Find research datasets worth reusing

Dataset for publication "Comparative Analysis of the Pharmacokinetics of a New Migliol-Based Antiviral Agent Campecin and Investigation of Degradation Mechanisms"

Open the record for dataset details and reuse information.

Master Index for Publicly accessible Data : SPPELME project

Open the record for dataset details and reuse information.

Reproduction package for the publication "Tidal disruption event AT2020ocn: early-time X-ray flares caused by a possible disc alignment process"

This package contains the data analysed in the paper "Tidal disruption event AT2020ocn: early–time X–ray flares caused by a possible disc alignment process". The software XSPEC (Arnaud 1996) is needed to perform the spectral analysis and reproduce the results shown in the paper. The structure is as follows: ./reproduction_ocn/nicer: contains all processed NICER data used in the paper, grouped by their epochs. "speclist-early.dat" lists all the early-time epochs before MJD 59130. "en_range.dat" lists the selected energy range at each epoch during the early-time period for X-ray spectral analysis. Within each epoch-specific folder, "src.fits" and "bkg.fits" are the source+background and background spectra re-binned using the FTOOL "ftgrouppha"; "*.arf" and "*.rmf" are ancillary file and response file for spectral analysis; rest files are direct products of the NICER data reduction process. See the paper for details. ./reproduction_ocn/swift: contains all Swift/UVOT data used in the paper, grouped by their observation IDs. "m2.fits", "w1.fits", and "w2.fits" contain the UV lightcurves from three UV filters, produced by Swift task "uvotproduct". "swfxraypclc.dat" is the Swift/XRT lightcurve, produced by the online Swift pipeline: https://www.swift.ac.uk/user_objects/ (Evans et al. 2009). "./reproduction_ocn/MOSFiT-products/" includes MCMC products from the MOSFiT package (Mockler et al. 2019). ./reproduction_ocn/xmm: contains the reduced XMM-Newton/EPIC-pn spectra of three epochs used in the paper. "1and2-slim.xcm" is fitting the XMM#1 and XMM#2 spectra using the slim disc model. "3-phenmnlgcl.xcm" and "3-relxillCp.xcm", are fitting the XMM#3 spectrum with, a powerlaw+zbbody model and a slim disc+relxillCp model, respectively.

Supplementary data (excel tables) for manuscritpt under revision (more details when the manuscript will be accepted for publication)

Open the record for dataset details and reuse information.

Dataset related to publication: Landcover-categorized fires respond distinctly to precipitation anomalies in the South-Central United States

Landcover-categorized fires respond distinctly to precipitation anomalies in the South-Central United States Kátia Fernandes and Sen g. Young doi: 10.3389/fenvs.2024.1433920 Abstract Satellite detection of active fires have contributed to advance our understanding of fire ecology, fire and climate dynamics, fire emissions and how to better manage the use of fires as a tool. In this study we use 12 years (2012-2023) of active fire data combined with landcover information in the South-Central United States to derive a monthly, open access dataset of categorized fires. This is done by calculating a fire predominance index used to rank fire prone land covers, which are then grouped into four main landscapes: grassland, forest, wildland and crop fires. County level aggregated analyses reveal spatial distributions, climatologies, and peak fire months that are particular to each fire type. Using the Standardized Precipitation Index (SPI), it is found that during climatological fire peak-month, SPI and fires exhibit an inverse relationship in forests and crops, whereas grassland and wildland fires show less consistent inverse or even direct relationship with SPI. This varied behavior is discussed in the context of landscapes’ responses to anomalies in precipitation, and fire management practices, such as prescribed fires and crop residue burning. In a case study of Osage County (OK) we find that large wildfires, known to be closely related to climate anomalies, occur where forest fires are located in the county and absent in areas of grassland fires. Weaker grassland fires response to precipitation anomalies can be attributed to the use of prescribed burning, which are normally planned under environmental conditions that facilitate control and thus avoided during droughts. Crop fires on the other hand, are set to efficiently burn residue and practiced more intensely in drier years than in wetter, explaining the consistently strong inverse correlation between fires and precipitation anomalies. In our increasingly volatile climate, understanding how fires, vegetation, and precipitation interact has become imperative to prevent hazardous fire conflagrations and to better manage ecosystems.

Data publication for "Different earthquake nucleation conditions revealed by stress drop and b-value mapping in the northern Chilean subduction zone"

Abstract: b-value catalog data publication supplement for "Different earthquake nucleation conditions revealed by stress drop and b-value mapping in the northern Chilean subduction zone" (Folesky, (SciRep,2024), <a href="https://doi.org/10.1038/s41598-024-63015-w">https://doi.org/10.1038/s41598-024-63015-w</a>). b-vales were computed based on the IPOC seismic catalog for northern Chile (Sippl et al., 2023).  b-values are computed only for events that have a known stress drop value, as assigned by Folesky et al., 2024. b-values are computed for the 200-1000 nearest neighbors of each event within a maximum distance of 50km using the maximum likelihood approach as implemented by T.Goebel (Geobel et al., 2017). Each seismotectonic class (Sippl et al., 2023) is processed separately. The standard deviation of the fit is obtained by 100 bootstrap iterations for each event while discarding randomly selected 10% of the neighbors. File descriptions: table columns ID, cls, Lon, Lat, Depth, Magntiude, a, b, Std ------------------ explanation ID : origin time cls : event class Lon : longitude Lat : latitude Depth : depth in km Magnitude : magnitude (MA) a : a- value  b : b- value Std : standard deviation from bootstrapping References: Folesky, Jonas, (SciRep,2024), <a href="https://doi.org/10.1038/s41598-024-63015-w">https://doi.org/10.1038/s41598-024-63015-w</a> Sippl, C., Schurr, B., Münchmeyer, J., Barrientos, S., Oncken, O. (2023): Catalogue of Earthquake Hypocenters for Northern Chile from 2007-2021 using IPOC (plus auxiliary) seismic stations. <a title="Follow link" href="https://doi.org/10.5880/GFZ.4.1.2023.004" target="_blank" rel="nofollow noopener">https://doi.org/10.5880/GFZ.4.1.2023.004</a> Folesky, J., Pennington, CN., Kummerow J., Hofman LR. (JGR: Solid Earth, 2024) <a href="https://doi.org/10.1029/2023JB027549">https://doi.org/10.1029/2023JB027549</a> Goebel, T. H., Kwiatek, G., Becker, T. W., Brodsky, E. E. & Dresen, G. What allows seismic events to grow big?: Insights from b-value and fault roughness analysis in laboratory stick-slip experiments. Geology 45, 815–818 (2017).

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

A public manifold left/right ventricular database

<h1>A public manifold left/right ventricular database</h1> This database contains the 44 postprocessed meshes based on the public databases <a href="https://doi.org/10.5281/zenodo.4590294">10.5281/zenodo.4590294</a> and <a href="https://doi.org/10.5281/zenodo.3890034">10.5281/zenodo.3890034</a>. This database was used to train the DeepSDF of our work Shape of my heart: Cardiac models through learned implicitly defined functions <a href="https://doi.org/10.48550/arXiv.2308.16568">10.48550/arXiv.2308.16568</a>. Further information on the postprocessing can be found in this article. <h2>Structure</h2> The database is split into the closed surfaces for each chamber between endo- and epicardium. <ul> <li>lv <ul> <li>endo</li> <li>epi</li> </ul> </li> <li>rv <ul> <li>endo</li> <li>epi</li> </ul> </li> </ul> The metadata files metadata.pkl.gz and metadata.csv contain a path to all meshes and their respective sources. Here's an example how the files can be read: <pre><code>import pandas as pd metadata = pd.read_pickle("metadata.pkl.gz") #or metadata = pd.read_csv("metadata.csv", sep=";")</code></pre> The file <code>point_clouds.pkl.gz</code> contains a DataFrame of the point cloud data that was used for generated for all 44 meshes and used in the validation and training. The data can be read similar to the metadata: <pre><code>import pandas as pd point_clouds = pd.read_pickle("metadata.pkl.gz")</code></pre> The data contains the following fields (SDF = signed distance function, LV = left ventricular, RV = right ventricular) <table> <tbody> <tr> <td>mesh_id</td> <td>xyz</td> <td>sdf_lv_endo</td> <td>sdf_lv_epi</td> <td>sdf_rv_endo</td> <td>sdf_rv_epi</td> </tr> <tr> <td>ID of the mesh from which the point cloud was generated</td> <td>Spatial coordinate</td> <td>SDF to the LV endocardium</td> <td>SDF to the LV epicardium</td> <td>SDF to the RV endocardium</td> <td>SDF to the RV epicardium</td> </tr> </tbody> </table> All provided meshes contain the following fields: The fiber and sheet orientation of the original meshes, computed using a rule-based approach. <ul> <li>fiber</li> <li>sheet</li> </ul> A true/false, (0/1) mask marking the valves of the meshes. <ul> <li>valve</li> </ul> The universal ventricular coordinates from the original meshes to provide orientation inside the ventricles. See <a href="https://doi.org/10.1016/j.media.2018.01.005">10.1016/j.media.2018.01.005</a> for more details. <ul> <li>uvc_intraventricular</li> <li>uvc_longitudinal</li> <li>uvc_rotational</li> <li>uvc_transmural</li> </ul> <h2>Acknowledgements</h2> This work was supported by the Swiss National Science Fund [Cardiotwin, Weave/Lead Agency, Project number 214817], by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [EXC-2047/1-390685813, and EXC2151-390873048], PRIN-PNRR [project no. P2022N5ZNP], and CSCS [production grant no. s1074].

Supplementary material for the publication: "Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer"

This dataset contains postprocessed data for the publication &bdquo;Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer“.

Temporal Dynamics and Demographic Profiles: Evaluating the Cultural Impact of Public Engagements with Civil War Shelters in Alicante (Spain)

In the context of sustainable tourism, understanding visitors' needs and preferences is crucial for planning effective strategies that promote the conservation of historical and cultural heritage. In this regard, the quantitative analysis of survey results from visitors to Civil War air-raid shelters in Alicante, Spain, emerges as a valuable tool for gaining detailed insights that enable the planning of specific visits and events. This paper aims to explore the significance of using quantitative data in developing strategies for sustainable tourism within this particular context.

Data associated with the publication 'Population-level coding of avoidance learning in medial prefrontal cortex' by Benjamin Ehret et al.

This repository contains data for the following publication: Population-level coding of avoidance learning in medial prefrontal cortex Ehret B., Boehringer R., Amadei E. A., Cervera M. R., Henning C., Galgali A., Mante V., Grewe, B. F. Nature Neuroscience 2024   The associated analysis code is published here: https://github.com/behret/paper_code_active_avoidance   This repository contains 1) source data to reproduce all figures and 2) processed data to reproduce most analyses.  A small subset requires access to the raw data, which is too extensive to be published online. However, raw data can be made available upon request.

Numerical data supporting the publication "Accurate and gate-efficient quantum Ansätze for electronic states without adaptive optimization"

Numerical data and plotting scripts for regenerating figures in the article "Accurate and gate-efficient quantum ansätze for electronic states without adaptive optimisation". See README for more details.

Data set for publication "Effect of milling atmosphere on stability and surface properties of ZnO/vermiculite hybrid nanocomposite powders"

Open the record for dataset details and reuse information.

Model dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"

<div> This is the Noah-MP model simulation dataset for the journal publication titled "Improved snow albedo evolution in Noah-MP land surface model coupled with a physical snowpack radiative transfer scheme"   </div>

CARMA determined light curves and PSDs to accompany ApJ publication Optical Variability Properties of Southern TESS Blazars

A collection of all Figures included in Appendix D of Optical Variability Properties of Southern TESS Blazars (Dingler & Smith 2024).

Quaver regressed light curves to accompany ApJ publication Optical Variability Properties of Southern TESS Blazars

A collection of all Figures included in Appendix A of Optical Variability Properties of Southern TESS Blazars (Dingler & Smith 2024).

Dataset for publication "Thermal cycling damage of silica refractories for high-temperature thermal energy storage (HT-TES) – can it be healed?"

Open the record for dataset details and reuse information.

Supplementary materials for publication Hlanze H. et al. Universal lineage-independent markers of multidrug re-sistance in M. tuberculosis. IJMS 2024.

Drug resistance mutations stat.xlsx - this is an Excel file with data on the statistical evaluation of associations between mutations in protein-coding genes of M. tuberculosis and multidrug resistance; Drug mutation associations for Cytoscape.cys - this is a Cytoscape 3.10.2 dataset with predicted epistatic interactions among global markers of drug resistance in the overall M. tuberculosis population.

Evaluations of public submissions to ICDAR'24 MapText Competition

Evaluations of submissions to the <a href="https://rrc.cvc.uab.es/?ch=28">ICDAR'24 Competition on Historical Map Text Detection, Recognition, and Linking</a>. Files in the archive (<code>evaluations.tar.bz2</code>) are stored in <code>ch28/tY/fZ/W.json</code> where <code>Y</code> is the task number (1–4), <code>Z</code> is the file number (1–2), and <code>W</code> is the submission ID. Tasks are: <ul> <li><code>1</code>: Word Detection</li> <li><code>2</code>: Phrase Detection (Word Detection and Grouping)</li> <li><code>3</code>: Word Detection and Recognition</li> <li><code>4</code>: Phrase Detection and Recognition</li> </ul> Files are: <ul> <li><code>1</code>: Rumsey data set</li> <li><code>2</code>: IGN (French Land Register) data set</li> </ul> The JSON output format results from the <a href="https://github.com/icdar-maptext/evaluation">official competition evaluation script</a> (commit-id: <code>816da23d6d4e44f7fd89ee07278ebbcd12d50d4d</code>). Version 2 updates results to run in an environment consistent with the RRC server.

Public social actions

Open the record for dataset details and reuse information.

Video supplement for publication "The impact of mesh size and microphysics scheme on the representation of mid-level clouds in the ICON model in hilly and complex terrain"

This is the video supplement for the manuscript "The impact of mesh size and microphysics scheme on the representation of mid-level clouds in the ICON model in hilly and complex terrain". It includes compiled videos from allsky cameras at two different field sites (CLOUDLAB (hilly terrain, HT) and CROSSINN (complex terrain, CT)) for in total four case studies (named by their dates). For each case study, we compiled a video of the cloud cover from the model for the 1 km and 65 m resolution, both with a one-moment (1M) and two-moment (2M) microphysics scheme.