Find research datasets worth reusing

opencc-by-4.0Jul 2024View details →

Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software

<pre>Dynamic sparse X-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst: Raw data and reconstruction software Dataset structure: - Dynamic_PEFC_data.h5 # Raw projection data for dynamic tomography imaging of PEFC catalyst hydration. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /humidity_readout # Relative humidity value at the time each projection is measured, 1D array with axis (Nangle). - /Deform_X # X/Y/Z components for the deformation vector field which characterize nonrigid deformation of the sample. - /Deform_Y - /Deform_Z - liquid_simulation.h5 # Numerical simulation of dynamic liquid filling process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). - phasetran_simulation.h5 # Numerical simulation of gradual linear density change process. - /sinogram # Sinogram of all projections, 3-dimensional array with axes (Nangle,X axis,Y axis). - /tomo_angle # Tomography rotation angle for each projection, 1D array with axis (Nangle). - /groundtruth_tomograms # Ground truth of the simulated tomograms, 4-dimensional array with axes (Timeframe,Y axis, Z axis, X axis). Reconstruction codes: - astra_nonrigid.zip # Compressed python package of modified version of astra-toolbox with nonrigid computed tomography implementation. - /astra # Python package folder, need to be added to Python import search path (sys.path). # If the pre-compiled version doesn't work, source code of the pacakge can be downloaded: # https://github.com/zr-gao/astra-toolbox-nonrigid # Follow the instructions and requirements on the website to compile and install the package. - reconstruction_PEFC.py # Python script for sparse dynamic tomography of the PEFC dataset. # Need to be in the same folder with Dynamic_PEFC_data.h5 to load data. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra(with nonrigid)*, h5py # * !!! Nonrigid computed tomography is used for the reconstruction, therefore the astra package with nonrigid implementation (in astra_nonrigid.zip) is required. - reconstruction_simulation.py # Python script for sparse dynamic tomography of numerical simulations. # Need to be in the same folder with liquid_simulation.h5 or phasetran_simulation.h5, loaded filename is selected in the code. # Follow the instructions in the code to set reconstruction parameters and export results. # Requirements: cupy, numpy, astra**, h5py # ** Reconstruction of numerical simulations does not use nonrigid computed tomography, therefore both the astra_nonrigid.zip and the official astra-toolbox package will work. # To download and install the official astra-toolbox refer to the repository: # https://github.com/astra-toolbox/astra-toolbox</pre>

opencc-by-4.0Dec 2017View details →

Raw data for "Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task"

Raw data for the simulation study " Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task" [1]. [1] Josupeit, A., Schoenmaker, E., van de Par, S., & Hohmann, V. (2018). Sparse periodicity‐based auditory features explain human performance in a spatial multitalker auditory scene analysis task. European Journal of Neuroscience, https://doi.org/10.1111/ejn.13981.

opencc-by-4.0Oct 2024View details →

Sparse camera volumetric video applications. A comparison of visual fidelity, user experience, and adaptability: Subjective fidelity study data

Supplementary Material to the Paper: Sparse camera volumetric video applications. A comparison of visual fidelity, user experience, and adaptability This folder contains all collected data and scripts that were used to analyze the subjective fidelity study.

opencc-by-4.0Feb 2023View details →

Data for threshold plot in "Finite-rate sparse quantum codes aplenty"

This dataset contains the quantum error correcting codes that were sampled to render the threshold plot under the erasure noise channel in the article. They are sorted by batch corresponding to different jobs on the computing cluster. Each batch contains many codes sorted by length. There are also three extra metadata files. The inputs that were given to sample codes (inputs.json), the CSP solver output (output.csv) and the result of the decoding simulation (decoding.json). Each file named code.json contains the sparse list of X and Z stabilizers. That is, each stabilizer is given by a list of integers corresponding to the index of the qubits supporting the stabilizer.

opencc-by-3.0-usSep 2023View details →

Data for 2D Signal Estimation for Sparse Distributed Target Photon Counting Data

Data used in publication of 2D Signal Estimation for Sparse Distributed Target Photon Counting Data Data provided consists of: raw and PTV processed MicroPulse DIAL (MPD) data (10.26023/MX0D-Z722-M406) simulated photon counting data and the processed results

edi44/100

Data for: Sparse subalpine forest recovery pathways, plant communities, and carbon stocks 34 years after stand-replacing fire (Greater Yellowstone Ecosystem, Wyoming, USA; 2022)

We assessed postfire forest recovery pathways, stem densities, understory plant communities, and carbon stocks across 55 plots in areas exhibiting sparse and reduced forest recovery 34 years after the 1988 Yellowstone Fires in the Greater Yellowstone Ecosystem, Wyoming, USA. Recovery pathways were identified using plot-level frequency distributions of tree ages and correlated with potentially important biotic and abiotic variables (e.g., elevation, seed source distance). Species- and age-specific stem densities were similarly regressed across environmental factors to determine variability in forest recovery across the sampled landscape. Understory plant communities were sampled in 0.25m-square quadrats and environmental drivers of individual species occurrence and whole compositional shifts were determined. Finally, carbon stock sizes were derived from field measures of tree characteristics, understory cover, and soil combined with regionally derived allometric equations. Data collection is complete and is part of a forthcoming manuscript at Ecological Monographs.

openCC (other)Sep 2024View details →

opencc-by-4.0Oct 2019View details →

A Tool for Uncertainty Quantification in Reconstructing Sparse Water Quality Time Series Data to Assess Risk Metrics for Watershed Health and TMDL Analysis

The uploaded file contains the input and output data which can be used to reproduce the results in the research article 'Uncertainty Quantification in Reconstruction of Sparse Water Quality Time Series: Implications for Watershed Health and Risk-Based TMDL Assessment'. Please refer to the file '<a href="https://zenodo.org/api/files/31b59cce-8eb2-4ee7-93aa-61474c6f6359/dst_2019_SJRW_TP_TDS.zip?versionId=2af2b54d-d5fb-4720-919d-de2e827595e2">dst_2019_SJRW_TP_TDS.zip'</a> for updated files..

opencc-by-4.0Nov 2024View details →

quickSparseM: a library for memory- and time-efficient computation on large, sparse matrices with application to omics data

This page contains the code and datasets used in "quickSparseM: a library for memory- and time-efficient computation on large, sparse matrices with application to omics data". File test_datasets.zip containes three datasets: <ul> <li>D1.RData: scRNA-seq omics data derived from Salcher et. al (2022)</li> <li>D2.RData: scRNA-seq omics data derived from Pineda et al. (2024)</li> <li>D3.RData: in silico WGS SNP data.</li> </ul> File test_scripts.zip containes the code to reproduce the results.

dryad40/100

spectre: An R package to estimate spatially-explicit community composition using sparse data

An understanding of how biodiversity is distributed across space is key to much of ecology and conservation. Many predictive modelling approaches have been developed to estimate the distribution of biodiversity over various spatial scales. Community modelling techniques may offer many benefits over single-species modelling. However, techniques capable of estimating precise species makeups of communities are highly data intensive and thus often limited in their applicability. Here we present an R package, spectre, which can predict regional community composition at a fine spatial resolution using only sparsely sampled biological data. The package can predict the presence and absence of all species in an area, both known and unknown, at the sample site scale. Underlying the spectre package is a min-conflicts optimisation algorithm that predicts species' presences and absences throughout an area using estimates of α-, β-, and γ-diversity. We demonstrate the utility of the spectre package using a spatially-explicit simulated ecosystem to assess the accuracy of the package's results. spectre offers a simple-to-use tool with which to accurately predict community compositions across varying scales, facilitating further research and knowledge acquisition into this fundamental aspect of ecology.

opencc-zeroOct 2022View details →

opencc-by-4.0Mar 2024View details →

Data associated with Versatile Multiple Object Tracking in Sparse 2D/3D Videos via Deformable Image Registration (2024)

This includes a volumetric whole-brain calcium recording of a freely behaving worm (C. elegans) captured at 4 Hz with tracked fluorescent neuronal nuclei, used to demonstrate the performance of a multi-object tracking algorithm (ZephIR) described in the associated publication.

opencc-by-4.0Jun 2024View details →

Supplement to "Discriminating non-stationary flood hazard effects via probabilistic estimation of sparse residuals from rescued and rated stage–discharge data"

This supplement contains the data and script associated with “Sparse hydrometric data rescue for exploratory analyses of conveyance-driven flood hazard trends and controls,” which has been submitted to a journal for consideration. This supplement is deposited on Zenodo.   Code, Data, and Attribute Descriptions   Uploaded are five directories (indicated in bold font, with their contents detailed below) containing input data and various outputs of the analyses performed for our case study on the Pulangi River at Lumayong (Philippines). Please consider this description equivalent to an omnibus README file for the deposited files. Note that we collected and rescued the hydrometric data herein from the archives of the Water Projects Division (WPD) of the Philippine Department of Public Works and Highways (DPWH).   ●      Pln_R_code contains (1) Pln_RC.R, the R script file, and (2) 240601_Pln_RC.RData, which stores objects generated from the script based on the last execution (on 1 June 2024). The script consists of admittedly too many lines of code (e.g., for data wrangling, formal analyses, and producing figures used in the manuscript) that should have been split into multiple .R files. Apologies. Please be guided by the outline and the comments and kindly reach out lest issues with the code arise.   ●      Pln_GH_PDFs contains the scanned gaugekeeper’s reports of gauge heights (“stages”).    ○      The name of each file varies. For example, Pln-GH-1100.pdf contains daily stage readings for all months in the year 2011. But, if the last two digits are not zeroes, as in Pln-GH-1204.pdf, they refer to the month of that year; in this case, for example, the PDF file contains stage data for the month of April in the year 2012. ○      Each scanned sheet contains sub-daily (with readings at “AM,” “NOON,” and “PM”) and mean daily stages for a given month. ○      Also indicated are the gaugekeeper’s remarks on the daily weather (e.g., “fair,” “cloudy”). Under inclement weather, the gaugekeeper would note the duration of rainfall and its intensity and might, at times, record extra stage readings.   ●      Pln_DM_PDFs contains the following PDF files and a sub-directory: ○      Pln-DM-<XXXXXX>.pdf contains the scanned log for each direct stage—discharge measurement, otherwise known as “gaugings.” Each file is named according to the date of gauging (YYMMDD). For example, the log for the gauging performed on 24 February 2011 can be found in the file Pln-DM-110224.pdf. Data from these gaugings are summarized in Pln-DM-filtered.csv in the Pln_In_CSVs directory. ■      The readability of each file varies based on the original quality of the original paper-format data. ■      The first page in each file contains, on the left side, a summary of the gauging data and metadata (e.g., date of measurement, number of gauging verticals or “sections” used, method of crossing or measuring the cross-section), and on the right side, the velocity—area readings at each gauging vertical. ■      The second page in each file contains the plotted cross-section of the channel at the time of measurement. ○      Pln-RC.pdf contains various other paper-format data and some annotations relevant to the historical rating at the station. ■      P. 1: The hydrographic engineer’s comment (dated 19 January 2012) on the evaluation of the discharge data, specifically detailing the periods of validity of the rating curves developed for different sub-periods of monitoring. ■      PP. 2—3: A summary table of all the gaugings performed from 1983 to 2010 whose logs were no longer retrievable (and hence not included as a DM-PDF file in this directory). ■      PP. 4—5: Plots of the official stage—discharge rating curves developed by DPWH hydrographers. ■      PP. 6—8: Rating tables used to convert daily mean stages to deterministic discharge estimates. Two of these rating tables were digitized and can be found in the Pln_RatingTables sub-directory in Pln_In_CSVs. ■      PP. 9—22: Daily stage (m) and its corresponding daily discharge (L/s) for the 2004—2010 sub-period. The stages were digitized and included in Pln-H_arch.csv in the Pln_In_CSVs directory. ○      Pln_DM_unused contains the PDF files of logs (including data and metadata) corresponding to the gaugings that were excluded from our analysis following our filtering step for gauging location consistency.   ●      Pln_In_CSVs contains the following CSV files and two sub-directories; these files were used as inputs to the R script for formal analyses: ○      Pln-H_arch.csv contains the mean daily stage values [“H_bar_arch”] (m) for every day in the 2004—2020 sub-period [“Date”] (YYYY-MM-DD). The stages in this file are already corrected for gross errors. ○      Pln-DM-filtered.csv contains the following information on the gaugings performed on the Pulangi at Lumayong (1983—2020): (i) date [“Date”] (YYYY-MM-DD); (ii) stage [“H”] (m); (iii) discharge in L/s [“Q_lps”] and m3/s [“Q_cms”]; (iv) wetted area [“A_sqm”] (m2); (v) mean flow velocity [“Vel_mps”] (m/s); (vi) channel width [“W_m”] (m); (vii) mean flow depth [“D_ave_m”] (m); (viii) location of the measurement cross-section with respect to the staff gauge, with negative values meaning downstream of the gauge and positive values meaning upstream of the gauge [“XS_loc_wrt_gage”]; (ix) maximum flow depth [“Max_Depth_m”] (m); and (x) minimum streambed elevation [“MINSBE”] (m). Note that this file includes only gaugings that passed our filtering step for measurement location consistency. ○      Pln-DM_oview.csv contains information on the temporal coverage (bounded by “Start_date” and “End_date”) of the available hydrometric data from the station archives. ○      Pln-H_sample_GrossE_corr.csv contains a sample sub-period (2017-06-15 through 2017-11-30) and the corresponding values of stages (m), uncorrected [“H_uncorrected”] and corrected for gross errors [“H_corrected”]. This CSV file was used as an input to the R script to produce one of the figures in the manuscript.  ○      Pln-XS-csv.csv contains data on the gauging transects: gauging ID [“GaugingID”]; date [“Date”] (YYYY-MM-DD); lateral distance from a fixed initial point [“Lat_distance”] (m); width of the gauging vertical [“Width_vert..m.”] (m); depth relative to the water surface [“Depth..m.”] (m); stage at the gauging vertical [“H_m_vert..m.”] (m); and elevation with respect to a fixed arbitrary datum at the station [“Elev..m.”] (m). ○      Pln_Rating_Tables contains two rating tables, Pln-DM - RatingTable_A.csv and Pln-DM - RatingTable_B.csv, prepared and used by DPWH hydrographers for converting stage values to deterministic discharge estimates for the Pulangi River at Lumayong for the 1980s—early 2000s sub-period. Each rating table contains columns for stage [“H”], discharge in L/s [“Q_lps”], and discharge in m3/s [“Q_cms”]. ○      Pln_Q_rated contains two CSV files: Pulangi.csv has the columns “YEAR”, “DAY”, and every month of the year [“JAN” through “DEC”] for the 1983—2003 sub-period, with the values under each month column indicating the deterministic discharge estimates in L/s; Pulangi_trunc.csv contains similarly formatted data, but for the 2009—2010 sub-period.   ●      Pln_Out_CSVs contains two CSV files, the primary outputs of the hydrometric data rescue effort. ○      Pln_Q_recon.csv contains the reconstructed discharge time series (1983—2020) and its associated uncertainties at the 95% credibility interval. It has the following columns: date [“Date”] (YYYY-MM-DD); daily stage [“H”] (m); lower bound of the discharge estimate [“Q_lwr”] (m3/s); median discharge estimate [“Q_med”] (m3/s); and the upper bound of the discharge estimate [“Q_upr”] (m3/s). Pln_H_recon.csv contains the reconstructed and quality-controlled stage time series (1983—2020). It has the following columns: date [“Date”] (YYYY-MM-DD); mean daily stage that has been corrected for gross errors, but not yet filtered through other quality checks [“H_bar_arch”] (m); quality check for low outliers [“Low_Outlier”] (TRUE/FALSE); quality check for flatliners [“Flatliner”] (TRUE/FALSE); difference between the stage values on day i and day i-1 [“Daily_Step”] (m); quality check for large steps [“Large_Step”] (TRUE/FALSE); and the corrected and quality-controlled mean daily stage values [“H”] (m).

dryad40/100

spectre: An R package to estimate spatially-explicit community composition using sparse data

Open the record for dataset details and reuse information.

publicOct 2022View details →

dryad40/100

Flongle data for KnowYourCG: Facilitating base-level sparse methylome interpretation

Open the record for dataset details and reuse information.

publicSep 2025View details →

opencc-by-4.0May 2020View details →

Sparse Vibrometer Data for Inverse Imaging

Experimental sparse vibrometer data for validating inversion methods. Instructions: <ul> <li>Extract the data zip file (for object 1 or 2) and copy the matlab scripts to a folder in the computer's local file system.</li> <li>Read the documentation to learn how to use the scripts.</li> </ul>

opengpl-3.0-or-laterOct 2023View details →

Supplementary Data for "Comparison of Ensemble-Based Data Assimilation Methods for Sparse Oceanographic Data"

This data set represents the supplementary data for the paper Comparison of Ensemble-Based Data Assimilation Methods for Sparse Oceanographic Data (Section 4) by Florian Beiser, Håvard Heitlo Holm, and Jo Eidsvik.It contains the data that is plotted in the manuscript. The code for plotting is provided in the supplementary software.

opencc-by-4.0Dec 2023View details →

Evaluation of flood hazards in data-sparse coastal lowlands: highlighting the Ayeyarwady Delta (Myanmar)

This folder includes datasets that were produced to assess flood hazards and exposure in the Ayeyarwady Delta in Myanmar by applying the new standardised, integrative approach of Seeger, K., Peffeköver, A., Minderhoud, P. S. J., Vogel, A., Brückner, H., Kraas, F., Nay Win Oo, Brill, D. (2024): Evaluating flood hazards in data-sparse coastal lowlands: highlighting the Ayeyarwady Delta (Myanmar). Environmental Research Letters. The README includes the names of files to be used for citation as well as a brief explanation when necessary. All processing details are given in the paper and related supplementary material.

opencc-by-4.0Sep 2019View details →

Simulated and experimental data distributed to the CASP13 participants in protein structure prediction assisted with sparse NMR data

All simulated and experimental data distributed to the CASP participants in protein structure prediction assisted with sparse NMR data in CASP13. Also available at http://predictioncenter.org/casp13/results.cgi?view=targets&model=first&tr_type=others&sub_type=N&groups_id=

openodc-byMar 2022View details →

Data from: Data-driven analysis of oscillations in Hall thruster simulations & Data-driven sparse modeling of oscillations in plasma space propulsion

Data from: Data-driven analysis of oscillations in Hall thruster simulations   - Authors: Davide Maddaloni, Adrián Domínguez Vázquez, Filippo Terragni, Mario Merino - Contact email: <a href="mailto:dmaddalo@ing.uc3m.es">dmaddalo@ing.uc3m.es</a> - Date: 2022-03-24 - Keywords: higher order dynamic mode decomposition, hall effect thruster, breathing mode, ion transit time, data-driven analysis - Version: 1.0.4 - Digital Object Identifier (DOI): <a href="https://doi.org/10.5281/zenodo.6359505">10.5281/zenodo.6359505</a> - License: This dataset is made available under the <a href="http://opendatacommons.org/licenses/by/1.0/">Open Data Commons Attribution License</a>   Abstract   This dataset contains the outputs of the HODMD algorithm and the original simulations used in the journal publication: Davide Maddaloni, Adrián Domínguez Vázquez, Filippo Terragni, Mario Merino, "Data-driven analysis of oscillations in Hall thruster simulations", 2022 Plasma Sources Sci. Technol. 31:045026. Doi: <a href="https://iopscience.iop.org/article/10.1088/1361-6595/ac6444">10.1088/1361-6595/ac6444</a>. Additionally, the raw simulation data is also employed in the following journal publication: Borja Bayón-Buján and Mario Merino, "Data-driven sparse modeling of oscillations in plasma space propulsion", 2024 Mach. Learn.: Sci. Technol. 5:035057. Doi:<a href="https://iopscience.iop.org/article/10.1088/2632-2153/ad6d29"> 10.1088/2632-2153/ad6d29</a>   Dataset description   The simulations from which data stems have been produced using the full 2D hybrid PIC/fluid code <a href="https://ep2.uc3m.es/assets/docs/pubs/conference_proceedings/domi19b.pdf">HYPHEN</a>, while the HODMD results have been produced using an adaptation of the original <a href="https://doi.org/10.1137/15M1054924">HODMD algorithm</a> with an improved <a href="https://doi.org/10.1063/1.4863670">amplitude calculation routine</a>. Please refer to the relative article for further details regarding any of the parameters and/or configurations.   Data files   The data files are in standard Matlab .mat format. A recent version of <a href="https://www.mathworks.com/products/matlab.html">Matlab</a> is recommended. The HODMD outputs are collected within 18 different files, subdivided into three groups, each one referring to a different case. For the file names, "case1" refers to the nominal case, "case2" refers to the low voltage case and "case3" refers to the high mass flow rate case. Following, the variables are referred as: <ul> <li>"n" for plasma density</li> <li>"Te" for electron temperature</li> <li>"phi" for plasma potential</li> <li>"ji" for ion current density (both single and double charged ones)</li> <li>"nn" for neutral density</li> <li>"Ez" for axial electric field</li> <li>"Si" for ionization production term</li> <li>"vi1" for single charged ions axial velocity</li> </ul> In particular, axial electric field, ionization production term and single charged ions axial velocity are available only for the first case. Such files have a cell structure: the first row contains the frequencies (in Hz), the second row contains the normalized modes (alongside their complex conjugates), the third row collects the growth rates (in 1/s) while the amplitudes (dimensionalized) are collected within the last row. Additionally, the time vector is simply given as "t", common to all cases and all variables. The raw simulation data are collected within additional 15 variables, following the same nomenclature as above, with the addition of the suffix "_raw" to differentiate them from the HODMD outputs.   Citation   Works using this dataset or any part of it in any form shall cite it as follows. The preferred means of citation is to reference the publication associated to this dataset, as soon as it is available. Optionally, the dataset may be cited directly by referencing the DOI: 10.5281/zenodo.6359505.   Acknowledgments   This work has been supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of ‘Fostering Young Doctors Research’ (MARETERRA-CM-UC3M), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). F. Terragni was also supported by the Fondo Europeo de Desarrollo Regional, Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación, under grants MTM2017-84446-C2-2-R and PID2020-112796RB-C22.

opencc-by-4.0Sep 2015View details →

Optimizing Sparse Matrix-Matrix Multiplication for the GPU supplementary data

This contains the matrices for the SpGEMM tests presented in "Optimizing Sparse Matrix-Matrix Multiplication for the GPU", by Steven Dalton, Nathan Bell, and Luke N. Olson. Each A matrix from Table 3 has an companion matrix P in the directory. The storage scheme appends "_P" to the end of the A matrix filename.