Find research datasets worth reusing

Suplementary material: Towards uncoding hepatotoxicity of approved drugs through navigation of multiverse and consensus chemical spaces

Supplementary material: "Towards uncoding hepatotoxicity of approved drugs through navigation of multiverse and consensus chemical spaces"

opencc-by-4.0Jan 2023View details →

Spectral data associated to the publication: "Near-infrared reflectance spectroscopy of sublimating salty ice analogues. Implications for icy moons" by R. Cerubini et al. (Planetary and Space Science 211, 2022)

This is the complete set of experimental NIR reflectance data collected by R. Cerubini and co-authors for the article "Near-infrared reflectance spectroscopy of sublimating salty ice analogues. Implications for icy moons" published in Planetary and Space Science 211 (2022). doi: https://doi.org/10.1016/j.pss.2021.105391. The article itself is published in open-access and provides the methodology for the spectral aquisitions, discussion of the errors and uncertainties, analysis of the spectra and implications for the composition of Solar System surfaces. The data are contained in ASCII files (columns separated by comma). The first column is the wavelength (in micrometers) and the other columns contain the reflectance data (in unit of reflectance factor). The different compositions are indicated in the filenames and correspond directly to the figures in the published paper.

opencc-by-4.0Feb 2023View details →

Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces (Figures)

High resolution figures related to the below manuscript: Atul Deshpande, Melanie Loth, et al., <a href="https://doi.org/10.1101/2022.06.02.490672">Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces</a>. bioRxiv 2022. doi:10.1101/2022.06.02.490672

X-ray tomographic datasets associated with the article "Pore space of in-situ semi-dense asphalt: A characterization by X-ray tomography" (DOI: 10.1016/j.conbuildmat.2024.139091)

This Zenodo repository provides two sets of 3D images, which constitute part of the dataset base for the article titled "Pore space of in-situ semi-dense asphalt: A characterization by X-ray tomography", written by the same authors cited here, together with other co-authors. The article is published in the journal "Construction and Building Materials". It can be reached via the following URL: <a href="https://doi.org/10.1016/j.conbuildmat.2024.139091" target="_blank" rel="noopener">https://doi.org/10.1016/j.conbuildmat.2024.139091</a>. The core specimens were obtained in 2019 from semi-dense asphalt (SDA) pavement sections located in the Swiss Canton of Zürich. For each of three pavement sections, labelled in the following as SDA4-1yr, SD4-5yr and SDA8, 100 mm diameter cores were extracted, both inside (I) and outside (O) of the wheel path, in order to see the effect of the traffic load on the pore space characteristics. Out of the original cores for the SDA4 pavements, 5 30 mm diameter sub-cores were drilled out of their centers, both in- and out-of the wheel path, and investigated with X-ray tomography. Only 1 30 mm core was analyzed for SDA8, both in- and out- of the wheel path. The asphalt in that pavement type has lower porosity, making it less interesting from the sound absorption viewpoint. The whole dataset consists of .7z archive files. Such files have the following designations: SDA_J_K_L_Tomogram.7z or SDA_J_K_L_PoreSpaceBinTomogram.7z, where J = 1,2, K = I,O and L = 1,2,3,4,5. When referring to the specimen naming within the corresponding article, the first index, J, refers to the specimen "age": J = 1 indicates the 1-year old specimens (called SDA4-1yr within the article); J = 2 refers to the 5-year old ones (SDA4-5yr). The second index, K, refers to the location of the specimen within the pavement section course ("I" for in-wheel path and "O" for out-of-wheel path). The final index L just enumerates the distinct specimens of the same group. There are two additional groups of archive files: LNA_I_Tomogram.7z/LNA_I_PoreSpaceBinTomogram.7z refers to the single in-wheel-path, 7-year old specimen (called SDA8 within the article); LNA_O_Tomogram.7z/LNA_O_PoreSpaceBinTomogram.7z refers to the single out-of-wheel path, 7-year old specimen. The two sets/types of 3D images can be recognized by the different file naming. The first set includes the raw X-ray tomograms of the 22 specimens analyzed. Each tomogram is stored in the form of a "stack" (or series) of 16-bit unsigned integer 2D TIFF image file, being one 2D cross-section (also called "slice", in tomographic jargon) from the "tomographed" volume. Such slices are contained in a folder. The folder was then archived in a .7z archive file. The second set of 3D images is characterized by the filename pattern SDA_J_K_L_PoreSpaceBinTomogram.7z. Each zipped folder contains the slices of the binary tomogram of the whole pore space of the respective specimen, segmented according with the 3d image analysis workflow described within the article. Each slice of such tomogram was stored as a 8-bit unsigned integer 2D TIFF image file, whose pixels can have only two possible values: 255, if the pixel is inside the segmented pore space; 0 if the pixel is outside it. Almost all of the acquired tomograms have an isotropic voxel size of 0.0214 mm, meaning that each slice is separated in space from the next one by such distance. The samples SDA_2_O_1 and SDA_2_I_1 have a voxel size of 0.0220 mm, while the sample LNA_I has a voxel size of 0.0223 mm.

Dataset of Machine Learning forecasted VTEC from paper: Uncertainty Quantification for Machine Learning-based Ionosphere and Space Weather Forecasting

The *csv files contain forecasted one-day-ahead Vertical Total Electron Content (VTEC), consisting of the mean/median VTEC values and the upper and lower VTEC bounds of the 95% confidence intervals of 4 models based on machine learning for test data. The first part of the *csv file name corresponds to the type of model: SE stands for the super-ensemble VTEC model, QGB stands for the quantile gradient boosting VTEC model, BNN1 stands for the Bayesian neural network VTEC model, and BNN2 stands for the Bayesian neural network with negative log-likelihood (NLL) loss VTEC model. The second part of the file name refers to the geographic location of the VTEC points for which the forecast is performed, i.e., 10E70N for 10 degree of longitude and 70 degree of latitude, 10E40N for 10 degree of longitude and 40 degree of latitude, and 10E10N for 10 degree of longitude and 10 degree of latitude. The last part of the file name corresponds to the test year, i.e., year 2017. The SE_*_2017.csv file consists of 14 columns. The index column ("Date-time") is expressed in Coordinated Universal Time (UTC) as YYYY-MM-DD. Columns 1-3 contain the VTEC forecast results of Random Forest (RF) trained on three data subsets; columns 4-6 contain the VTEC forecast results of Adaptive Boosting (AB) trained on three data subsets; columns 7-9 contain the VTEC forecast results  of Gradient Boosting (XGBoost) trained on three data subsets. Column 10 ("Mean") represents the mean of columns 1-9, i.e., the ensemble mean; column 11 ("Std") represents the standard deviation of columns 1-9, i.e., the ensemble spread; columns 12 ("UB") and 13 ("LB") contain the upper and lower bounds of the 95% confidence interval of VTEC, respectively; and column 14 contains the Global Ionosphere Maps (GIM) values of CODE, i.e., the ground-truth in this study. The QGB_*_2017.csv file consists of 4 columns. The index column ("Date-time") is expressed in UTC as YYYY-MM-DD. Column 1 ("Median") contains the median VTEC forecast, column 2 ("LB") contains the lower VTEC bound of the 95% confidence interval, column 3 ("UB") contains the upper VTEC bound of the 95% confidence interval, and column 4 contains the GIM values of CODE, i.e., the ground-truth in this study. The BNN*_2017.csv file consists of 5 columns. The index column ("Date-time") is expressed in UTC as YYYY-MM-DD. Column 1 ("Mean") contains the mean VTEC forecast, column 2 ("Std") contains the standard deviation, column 3 contains GIM values of CODE, i.e., ground-truth in this study; column 4 ("UB") contains the upper VTEC bound of the 95% confidence interval, and column 5 ("LB") contains the lower VTEC bound of the 95% confidence interval. ---------------------------------------------------------------------------------------------------------------------------------------- Contact ---------------------------------------------------------------------------------------------------------------------------------------- If you have any questions regarding these data, please contact: Randa Natras Deutsches Geodätisches Forschungsinstitut (DGFI-TUM) Technical University of Munich Arcisstraße 21 80333 München randa.natras@tum.de

Hubble and Webb Space Telescope data for the cluster field RXJ-2129

This repository contains reduced image mosaics from the Hubble and Webb Space Telescopes of the galaxy cluster RXJ-2129. Data were processed with the <a href="https://github.com/gbrammer/grizli">grizli</a> software pipeline, as described by <a href="https://arxiv.org/abs/2302.10936">Valentino et al. (2023, ApJ, in press)</a>, with the software and observatory calibrations available as of October 2022. For a given broad-band filter, the science data are given in the "sci.fits" files.  Inverse variance weight images are provided in the "wht.fits" files.   Aperture photometry following the methodology described by Valentino et al. is provided in "rxj2129-grizli-v4.0-fix_phot_apcorr.fits", and photometric redshift estimates derived with the <a href="https://github.com/gbrammer/eazy-py/">eazy-py </a>software are tabulated in "rxj2129-grizli-v4.0-fix.zout.fits".

opencc-by-4.0Jan 2023View details →

Green Space Distribution m2 per capita in Valladolid city

Urban green infrastructures are key part of the sustainable development in our cities. They can provide important Ecosystem Services in them, including provisioning, regulating, supporting and cultural services. The total surface of green areas needs to be relativized in terms of total area or per capita, in order to compare results with other cities or to observe the evolution within the same city.

opencc-by-4.0May 2023View details →

Copernicus High Resolution Vegetation Phenology and Productivity for Doñana Natural Space

GeoTIFF rasters with the following phenometrics obtained from Sentinel 2 Data:.   * Start of the season Day of the Year (SOSD) * Maximun of the Season Day of the Year (MAXD) * End of the Season Day of the Year (EOSD) * Start of the season Value (SOSV) * Maximun of the Season Value (MAXV) * End of the Season Value (EOSV)   These rasters have been downloaded, mosaicked and croped with Doñana Natural Space DEIMS.ID through Pyvpp python package.

opencc-by-4.0Jan 2023View details →

Supplementary data for the journal article "Quantifying the impact of 3D pore space morphology on diffusive mass transport in loam and sand"

Binarized cutouts (black=pore, white=soil) of 3D CT images of soil samples from loam and sand together with geometrical descriptors and diffusive properties computed on these cutouts. The geometrical descriptors include, among others, porosity, specific surface area, geodesic tortuosity, geometric tortuosity, constrictivity, mean chord length and mean of spherical contact distribution. Diffusion is quantified by the so-called M-factor which equals the ratio of the effective and intrinsic diffusivity. This data supplements the journal article "Quantifying the impact of 3D pore space morphology on diffusive mass transport in loam and sand". Additional information can be found there.

opencc-by-4.0Jun 2023View details →

Perceptual space of tire noises

Data of a free sorting experiment of vehicle passing-by noises (70 km/h). Experiment conducted in the framework of Leon-T project, WP4 (see https://www.leont-project.eu/).

opencc-by-4.0Nov 2022View details →

Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space

The products are the first regional-scale and high-resolution (1 and 6 km) irrigation water data sets obtained from remote sensing observations. They cover three major river basins: the Ebro river basin (North-eastern Spain), the Po valley (Northern Italy), and the Murray-Darling basin (South-eastern Australia). The data sets are an outcome of the European Space Agency (ESA) Irrigation+ project (<a href="https://esairrigationplus.org/">https://esairrigationplus.org/</a>). The irrigation amounts have been estimated through the SM-based (Soil-Moisture-based) inversion approach. The satellite-derived irrigation products referring to the European sites have a spatial resolution of 1 km, and they are retrieved by exploiting Sentinel-1 soil moisture data obtained through the RT1 (first-order Radiative Transfer) model. A spatial sampling of 6 km is instead used for the Australian pilot area, since in this case the soil moisture information comes from CYGNSS (Cyclone Global Navigation Satellite System) observations. The three irrigation products are delivered with a weekly temporal aggregation. The 1 km data sets over the two European regions cover a period ranging from January 2016 to July 2020, while the irrigation estimates over the Murray-Darling basin are available for the time span April 2017 – July 2020. Details on the data sets development and  on their performance assessment can be found in: Dari, J., Brocca, L., Modanesi, S., Massari, C., Tarpanelli, A., Barbetta, S., Quast, R., Vreugdenhil, M., Freeman, V., Barella-Ortiz, A., Quintana-Seguí, P., Bretreger, D., Volden, E. Regional data sets of high-resolution (1 and 6 km) irrigation estimates from space. Earth System Science Data, 15, 1555–1575, https://doi.org/10.5194/essd-15-1555-2023, 2023.   Novelties in v1.1 with respect to v1.0: v1.1 of irrigation estimates through the SM-based inversion approach are currently available for the Ebro basin and the Po valley only. The novelties with respect to the previous version are: (i) the use of high-resolution (1 km) potential evapotranspiration rates in the algorithm and (ii) temporal extension as now the data sets cover a 6-year period from January 2016 to December 2021. Acknowledgements: ESA Irrigation+ project, <a href="https://esairrigationplus.org/">https://esairrigationplus.org/</a>, (contract n. 4000129870/20/I-NB). ESA 4DMED-Hydrology project, <a href="https://esairrigationplus.org/">https://www.4dmed-hydrology.org/</a>, (contract n. 4000136272/21/I-EF).

RMG-DB-11: Enumerating Reaction Space for Small Molecule Chemistry

This repository presents approximately 750 million atom-mapped reaction SMILES. Reactions are generated by applying templates from the Reaction Mechanism Generator (RMG) database to a subset of the species from GDB11. Thus, we refer to this dataset as RMG-DB-11 i.e., the Reaction Mechanism Generator Database whose species contain up to 11 heavy atoms. All SMILES have been canonicalized by RDKit. All reactions are labeled with their corresponding RMG template. This data serves as a crucial starting point for quantitative predictive chemistry. Many methods that search for transition state structures require atom-mapped SMILES, which this repository provides. This data is also well-suited for unsupervised pre-training of various machine learning models. To parse the data with Python, start with import pandas as pd. Reactions with 1-8 heavy atoms can be parsed using the following code snippet: pd.read_csv(<filepath>). Reactions with 9 heavy atoms can be parsed using pd.read_pickle(<filepath>, compression='zip'). The file names below include the word "zip" as a helpful hint to use the compression argument. Due to the large number of reactions with 10 and 11 heavy atoms, these are split into smaller chunks. First untar the file using tar -xvf <tar_archive> to obtain several zipped pickle files that can each be parsed using the same method as with 9 heavy atoms.

opencc-by-4.0Jul 2023View details →

cMSSM parameter space points generated with SPheno and micrOMEGAS

These two datasets were produced to be used in two lectures on Machine Learning for SUSY Model Building taught in <a href="https://indico.cern.ch/event/1214657/">pre-SUSY 2023 summer school</a> in Southampton. The code used to generate and to analyse these data can be found <a href="https://gitlab.com/miguel.romao/ml-for-model-building-susy-2023">here</a>. The datasets are as following: <ul> <li>1 million points generated using SPheno only (so no Dark Matter relic density) for the cMSSM with the physical parameters randomly sampled from the table bellow. The columns are <ul> <li>'m0', 'm12', 'A0', 'tanb': the four physical parameters of the theory</li> <li>'idx': an utility identifier used during generation, can/should be ignored</li> <li>The flattened SPheno outputs. These are obtained by reading the resulting slha spectrum file outputted by SPheno and flatten the blocks. For example from the 'MINPAR' block, the key-value pairs are given by the columns  'MINPAR_1', 'MINPAR_2',  'MINPAR_3',  'MINPAR_4', 'MINPAR_5', and likewise for all blocks in the slha file.</li> </ul> </li> <li>10 thousand points generated using SPheno, and which spectrum outputs was then fed to micrOMEGAS (MSSM model configured to accept low-scale slha files as input), with the physical parameters randomly sampled from the same table bellow. The columns are: <ul> <li>The same as above, in addition to</li> <li> 'Omega', 'dm_spin',  'dm_mass' obtained from the micrOMEGAS output, representing Dark Matter relic density, Dark Matter spin, Dark Matter mass, respectively.</li> </ul> </li> </ul> The full list of columns can be seen in `column_names.txt` file. Versions: <ul> <li>SPheno 4.0.5, with a patch to output a warning when the LSP is charged. This version can be found <a href="https://gitlab.com/lip_ml/blackboxbsm">here</a>.</li> <li>micrOMEGAS 5.3.41, with the MSSM model adapted for low-scale slha inputs.</li> </ul> The datasets are provided in <a href="https://parquet.apache.org/">Apache `parquet`</a> format. In order to read them using `pandas`, an installation with the optional flag `[parquet]` should be used. Alternatively, one can use <a href="https://arrow.apache.org/docs/python/index.html">`pyarrow`</a>.

opencc-by-4.0Jun 2023View details →

M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research [V1.6]

V1.6 release notes: <ul> <li>The test subset is released, which contains T1w (6 repetitions/subject), T2w (6 repetitions/subject), and FLAIR (4 repetitions/subject) data from 25 new subjects. These data have passed motion inspection, but one should note that due to the doubled repetition numbers, the average inter-contrast motions are around twice larger than those in the training and validation subsets. To facilitate users, we release the ground truth images as well, but please do not use them during hyperparameter tuning.</li> </ul> V1.5 release notes: <ul> <li>T1w Gradient echo (GRE) data for all 183 subjects are released. Note that the phase encoding direction for GRE data is in the AP direction, different from other contrasts. These GRE data were not checked for motions.</li> <li>A few incorrect records of patient_id were corrected in the H5 files.</li> <li>Scans 2022062708 and  2022062709 were removed due to duplication. Two new scans were added to replace them.</li> </ul> V1.1 release notes: <ul> <li>Please refer to https://www.nature.com/articles/s41597-023-02181-4 for details.</li> </ul>

The administrative topography of Rome. Mapping administrative space and the spatial dynamics of Roman Republicanism

This dataset contains the following figures: Table 1, The Radar Chart Map 1, ROME, 2nd CENTURY BCE Map 2, ROME, 1st CENTURY BCE Map 3, ROME, 1st CENTURY ACE Map 4, ROME, 2nd CENTURY ACE Map 5, ROME, 3rd CENTURY ACE

Navigating within the Safe Operating Space with Carbon Capture On-Board

This file contains the data used to plot Figure 3 and Figure 4 in the manuscript Navigating within the Safe Operating Space with Carbon Capture On-Board published in ACS Sustainable Chemistry and Engineering.

opencc-by-4.0Oct 2022View details →

Data files for:Critical assessment of the chemical space covered by LC-HRMS non-targeted analysis

This upload contains the data for the review: "Critical assessment of the chemical space covered by LC-HRMS non-targeted analysis". All the files needed to run the code uploaded to GitHub (https://github.com/tobihul/CEC_review_code) can be found here. Included is:  <ul> <li>All 2657 structures found in the studied papers with their CID, InChIKey, and SMILES and whether they can be found in MassBank</li> <li>All the experimental parameters retrieved for each study in each category along with the general scope of each study</li> <li>The file with the CID, MW, XLogP3 and experimental parameters for each of the 61 papers</li> <li>The CSV file containing all classes of each of the compounds from the papers</li> <li>The CSV with all of the structures used to plot the chemical space of NORMAN SusDat (their CIDs)</li> </ul>

opencc-by-4.0Aug 2023View details →

Human Inner Ear Anatomy: Labeled Volume CT Data of Inner Ear Fluid Space and Anatomical Landmarks

The provided dataset comprises 43 instances of temporal bone volume CT scans. The scans were performed on human cadaveric specimen with a resulting isotropic voxel size of \(99 \times 99 \times 99 \, \, \mathrm{\mu m}^3\). Voxel-wise image labels of the fluid space of the bony labyrinth, subdivided in the three semantic classes cochlear volume, vestibular volume and semicircular canal volume are provided. In addition, each dataset contains JSON-like descriptor data defining the voxel coordinates of the anatomical landmarks: (1) apex of the cochlea, (2) oval window and (3) round window. The dataset can be used to train and evaluate algorithmic machine learning models for automated innear ear analysis in the context of the supervised learning paradigm.   Usage Notes The datasets are formatted in the HDF5 format developed by the <a href="https://www.hdfgroup.org/solutions/hdf5/">HDF5 Group</a>. We utilized and thus recommend the usage of Python bindings <a href="https://www.h5py.org/">pyHDF</a> to handle the datasets. The flat-panel volume CT raw data, labels and landmarks are saved in the HDF5-internal file structure using the respective group and datasets: <pre><code>raw/raw-0 label/label-0 landmark/landmark-0 landmark/landmark-1 landmark/landmark-2</code></pre> Array raw and label data can be read from the file by indexing into an opened h5py file handle, for example as numpy.ndarray. Further metadata is contained in the attribute dictionaries of the raw and label datasets. Landmark coordinate data is available as an attribute dict and contains the coordinate system (LPS or RAS), IJK voxel coordinates and label information. The helicotrema or cochlea top is globally saved in landmark 0, the oval window in landmark 1 and the round window in landmark 2. Read as a Python dictionary, exemplary landmark information for a dataset may reads as follows: <pre><code class="language-python">{'coordsys': 'LPS', 'id': 1, 'ijk_position': array([181, 188, 100]), 'label': 'CochleaTop', 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]), 'xyz_position': array([ 44.21109689, -139.38058589, -183.48249736])}</code></pre>   <pre><code class="language-python">{'coordsys': 'LPS', 'id': 2, 'ijk_position': array([222, 182, 145]), 'label': 'OvalWindow', 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]), 'xyz_position': array([ 48.27890112, -139.95991131, -179.04103763])}</code></pre>   <pre><code class="language-python">{'coordsys': 'LPS', 'id': 3, 'ijk_position': array([223, 209, 147]), 'label': 'RoundWindow', 'orientation': array([-1., -0., -0., -0., -1., -0., 0., 0., 1.]), 'xyz_position': array([ 48.33120126, -137.27135678, -178.8665465 ])}</code></pre>

opencc-by-nc-4.0Nov 2023View details →

Model Weights for "Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting"

Model weights for use with the SatIQ fingerprinting models used in the paper “Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting”. The models are used to authenticate Iridium satellites from high sample rate message headers. The data collection and model code can be found at the following URL: <a href="https://github.com/ssloxford/SatIQ">https://github.com/ssloxford/SatIQ</a> The preprint is available on arXiv at the following URL: <a href="https://arxiv.org/abs/2305.06947">https://arxiv.org/abs/2305.06947</a> The final trained model is <code>ae-triplet-final.h5</code>. The others are from the additional experiments and analyses described in the paper, and are included for completeness. When using this data, please cite the following paper: “Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting”. The BibTeX entry is given below: <pre><code>@inproceedings{smailesWatch2023, author = {Smailes, Joshua and K{\"o}hler, Sebastian and Birnbach, Simon and Strohmeier, Martin and Martinovic, Ivan}, title = {{Watch This Space}: {Securing Satellite Communication through Resilient Transmitter Fingerprinting}}, year = {2023}, publisher = {Association for Computing Machinery}, booktitle = {Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security}, location = {Copenhagen, Denmark}, series = {CCS '23} }</code></pre>

Space weather disrupts nocturnal bird migration

Our paper tests for the effects of space weather-induced geomagnetic disturbances on radar-detected nocturnal bird migration. We find evidence for a ~10% decrease of migration intensity after controlling for weather variables and spatiotemporal autocorrelation, and also for a decrease in the effort birds spent flying against the wind in the fall, especially under overcast conditions. This repository provides the data and the code used to arrive at these conclusions and plot the main results. Weather radar data was processed from the NOAA NEXRAD network, weather data was accessed from the North American Regional Reanalysis, and magnetometer data was accessed from the SuperMAG inventory.