Find research datasets worth reusing

Computational analysis of cortical neuronal excitotoxicity in a large animal model of neonatal brain injury

This is the dataset accompanying the manuscript: "Computational Analysis of Cortical Neuronal Excitotoxicity in a Large Animal Model of Neonatal Brain Injury" Panagiotis Kratimenos1,2,5 *, Abhya Vij5, Robinson Vidva6, Ioannis Koutroulis3,4,5, Maria Delivoria-Papadopoulos7**, Vittorio Gallo1,5, and Aaron Sathyanesan1,5* 1Center for Neuroscience Research, Children’s National Research Institute, Children’s National Hospital, Washington DC, USA 2Department of Pediatrics, Division of Neonatology, Children’s National Hospital, Washington DC, USA 3Department of Pediatrics, Division of Emergency Medicine, Children’s National Hospital, Washington, DC, USA 4Center for Genetic Medicine Research, Children’s National Research Institute and Department of Genomics and Precision Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC, USA 5George Washington University School of Medicine and Health Sciences, Washington DC, USA 6Digirobi Solutions, Bengaluru, Karnataka, India 7Department of Pediatrics, Drexel University College of Medicine, Philadelphia, PA, USA *Corresponding Authors: Panagiotis Kratimenos, MD, PhD: <a href="mailto:panagiotis.kratimenos@childrensnational.org">panagiotis.kratimenos@childrensnational.org</a> Aaron Sathyanesan, PhD: <a href="mailto:asathyanesan@childrensnational.org">asathyanesan@childrensnational.org</a> 111 Michigan Avenue, Washington, DC, 20010, USA

opencc-by-4.0Oct 2020View details →

Commodity Dataset | Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform

(Commodity data in raster format) Supplementary materials for “Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform” that had been published on Land MDPI (2020). doi:<a href="https://doi.org/10.3390/land9100377">10.3390/land9100377</a>  The data included: 1) Raster data of commodity maps (TIFF Compressed in ZIP) 2) READ ME for the dataset (DOCX) 3) Legend for raster data in ArcGIS Format (LYR)

Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT) dataset of 23 mandarins moving over a circular trajectory

SummaryThis dataset is a collection of X-ray projection images of 23 mandarins moving over a circular trajectory in such a way that the projections of multiple adjacent mandarins overlap. The dataset was acquired to test out Trajectory with Overlapping Projections x-ray Computed Tomography (TOP-CT), about which a paper is published in IEEE Transactions on Computational Imaging [Schut 2022]. DescriptionSample informationThe samples are 23 mandarins. The first 10 are of the Nadorcott cultivar, and the remaining 13 are of the Clemenrubi cultivar. The diameter of the mandarins ranges between 50 and 58 mm. Per sample metadata can be found in the mandarin_metadata.csv file.Scanner informationThe dataset is acquired in the FleX-ray Laboratory, developed by TESCAN-XRE, located at CWI in Amsterdam. The CT scanner consists 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].Scanning geometryThe mandarins were moved according to a custom scanning protocol, with the intention to simulate a conveyor belt setup. A wooden disk was attached on top of the rotation stage and six evenly spaced object positions were marked on the disk at a fixed distance from the center of rotation. Pieces of cardboard tube were used as sample holders to make sure the mandarins wouldn't roll as the disk would rotate and to raise them from the disk without attenuating too much of the X-ray signal. The rotation stage was positioned in such a way that over a full rotation of the disk, each mandarin would be completely in view of the detector for more than 180 degrees of the rotation, while there would also be a position at which it would be completely out of view. An image illustrating the exact dimensions is included in mandarin_carousel_dimensions.png.The scan was performed in phases. Every phase 400 projection images were acquired, while rotating the disk for 60 degrees. This would rotate one of the positions out of view of the scanning setup. Before the first 6 phases a mandarin was added on the position that was out of view of the setup. For the phases after that the position that would be out of view would contain a mandarin that had rotated the full circle so that mandarin was replaced with a new mandarin. At the last 6 phases there would be no new mandarins left to add so the mandarin that was out of view of the setup would only be removed. The projection images acquired from each phase were concatenated resulting in a dataset of 11200 projections. At most 5 mandarins were in view at a given time.Note: Due to a small oversight while scanning, the 19th mandarin is not included on projections 9200-9205. This area can be masked out during reconstruction.Scanning settingsA peak voltage of 90kV was used, the target power was set to 49.5W and the spectrum was pre-filtered using 0.1mm of copper. An exposure time of 200 ms was used for each projection. A start-stop acquisition scheme was used to minimize vibrations and to make adding and removing mandarins easier: After each projection image was acquired, the stage was rotated to a new position and the scanner was paused for 200 ms before acquiring the next projection image. Darkfield and flatfield images were acquired before and after all the mandarins were scanned using the average over 200 images. 2x2 pixel hardware binning was used and all images were cropped to a 500 pixel high region around the center, resulting in 11200 projection images of 956x500 pixels (11.1GB uncompressed). All images are stored in .tif format.Reconstructing volumesThe repository <a href="https://github.com/D1rk123/top-ct_experiments">https://github.com/D1rk123/top-ct_experiments</a> contains code for TOP-CT simulations and reconstructions. The script mandarin_carousel_experiment.py was specifically written to reconstruct volumes for each separate mandarin from this dataset. Research group These datasets are produced by the Computational Imaging group at Centrum Wiskunde & Informatica (CI-CWI) in Amsterdam, The Netherlands: <a href="https://www.cwi.nl/research/groups/computational-imaging">https://www.cwi.nl/research/groups/computational-imaging</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.References[Schut 2022] D. E. Schut, K. J. Batenburg, R. van Liere, and T. van Leeuwen, "TOP-CT: Trajectory with Overlapping Projections X-ray Computed Tomography", 2022, IEEE Transactions on Computational Imaging [Coban 2020] S. B. Coban, F. Lucka, W. J. Palenstijn, D. Van Loo, and K. J. Batenburg, "Explorative imaging and its implementation at the FleX-ray Laboratory," J. Imaging, vol. 6, no. 18, 2020, doi: 10.3390/jimaging6040018.If you use (parts of) this data in a publication, please consider citing the first article.

Raw Data of Mangoes; Computer Code

Exporting mangoes to foreign countries while maintaining quality is a challenge for distributors. The quality and maturity of mangoes are inhomogeneous, even when mangoes are harvested from the same tree at the same time. While quality affects the product value at the time of harvest, maturity affects the product value over time after harvest. The maturity of mangoes also greatly affects the storage and transport time. This data is collected in Vietnam with many mango cultivars and used to train machine learning models for the automatic classify mango system.

Computer Code; Raw Image_Mangoes

Supplemental material: Operative videos on application of microscope-based augmented reality with intraoperative computed tomography-based navigation for resection of skull base meningiomas

Supplemental material Operative videos: Patient number 9: Microsurgical resection of medial sphenoid wing meningioma using microscope-based augmented reality and intraoperative computed tomography-based navigation Pt 28:Microsurgical resection of right clinoidal meningioma via fronto-temporal craniotomy with microscope-based augmented reality Pt 31:Microsurgical resection of recurrent sphenoid wing meningioma using microscope-based augmented reality with intraoperative computed tomography Pt 36: Microsurgical resection of giant olfactory meningioma via bifrontal approach with use of augmented reality and intraoperative CT-based navigation

Computational Analysis of Two-dimensional High-throughput Data from Large-scale RNAi Screens and Single-cell Transcriptomics

This publication provides a singularity definition file to reproduce the computational environment along with the scripts to reproduce every figure or table in the revised manuscript using ZetaSuite Perl module and R package. First, generate a new folder and then download all the files into the folder. Then, uncompressed the files DataSets_part1.tar.gz,DataSets_part2.tar.gz,DataSets_part3.tar.gz,DataSets_part4.tar.gz, and scripts.tar.gz. within the folder. Next, move all the files in DataSets_part1 folder, DataSets_part2 folder,DataSets_part3 folder and DataSets_part4 folder to a new folder called DataSets. Finally, run the following scripts to generate the figures and tables in our manuscript. Regeneration of Figure2 and S2: singularity exec ZetaSuite.sif sh Figure2andS2.sh   Regeneration of Figure3 and S3: singularity exec ZetaSuite.sif sh Figure3andS3.sh   Regeneration of Figure4 and S4: singularity exec ZetaSuite.sif sh Figure4andS4.sh   Regeneration of Figure5 and S5: singularity exec ZetaSuite.sif sh Figure5andS5.sh   Regeneration of Figure6 and S6: singularity exec ZetaSuite.sif sh Figure6andS6.sh   Regeneration of Figure7 and S7: singularity exec ZetaSuite.sif sh Figure7andS7.sh

opencc-by-4.0May 2022View details →

Study-Data: Longitudinal Study about the relationship between computer mouse usage and emotional states

This Dataset contains the raw data files of the longitudinal study about the relationship between computer mouse usage and emotional states. The files are gzipped json files. There are 3 separate data files. <ol> <li>The self-directed mouse usage data recorded during a 5 minute interval of regular computer use (FreeMouse_dataset)</li> <li>The mouse usage data during the point-and-click task (MouseTask_dataset)</li> <li>The sociodemographics of the dataset (sociodem_dataset)</li> </ol> Note that the sociodemographic data is also included in the FreeMouse dataset as well as in the MouseTask dataset. For any questions about the dataset, contact: paul.freihaut@psychologie.uni-freiburg.de

opencc-by-4.0May 2022View details →

CaRCC Research Computing and Data (RCD) Workforce Survey Data 2021 - Part 1

Datasets and analysis to accompany "Characterizing the US Research Computing and Data (RCD) Workforce" Paper: Christina Maimone, Scott Yockel, Timothy Middelkoop, Ashley Stauffer, and Chris Reidy. 2022. Characterizing the US Research Computing and Data (RCD) Workforce. In Practice and Experience in Advanced Research Computing (PEARC ’22), July 10–14, 2022, Boston, MA, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3491418.3530289

opencc-by-4.0Jun 2020View details →

Shedding Light on Metal-Based Nanoparticles in Zebrafish by Computed Tomography with Micrometer Resolution

Supplementary 3D image stacks of microtomography data. 100 layer xy, xz, and yz image stacks Publication included as PDF file (open access, DOI: 10.1002/smll.202000746) ******************************************** Metal-based nanoparticles are clinically used for diagnostic and therapeutic applications. After parenteral administration, they will distribute throughout different organs. Quantification of their distribution within tissues in the 3D space, however, remains a challenge owing to the small particle diameter. In this study, synchrotron radiation-based hard X-ray tomography (SRμCT) in absorption and phase contrast modes is evaluated for the localization of superparamagnetic iron oxide nanoparticles (SPIONs) in soft tissues based on their electron density and X-ray attenuation. Biodistribution of SPIONs is studied using zebrafish embryos as a vertebrate screening model. This label-free approach gives rise to an isotropic, 3D, direct space visualization of the entire 2.5 mm-long animal with a spatial resolution of around 2 μm. High resolution image stacks are available on a dedicated internet page (http://zebrafish.pharma-te.ch). X-ray tomography is combined with physico-chemical characterization and cellular uptake studies to confirm the safety and effectiveness of protective SPION coatings. It is demonstrated that SRμCT provides unprecedented insights into the zebrafish embryo anatomy and tissue distribution of label-free metal oxide nanoparticles.

opencc-by-4.0Jun 2022View details →

Data: Computed tomography lacks sensitivity to image gold labelled mesenchymal stromal cells in vivo as evidenced by multispectral optoacoustic tomography.

This data set includes all the raw data collected for the following article: "Computed tomography lacks sensitivity to image gold labelled mesenchymal stromal cells in vivo as evidenced by multispectral optoacoustic tomography."

opencc-by-4.0Jun 2021View details →

Computing Book Parts with EEBO-TCP

Full frequency data for the div type attributes used in EEBO-TCP files to accompany the article 'Computing Book Parts with EEBO-TCP'.

opencc-by-4.0Jun 2022View details →

Machine Learning Models and New Computational Tool for the Discovery of Insect Repellents that Interfere with Olfaction

<ul> <li>SI1_Supporting Information file (docx) brings together detailed information on the outstanding models obtained for each dataset analyzed in this study such as statistical and training parameters and outliers. There can be found the responses in spikes/s of the mosquito Culex quinquefasciatus to the 50 IRs. Besides, there is presented a full table of the up-to-date studies related to QSAR and insect repellency.</li> <li>SI2_EXP1_50IRs from Liu et al (2013) SDF file presents the structures of each of the 50 IRs analyzed.</li> <li>SI3_EXP2_Datasets gathers the four datasets as SDF files from Oliferenko et al. (2013), Gaudin et al. (2008), Omolo et al. (2004), and Paluch et al. (2009) used for the repellency modeling in EXP2.</li> <li>SI4_EXP3_Prospective analysis provides Malaria Box Library (400 compounds) as an SDF file, which were analyzed in our virtual screening to prospect potential virtual hits.</li> <li>SI5_QuBiLS-MIDAS MDs lists contain three TXT lists of 3D molecular descriptors used in QuBiLS-MIDAS to describe the molecules used in the present study.</li> <li>SI6_EXP1_Sensillar Modeling comprises two subfolders: Classification and Regression models for each of the six sensilla. Models built to predict the physiological interaction experimentally obtained from Liu et al. (2013). All of the models are implemented in the software SiLiS-PAPACS. Every single folder compiles a DOCX file with the detailed description of the model, an XLSX file with the output obtained from the training in Weka 3.9.4, an ARFF, and CSV files with the MDs for each molecule, and the SDF of the study dataset.</li> <li>SI7_EXP2_Repellency Modeling encompasses the four datasets in the study: Oliferenko et al. (2013), Gaudin et al. (2008), Omolo et al. (2004), and Paluch et al. (2009). Inside the subfolders, there are three models per type of MDs (duplex, triple, generic, and mix) selected that best predict each dataset. As well as the SI6 folder, each model includes six files: DOCX, XLSX, ARFF, CSV, and an SDF.</li> <li>SI8_Virtual Hits includes the cluster analysis results and physico-chemical properties of new IR virtual leads.</li> </ul>

Data for manuscript "rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data"

Output files generated by rMATS-turbo for the two example datasets described in the manuscript titled "rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data". <table> <tbody> <tr> <td>File</td> <td>Description</td> <td>Cell lines</td> <td>BioProject</td> </tr> <tr> <td>PC3E-GS689.tar.gz</td> <td>Compressed folder containing all 36 rMATS-turbo output files for Example 1 described in the manuscript</td> <td>PC3E and GS689 cell lines</td> <td>PRJNA438990</td> </tr> <tr> <td>CCLE.tar.gz</td> <td>Compressed folder containing all 36 rMATS-turbo output files for Example 2 described in the manuscript</td> <td>1,019 CCLE human cancer cell lines</td> <td>PRJNA523380</td> </tr> </tbody> </table> A detailed description of the output files is available in the manuscript and the rMATS-turbo software GitHub repository (https://github.com/Xinglab/rmats-turbo).

opencc-by-4.0Oct 2021View details →

Application-Oriented Performance Benchmarks for Quantum Computing

Complete dataset and Jupyter Notebook used to produce image files for the paper at     https://arxiv.org/abs/2110.03137. To execute the notebook, copy the .ipynb file and the _data directory to the top level of the repository at:     https://github.com/SRI-International/QC-App-Oriented-Benchmarks

Cone-Beam Computed Tomography Dataset of a Chicken Bone Imaged at 4 Different Dose Levels

Summary This dataset is a collection of X-ray projection images of a chicken leg bone imaged in a cone-beam computed tomography (CBCT) scanner, using four different dose levels. The dataset also includes a metadata file for each of the scans, specifying the scan geometry and other important scan parameters.   Description Sample Information The sample is a chicken bone obtained from a cooked chicken. The bone was boiled to remove soft tissues, after which it was left to dry in room temperature for several months to remove extra moisture. For the scan the sample was placed directly into the rotation stage and secured with a screw. Scanner The measurements were acquired using a cone-beam computed tomography scanner designed and constructed in-house in the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki. The scanner consists of a molybdenum target X-ray tube (Oxford Instruments XTF5011), a motorized rotation stage (Thorlabs CR1-Z7), and a 12-bit, 2240x2368 pixel, energy-integrating flat panel detector (Hamatsu Photonics C7942CA-22). Scan Settings The dataset consists of four different scans of the same sample. For each scan 721 X-ray projections were acquired using an angle increment of 0.5 degrees. The X-ray source was set at 40 kV with a 0.5 mm aluminum filter. For the different scans, the relative doses, tube currents, and exposure times were: <ul> <li>100 % relative dose: tube current 1 mA, exposure time 2000 ms,</li> <li>50 % relative dose: tube current 1 mA, exposure time 1000 ms,</li> <li>25 % relative dose: tube current 0.5 mA, exposure time 1000 ms,</li> <li>10 % relative dose: tube current 0.2 mA, exposure time 1000 ms.</li> </ul> The scans were made in sequence, proceeding from the lowest dose to the highest dose. Data Post-Processing Before the scans, two correction images were acquired for each scan setting. A dark current image was created by averaging 100 images taken with the X-ray source off. A flat-field image was created by averaging 100 images taken with the X-ray source switched on with no sample placed in the scanner. After the scan, dark current and flat-field corrections were applied to each projection image using the Hamamatsu HiPic imaging software version 9.3. Data Format The X-ray projections are stored in .tif format. The metadata are contained in .txt files with formatting that is both human-readable and machine-readable. Notes Due to a slightly misaligned center of rotation in the scanner, the CT reconstructions can appear blurry. It was empirically observed that this problem can be compensated for quite well by shifting each projection left by 4 pixels, using circular boundary conditions, before performing any other operations on the projections. It was also observed that the scans are not entirely aligned, with a small angular discrepancy between each reconstruction.   Research Group This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland: <a href="https://www2.helsinki.fi/en/researchgroups/inverse-problems">https://www2.helsinki.fi/en/researchgroups/inverse-problems</a>.   Additional Links To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected in the Industrial Mathematics Computed Tomography Laboratory, and available at <a href="https://github.com/Diagonalizable/HelTomo">https://github.com/Diagonalizable/HelTomo</a>.   Contact Details For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.

Student answers to computational thinking tasks based on Riau Malay culture

The data is the result from the test of computational thinking tasks based on Riau Malay culture to elementary school students in a public school in Pekanbaru

Code and data for "Computational morphology of debris and alluvial fans on irregular terrain using the visibility polygon"

This code is for simulating conical fan morphologies.  The code and the dataset can be read/run by using Matlab. The description is as follows: 1. Dataset (FieldCase1Input.mat, FieldCase2Input.mat) provides the input data needed to simulate the two field cases. The simulations can be run using the code Case4_FieldCase1.m and Case5_FieldCase2.m. The results can be compared with the provided output data (FieldCase1Output.mat, FieldCase2Output.mat). 2. Code for three idealized cases, including the comparison of analytical solutions and model simulations are provided. 3. Function VisiPolygon is the algorithm for calculating point visibility polygon. Function FanTopo_slope is the model for simulating fan morphology.