div> <div> <h3>TITLE: Confocal dataset of Purkinjie cells from larified cerebellum slices from L7GFP mouse  </h3> </div> <div>  SUMMARY:   </div> <div> We have processed cerebellar material from transgenic mice expressing the green fluorescent protein (GFP). Specifically, in L7GFP mice, the expression of GFP in Purkinje is specifically driven by the Pcp-2 promoter (Zhang, X., Baader, S. L., Biang, F., Müller, W., and Oberdick, J. (2001). High level Purkinje cell specific expression of green fluorescent protein in transgenic mice. Histochem. Cell Biol. 115, 455–464).  The sample were firstly clarified using the optimization of the CLARITY protocol developed by our group, and then acquired using a confocal microscope.    </div> <div>  EXPERIMENTAL METHODS AND MATERIALS:   </div> <div> Data contained in this folder belong to an adult male L7GFP mouse, processed as in Magliaro et al., 2016 (<a href="https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2016.00179/full#h3" target="_blank" rel="noreferrer noopener">https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2016.00179/full#h3</a>).  </div> <div>  Briefly, the L7GFP mouse was anesthetized with a lethal dose of 7% chloral hydrate and then perfused with 20 mL of Phosphate Buffered Saline and then 20 mL of a solution, containing 4% acrylamide, 0.05% bis-acrylamide and 0.25% VA-044 thermally triggered initiator at 4°C, as described in Chung et al. (2013) (Chung, K., Wallace, J., Kim, S.-Y., Kalyanasundaram, S., Andalman, A. S., Davidson, T. J., et al. (2013). Structural and molecular interrogation of intact biological systems. Nature 497, 332–337. doi: 10.1038/nature12107). The brain was extracted and submerged in 20 mL hydrogel solution for 3 days at 4°C in a 50 mL Falcon tube to allow gel diffusion into the tissue. Then a vacuum was applied to the tube for 10 min to enable hydrogel formation. Hydrogel polymerization was thermally initiated by incubating the infused tissue overnight at 37°C. At this point, the mouse cerebellum was isolated and then cut into 1 mm-thick coronal slices using a Leica VT1200S vibratome. The cut settings were reported in Mattei et al., 2015 (Mattei, G., Cristiani, I., Magliaro, C., and Ahluwalia, A. (2015). Profile analysis of hepatic porcine and murine brain tissue slices obtained with a vibratome. PeerJ 3:e932. doi: 10.7717/peerj.932). Each slice was finally placed in a 50 mL Falcon tube at 37°C with 20 mL of CLARITY clearing solution (200 mM Boric Acid and 4% Sodium Dodecyl Sulphate, pH adjusted to 8.5 by adding 1 M NaOH dropwise) for 5 days, as this is demonstrated to be the optimal clearing time in Magliaro et al., 2016 (<a href="https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2016.00179/full#h3" target="_blank" rel="noreferrer noopener">https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2016.00179/full#h3</a>).  </div> <div> </div> <div> Confocal microscopy data   </div> <div> Image stacks of samples were mounted on a glass slide with FocusClear™ (Celexplorer Labs Co., Hsinchu, Taiwan) and then acquired with a confocal microscope (Nikon A1).  The confocal settings were the following: 4.84 W laser power, emission and excitation wavelengths of 488 nm and 502 nm respectively.  Refer to metadata.xlsx file for detailed info. </div> <div>   </div> <div> Ethical approval  </div> <div> The mouse was obtained from the Department of Translational Research, New Technologies in Medicine and Surgery of the University of Pisa (Italy). Mice were used to perform the experiments, which were conducted in conformity with the European Communities Council Directive of 24 November 1986 (86/609/EEC and 2010/63/UE) and in agreement with the Italian DM26/14. Experiments were approved by the Italian Ministry of Health and Ethical Committee of the University of Pisa.   </div> <div>    </div> </div>