Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,582
datasets available to search
ShareScore release 0.9.0
Dataset results
1,582 results for “manuscript”
Supporting dataset for manuscript "Estimating the microarthropod diversity in cropping systems by comparing ecological indices across Europe" by Bigiotti et al. 2025
<p>This dataset contains counts of mesofauna individuals classified as Biological Forms (BF). It represent the raw data used for all indices calculation in the manuscript entitled "Microarthropod communities' diversity for soil health assessment: comparing several ecological indices across Europe" by authors Gaia Bigiotti, Francesco Vitali, Stefano Mocali, Giovanni L’Abate, Eligio Malusà, Dawid Kozacki, Irena Bertoncelj, Morgane Ourry, Massimo Pugliese, Heinrich Maisel, Expedito Olimi, Maria Grazia Tommasini, Carlo Jacomini, and Lorenzo D’Avino.</p>
Dataset for the manuscript: Pixel-wise programmability enables dynamic high-SNR cameras for high-speed microscopy
<p>These are the data files used to generate the figures in the paper: Pixel-wise programmability enables dynamic high-SNR cameras for high-speed microscopy. DOI: 10.1101/2023.06.27.546748</p>
Dataset for the submitted manuscript titled 'Response of Southern Ocean Resource Stress in a Changing Climate'
<p>Netcdf output files of Primary Production, carbon export, Fe and Mn limitations, and deficiencies from PISCES-QUOTA model with Mn limitation (described in Anugerahanti and Tagliabue, 2023) forced by historical IPSL CM5A climate model simulation (1850-2005) and RCP8.5 high emission IPSL CM5A simulation (2005-2100) on the ORCA2 grid, as described and discussed in Anugerahanti and Tagliabue, in the manuscript submitted for Geophysical Research Letters. Due to the large size of the files, this has been collated to only contain surface/ upper 100m south of 40S. </p>
Groundwater level data used in the manuscript titled "An explainable Bayesian TimesNet for probabilistic groundwater level prediction with application to semi-arid regions"
<p>The standardized semimonthly groundwater levels collected from 30 monitoring wells in Dalad County, China. The data were obtained from the Ministry of Water Resources of China and the groundwater yearbooks. The data are used in our submitted manuscript titled "An explainable Bayesian TimesNet for probabilistic groundwater level prediction with application to semi-arid regions". If you find this dataset useful for your research, please consider to cite our manuscript upon publication. </p>
Data files for manuscript "Coral growth along a natural gradient of seawater temperature, pH, and oxygen in a nearshore seagrass bed on Dongsha Atoll, Taiwan"
<p>Data files and README file for the manuscript "Coral growth along a natural gradient of seawater temperature, pH, and oxygen in a nearshore seagrass bed on Dongsha Atoll, Taiwan" by Ariel K. Pezner, Travis A. Courtney, Wen-Chen Chou, Hui-Chuan Chu, Benjamin W. Frable, Samuel A. H. Kekuewa, Keryea Soong, Yi Wei, and Andreas J. Andersson.</p> <p>Data files include carbonate chemistry data from discrete seawater samples taken over a shallow seagrass bed, <em>Porites</em> skeletal extension, density, and calcification rates from 15 coral cores collected in the seagrass bed (as well as collection locations), and data from an autonomous CTD sensor deployed in the shallow seagrass. </p>
Dataset of the manuscript: Efficient removal of nanoplastics from industrial wastewater through synergetic electrophoretic deposition and particle-stabilized foam formation
<p>This dataset is based on the data underlying the figures shown in the manuscript titled Efficient removal of nanoplastics from industrial wastewater through synergetic electrophoretic deposition and particle-stabilized foam formation. A readme file is uploaded to decribe the content of all data folders. All data are sorted according to their appearance in the figures of the main manuscript.</p>
Companion data deposit of manuscript: Evaluating and improving the representation of bacterial contents in long-read metagenome assemblies
<p>This upload contains the metagenome assemblies and their binning results generated & described in the manuscript "Evaluating and improving the representation of bacterial contents in long-read metagenome assemblies" (preprint version: arxiv2210.00098, "Towards complete representation of bacterial contents in metagenomic samples"). </p> <p>Mapping of sample names in the file names and the descriptors used as in the manuscript can be found in table S1, which is available along with the manuscript and also included in the supplementary_tables_and_figures tar archive here.</p>
Dead Sea Scrolls data collection (images, labels, prediction plots) for dating ancient manuscripts using radiocarbon and AI-based writing style analysis
<p>The dataset is associated with the following article:<br>Title: <strong>Dating ancient manuscripts using radiocarbon and AI-based writing style analysis</strong><br>Authors: Mladen Popović, Maruf A. Dhali, Lambert Schomaker, Johannes van der Plicht, Kaare Lund Rasmussen, Jacopo La Nasa, Ilaria Degano, Maria Perla Colombini, and Eibert Tigchelaar<br><em>(Under review)</em></p> <p>This data set is collected for the ERC project:<br>The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls<br>PI: Mladen Popović<br>Grant agreement ID: 640497<br>Project website: <a href="https://cordis.europa.eu/project/id/640497">https://cordis.europa.eu/project/id/640497</a></p> <p> </p> <p><strong>Copyright (c) </strong> University of Groningen, 2024. All rights reserved.<br><strong>Disclaimer and copyright notice for all data contained on the *.tar.gz files:</strong></p> <p><strong>1)</strong> permission is hereby granted to use the data for research purposes. It is not allowed to distribute this data for commercial purposes.</p> <p><strong>2) </strong>provider gives no express or implied warranty of any kind, and any implied warranties of merchantability and fitness for purpose are disclaimed.</p> <p><strong>3) </strong>provider shall not be liable for any direct, indirect, special, incidental, or consequential damages arising out of any use of this data.</p> <p><strong>4) </strong>the user should refer to the first public article mentioned above on this data set.</p> <p><strong>5) </strong>the recipient should refrain from proliferating the data set to third parties external to his/her local research group. Please refer interested researchers to this site to obtain their own copy.</p> <p> </p> <p><strong>Organization of the data:<br></strong><em>(Update on 19 April 2024: OxCal data for accepted 2-sigma ranges are updated with the incusion and exclusion of minor peaks. New prediction plots are added after the model is trained with accepted 2-sigma ranges, including minor peaks. The old plots are also kept. <br><br><OLD Updates below; disregard><br>updated on 07-Feb-2024: OxCal data for selected ranges added in a new directory in addition to previously available original OxCal data. Enoch's prediction plots and test images are reorganized for easy access to the users.<br><OLD Updates above; disregard><br><br>Please use the files from this version and disregard the previous two versions: 10.5281/zenodo.10629480 and 10.5281/zenodo.8168210)</em></p> <p>There are four *.tar.gz files:</p> <p><em><strong>C14-Oxcal-data-updated.tar.gz</strong></em> contains one directory with radiocarbon data (OxCal [1] raw data) for all 30 manuscripts. Three additional directories contain name-corrected files for original OxCal data, files with accepted ranges, and files with accepted ranges including minor peaks. Please refer to the original article for details about OxCal data and the manuscripts. 25 out of 30 raw OxCal data are used (accepted ranges only) as the training labels during the training of Enoch, the date prediction model.</p> <p><em><strong>train-images-c14.tar.gz</strong></em> contains the clean and preprocessed (binarized, aligned, and arrangement corrected) training images for the 25 radiocarbon-dated training manuscripts (including 4Q52; 64 images in total). </p> <p><em><strong>test-images-all.tar.gz</strong></em> contains the clean and preprocessed test images for 135 previously undated manuscripts. The images are organized in three different directories: the first one with all 359 images for the 135 manuscripts, the second one with the selected 135 images, and the final one with 25 images to illustrate the poor quality of images. </p> <p><em><strong>Enoch-prediction-new-with-minor-peaks.tar.gz</strong></em> contains the new date prediction plots for each of the 135 test images, where Enoch was trained with the inclusion of minor peaks for the 2-sigma accepted ranges and with a data balancing threshold of 0.05. These plots are used by expert palaeographers' evaluation of Enoch's style-based date predictions of 135 previously undated manuscripts.</p> <p><em><strong>Enoch-predictions.tar.gz</strong></em> contains the date prediction plots for each of the 135 test images. There are two directories inside the *.tar.gz file:<br><br>- <em>prediction-plots-for-selected-135:</em> Prediction plots with data balancing threshold of 0.05. <br>- <em>extra-plots:</em> contains four additional directories:<br> - <em>Enoch-predictions-c14wo4Q52-balanced05:</em> Prediction plots with data balancing threshold of 0.05. <br> - <em>Enoch-predictions-c14wo4Q52-balanced10:</em> Prediction plots with data balancing threshold of 0.1.<br> - <em>Enoch-predictions-c14wo4Q52-unbalanced:</em> Unbalanced raw predictions.<br> - <em>Enoch-predictions-c14wo4Q52-combined:</em> Combined plots with all three prediction plots (unbalanced, 0.05, 0.1).<br>Please refer to the original article for more details.</p> <p>The updated code to run the plot is available here: <a href="https://doi.org/10.5281/zenodo.10998860">https://doi.org/10.5281/zenodo.10998860</a></p> <p><strong>If you have any questions, please get in touch with us:</strong><br>Mladen Popović <m.popovic(at)rug.nl><br>Maruf A. Dhali <m.a.dhali(at)rug.nl><br>Lambert Schomaker <l.r.b.schomaker(at)rug.nl></p> <p> </p> <p><strong>References:</strong><br>1. Bronk Ramsey, C. (2001). Development of the radiocarbon calibration program. <em>Radiocarbon</em>, <em>43</em>(2A), 355-363.</p>
Supplementary material of the manuscript "Beyond authorship: Analyzing disciplinary patterns of contribution statements using the CRediT taxonomy"
<p>Supplementary material of the manuscript "Beyond authorship: Analyzing disciplinary patterns of contribution statements using the CRediT taxonomy". In this research article we present the first cross-disciplinary descriptive analysis on the use of contribution statements. Our main objective is to obtain further insight on contributions by a variety of fields (Multidisciplinary, Health, Life, Physical and Social Sciences) from the largest dataset used up to now. We examine more than 700,000 articles published between 2017 and 2024 in Elsevier and PLOS journals, in combination with bibliometric data extracted from the Scopus database. The descriptive analysis of the dataset focuses on the overall coverage of the merged data, the distribution of authorship and disciplines at paper level, and the interactions between contribution statements, author order and disciplines. Our two main findings indicate that, on the one hand, looking at contributions and authorship order can enrich the way we understand science as a social endeavor. On the other hand, delving deeper into contributorship differences by field is key. We underscore the value of the CRediT taxonomy in unveiling nuanced research dynamics and offering a more equitable framework for evaluation.</p>
Dataset of the manuscript: Predictive design of plasmonic color
<p>This data publication is based on the metadata and datasets underlying the manuscripts "Predictive design of plasmonic color"</p> <p>Folder "Figure 1" contains:</p> <ul> <li>Simulated Extinction spectra of 120 nm silica particle coated with 20 nm gold shell</li> <li>Simulated Transmission spectra of 120 nm silica particle coated with 20 nm gold shell scaled for different concentrations + Lab and RGB color coordinates</li> <li>3D plots of accessible color gamut for Ag, Au, SiO2@Au and SiO2@Ag nanoparticles</li> </ul> <p>Figure "Figure 2" contains:</p> <ul> <li>Simulated colors of targeted for varying concentrations</li> <li>Color-map for color of the year 2022</li> <li>Color coordinates (Lab and RGB) of structure in spot 1 and spot 4 scaled for different concentrations</li> </ul> <p>Figure "Figure 3" contains:</p> <ul> <li>Photographs of realized particle dispersion (spot1: 0@52, spot2: 113@46, spot3: 137@24, spot 4: 398@37) at high and low concentrations, approx. concentrations in number of particles per ml</li> <li>TEM images of realized particles (for core-shell particle: after seeding (x@NP) and after shell growth)</li> <li>UV-Vis:</li> <ul> <li>Measured absorbance spectra and scaled transmission spectra of realized particles spot</li> <li>Color coordinates of simulated, experimentally determined and target color</li> </ul> </ul> <p>Figure "Figure 4" contains:</p> <ul> <li>Photographs of realized particle dispersion (CRC1411yellow: Ag0@3-24, CRC1411red: Au0@13, CRC1411blue: Au113@35), approx. concentrations in number of particles per ml</li> <li>TEM images of realized particles (for core-shell particle: after seeding (x@NP) and after shell growth)</li> <li>UV-Vis:</li> <ul> <li>Measured absorbance spectra and scaled transmission spectra of realized particles</li> <li>Color coordinates of simulated, experimentally determined and target color</li> </ul> <li>color maps for CRC1411 target colors</li> </ul> <p> </p>
Code for manuscript "Organic ligands in whale excrement support iron availability and reduce copper toxicity to the surface ocean" by Monreal et al.
<p>.zip file containing GitHub repository titled "ligands-in-whale-excrement" (<a href="https://github.com/patrickmon38/ligands-in-whale-excrement/tree/main">https://github.com/patrickmon38/ligands-in-whale-excrement/tree/main</a>)<br><br><strong>README from GitHub: </strong></p> <div> <h3>Code used to generate figures for the manuscript "Organic ligands in whale excrement support iron availability and reduce copper toxicity to the surface ocean" by Monreal et al. are found in this repository.</h3> </div> <div> <p>In press at Communications Earth & Environment</p> <p> </p> </div> <p>Most data (all except .mzXML data) called in code is from Github_Data_For_Whale_Ligand_Manuscript.xlsx in this repository.</p> <p> </p> <p>Mass spec data from .mzXML files are has been depositied and is available for download in the Mass Spectrometry Interactive Virtual Environment (MassIVE). LC-ESI-MS (Orbitrap) and LC-FT-ICR-MS raw data can be accessed there under MSV000094994 (doi:10.25345/C50000B5D) and MSV000094995 (doi:10.25345/C5V98034P), respectively.</p> <p> </p> <p>html output from Rmarkdown file can be viewed directly at <a href="https://html-preview.github.io/?url=https://github.com/patrickmon38/ligands-in-whale-excrement/blob/main/Figures_for_GitHub_Whale_Excrement_Manuscript.html" rel="nofollow">https://html-preview.github.io/?url=https://github.com/patrickmon38/ligands-in-whale-excrement/blob/main/Figures_for_GitHub_Whale_Excrement_Manuscript.html</a>.</p> <p>If trying to run Rmarkdown on your own system, you will need to download the .xlsx and .mzXML files and change paths accordingly.</p> <p>Co-authors of this manuscript:</p> <h5>Patrick J. Monreal (University of Washington)</h5> <h5>Matthew S. Savoca (Stanford University)</h5> <h5>Lydia Babcock-Adams (National High Magnetic Field Laboratory)</h5> <h5>Laura E. Moore (University of Washington)</h5> <h5>Angel Ruacho (Univesrity of Washington)</h5> <h5>Dylan Hull (University of Washington)</h5> <h5>Logan J. Pallin (Unversity of California, Santa Cruz)</h5> <h5>Ross C. Nichols (Unversity of California, Santa Cruz)</h5> <h5>John Calambokidis (Cascadia Research Collective)</h5> <h5>Joseph A. Resing (Unviersity of Washington/CICOES/NOAA)</h5> <h5>Ari S. Friedlaender (Unversity of California, Santa Cruz)</h5> <h5>Jeremy Goldbogen (Stanford University)</h5> <h5>Randelle M. Bundy (University of Washington)</h5> <p> </p> <div> </div> <div><strong>If there are issues or questions with the code, author for contact is Patrick Monreal (<a href="mailto:pmonreal@uw.edu">pmonreal@uw.edu</a>).</strong></div>
Data accompanying manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'
<p>This upload contains the data which accompanies the manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'.</p> <p><strong>Metrics calculated for each of the 4269 50 km by 50 km regions</strong></p> <table> <tbody> <tr> <td>Filename (IFPA)</td> <td>Filename (Bedmachine)</td> <td>Filename (Bedmap3)</td> <td>Description</td> </tr> <tr> <td>x_ifpa.nc<br>y_ifpa.nc</td> <td> </td> <td> </td> <td>X and Y coordinates</td> </tr> <tr> <td>mean_ifpa.nc or ifpa_mean.nc</td> <td>bedmach_mean.nc</td> <td> </td> <td>Mean elevation (m)</td> </tr> <tr> <td> <p>ifpa_count.nc<br>ifpa_count_max_20.nc<br>ifpa_count_max_100.nc<br>ifpa_count_max_250.nc</p> </td> <td>bedmach_count.nc<br>bedmach_count_max_20.nc<br>bedmach_count_max_100.nc<br>bedmach_count_max_250.nc</td> <td> </td> <td> <p>The number of hills with a 50 m prominence within a 5 km neighbourhood <br>(or 20 m, 100 m, 250 m respectively)</p> </td> </tr> <tr> <td>ifpa_b1_5km.nc<br>ifpa_b1_thickness.nc</td> <td>bedmach_b1_5km.nc<br>bedmach_b1_thickness.nc</td> <td> </td> <td>The fourier fractal dimension for wavelengths greater than 5 km or the ice thickness respectively</td> </tr> <tr> <td>ifpa_std_deslope.nc<br>i_std_l.nc</td> <td>bedmach_std_deslope.nc<br>b_std_l.nc</td> <td> </td> <td>The standard deviation:<br>- with the best fit slope removed<br>- of some long wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_wav_max_power.nc</td> <td>bedmach_wav_max_power.nc</td> <td> </td> <td>The wavelength in the Fourier spectrum with the maximum power</td> </tr> <tr> <td>ifpa_rms_slope.nc<br>i_rms_slope_h.nc</td> <td>bedmach_rms_slope.nc<br>b_rms_slope_h.nc</td> <td> </td> <td>The RMS slope of:<br>- the bed elevation<br>- some short wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_rms_curvature.nc</td> <td>bedmach_rms_curvature.nc</td> <td> </td> <td>The RMS curvature of the bed elevation</td> </tr> <tr> <td> </td> <td>source.nc</td> <td> </td> <td>The method used to calculate the bed topography (Bedmachine only)</td> </tr> <tr> <td> </td> <td> </td> <td>mean_nearest.nc</td> <td>The mean distance from each IFPA grid point to the nearest Bedmap3 data point</td> </tr> <tr> <td> </td> <td> </td> <td>bedmap3_count.nc</td> <td>The number of Bedmap3 data points within the region</td> </tr> </tbody> </table> <p> </p> <p><strong>Datasets required for plotting</strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>Groundingline_Antarctica_v2.shp</td> <td>Antarctic grounding line </td> </tr> <tr> <td>ECR_features.shp</td> <td>Outline of significant features within the example regions chosen</td> </tr> <tr> <td>IFPA_bed.nc</td> <td>OLD VERSION of IFPA bed topography map for Antarctica</td> </tr> <tr> <td> <p>IFPA_bed_C50.nc</p> </td> <td>IFPA bed topography map for Antarctica (without radar correction)</td> </tr> </tbody> </table> <p><strong>To plot the figures, you will either require the following datasets: </strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>IFPA_figures_data.zip</td> <td>Additionally data to plot figures 1,6,8 and 9 </td> </tr> <tr> <td> <p>HA_data.csv<br>HB_data.csv<br>RSB_data.csv</p> </td> <td>For Highland A, Highland B and Recovery Subglacial Basin<br>IFPA (radar corrected), IFPA (not radar corrected), Bedmachine v3, and ice-penetrating radar profiles</td> </tr> <tr> <td> <p>HA_data_ifpa.csv<br>HB_data_ifpa.csv<br>RSB_data_ifpa.csv</p> </td> <td> <p>For Highland A, Highland B and Recovery Subglacial Basin<br>IFPA (radar corrected) map for the region crossed by the ice-penetrating radar profile</p> </td> </tr> </tbody> </table> <p><strong>or, the figures can be regenerated using the following datasets, which are available at the listed DOIs</strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> <td>Reference</td> <td>DOI</td> </tr> <tr> <td>GaplessREMA100.nc</td> <td>Gapless REMA Antarctica dataset at 100m resolution</td> <td>Dong et al. (2022)</td> <td>10.1016/j.isprsjprs.2022.01.024</td> </tr> <tr> <td>BedMachineAntarctica-v3.nc</td> <td>MEaSURES BedMachine Antarctica bed topography map version 3</td> <td>Morlighem et al. (2020)</td> <td>10.5067/FPSU0V1MWUB6</td> </tr> <tr> <td>antarctica_ice_velocity_450m_v2.nc</td> <td>ITSLIVE Antarctic velocity map</td> <td>Gardner et al. (2019)</td> <td>10.5067/6II6VW8LLWJ7</td> </tr> <tr> <td>antarctic_ice_vel_phase.nc</td> <td>MEaSURES Antarctic velocity map</td> <td>Mouginot et al. (2019)</td> <td>10.5067/PZ3NJ5RXRH10</td> </tr> <tr> <td>UTIG_2010_ICECAP_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the UTIG Icecap survey</td> <td>Wright et al. (2012)</td> <td>10.1029/2011JF002066</td> </tr> <tr> <td>BAS_2012_ICEGRAV_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the BAS Icegrav survey</td> <td>Forsberg et al. (2018)</td> <td>10.1144/SP461.17</td> </tr> </tbody> </table> <p><strong> </strong></p>
Supporting data for the manuscript "Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads"
<p>Supporting data for the manuscript "Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads".</p> <p>The archive contains files that are necessary to reproduce the cell line benchmarks from the paper, including:</p> <ul> <li>Scripts and command lines</li> <li>Original VCF outpurs of all tools used in benchmarking</li> <li>Minda evaluations and truthset VCF files</li> <li>Full Severus outputs + visualizations</li> <li>truvari calls</li> </ul>
Dataset for the manuscript "Attribute Recognition: A New Method for Grouping Planetary Images by Visual Characteristics, Using the Example of Mn-Rich Rocks in the Floor of Gale Crater, Mars."
<p>This dataset supports the manuscript "Attribute Recognition: A New Method for Grouping Planetary Images by Visual Characteristics, Using the Example of Mn-Rich Rocks in the Floor of Gale Crater, Mars." The dataset is contained in a single CSV file with 201 data rows (one row per NASA Curiosity rover ChemCam instrument target used in the study). The columns in this dataset include the martian solar day (sol) on which each target was imaged by ChemCam; the standoff distance from ChemCam to each target (in meters); binary columns (values are either 1 or 0, indicating presence or absence, respectively) for each of the 17 visual attributes we documented for each target image; the corresponding greyscale ChemCam RMI mosaic file location (on the Planetary Data System); and columns indicating which group each target was sorted into under each classification algorithm discussed in the text (P_{SG}: simple graph method; P_{AP}: automatic partitioning method; P_{\lambda=1.6}: community detection method with \lambda=1.6). To obtain the binary strings used for the classification algorithms, the 17 visual attribute columns can be concatenated. </p> <p>Also included is a collection of HTML files that enables easy viewing of the RMI mosaics in each cluster, using the Planetary Data System links. To use it, download the <code>.zip</code> file, unzip it, and open the <code>index.html</code> file in the browser of your choice (likely will work to simply double-click <code>index.html</code>)</p>
In-situ Treatment of manuscripts and Printed Books in Trinity College Dublin
<p>This is the recording and transcript of a lecture given by Anthony Cains in 1988 at the Fifth Anniversary Conference of the Parker Library Conservation Project. A publication based upon the conference paper, supplemented with new material and updated, was released in 1994:</p> <p>Cains, Anthony G. 1994. ‘<em>In-Situ</em> Treatment of Manuscripts and Printed Books in Trinity College Dublin’. In <em>Conservation and Preservation in Small Libraries</em>, edited by Nicholas Hadgraft and Katherine Swift, 127–31. Cambridge: Parker Library Publications.</p>
Data files and videos for manuscript "Biallelic pathogenic variants in ROBO1 associate with syndromic CAKUT"
<p>#2021-11-20<br> #Summary<br> This ZIP-file contains the supplementary files of our ROBO1 study. <br> Suppl. File S1 details the next-generation sequencing-based gene panel comprising 426 CKD-associated genes performed in ID 1.<br> Suppl. File S2 contains a variant information and raw in silico data.<br> Suppl. Videos SV1A and SV1B show cardiac valve anomalies comprising mitral valve prolapse in ID 1.<br> Suppl. Video SV2 shows renal anomalies and hydroureter as documented in-vivo by microCT-scan of the mutant mice.</p> <p>#Folder structure<br> ./ (parent directory containing this README file and all subfolders)<br> ./Files/ (contains Excel Suppl. File S1 and Suppl.File S2)<br> ./Videos/ (contains the Suppl. Videos)</p> <p><br> #Files and checksums<br> Algorithm Hash Path<br> --------- ---- ----<br> MD5 2BCB0774F8303FC5DBA2313DFD19FBEE Files\FileS1_Panel-Content.xlsx<br> MD5 015F01832AE1C6610D9C1FB8902FDF17 Files\FileS2_Variants-and-Domains_2021-05-18.xlsx<br> MD5 8BBE3DE1C21F161BCE07A2AFB4A6F7CD Videos\Supplementary Video SV1\Supplementary Video SV1.docx<br> MD5 5EA354253D7E489B48EDFBDE59CFB798 Videos\Supplementary Video SV1\Supplementary Video SV1A.mov<br> MD5 61796C4A5F7EAD79D642ED9D11514A95 Videos\Supplementary Video SV1\Supplementary Video SV1B.mov<br> MD5 4098A23D5CE1C67C1D507D4DD5F4F866 Videos\Supplementary Video SV2\Lo_Robo mutant.mov<br> MD5 D0B219332F227DBECF1A2A1A6AD0925A Videos\Supplementary Video SV2\Supplementary Video SV2.mov</p>
Data used for manuscript "The coordination of green-brown food webs and their disruption by anthropogenic nutrient inputs"
<p>Data used for manuscript "The coordination of green-brown food webs and their disruption by anthropogenic nutrient inputs".</p> <p>This includes estimations of various properties of food webs, such as stocks of compartments, fluxes between compartments, and conversion efficiencies.</p>
OpenData for the Independent morphological variables correlate to aging, Mild Cognitive Impairment, and Alzheimer's Disease manuscript
<p>Results from processed structural MRI images from the <a href="https://www.sciencedirect.com/science/article/pii/S1053811920306868">AHEAD</a> and <a href="https://www.nature.com/articles/s41597-021-00870-6">AOMIC</a> dataset included in the Supplementary Information at the Independent morphological variables correlate to aging, Mild Cognitive Impairment, and Alzheimer's Disease manuscript from de Moraes et al. submitted to PNAS.</p> <p>For this data, we processed the datasets with <a href="https://surfer.nmr.mgh.harvard.edu/">FreeSurfer</a> v6.0.0 standard processing pipeline (<em>recon-all</em>) and the estimation of the <a href="https://surfer.nmr.mgh.harvard.edu/fswiki/LGI">local Gyrification Index</a> from <a href="http://ltswww.epfl.ch/~schaer/Schaer_TMI.pdf">Schaer, M. et al. 2008</a>. We further extracted the morphological measurements from the generated surfaces using the Cortical Folding Analysis Tools from <a href="https://zenodo.org/record/3608675">Wang et al. 2019</a>. Here, we included the raw morphological datasets joined with the demographics and subjects' information.</p>
Data published in manuscript "Effects of reversal of water flow in an Arctic floodplain river on fluvial emissions of CO2 and CH4" by Castro-Morales et al.
<p>This data is published in the manuscript<strong>:</strong></p> <p>Castro-Morales, K., Canning, A., Körtzinger, A., Göckede, M., Küsel, K., et al. (2022). Effects of reversal of water flow in an Arctic floodplain river on fluvial emissions of CO<sub>2</sub> and CH<sub>4</sub>. <em>Journal of Geophysical Research: Biogeosciences</em>, 127, e2021JG006485. <a href="https://doi.org/10.1029/2021JG006485">https://doi.org/10.1029/2021JG006485</a>.</p> <p>The data contains the water properties and gases data measured at a site in Ambolikha River, meteorological data measured at an eddy covariance tower located in the neighbor floodplain, and data from the analysis of dissolved organic matter in river water samples. The data was collected between 26 June, 2019 and 02 August, 2019.<strong> </strong></p> <p>This folder contains four data files and the file "README_Data_access_Castro-Morales_etal_Ambolikha_River.txt" should be read before accessing the data. The authors recommend downloading Version 2.0 because it is the most up to date data.</p> <p>For questions contact the main and corresponding author Dr. Karel Castro-Morales at: karel.castro.morales@uni-jena.de</p>
SBF-SEM datasets related to the manuscript "Trans-cellular tunnels induced by the fungal pathogen Candida albicans facilitate invasion through successive epithelial cells without host damage" by Lachat et al, 2022.
<p>11 serial block face- scanning electron microscopy (SBF-SEM) datasets described in the manuscript "Trans-cellular tunnels induced by the fungal pathogen Candida albicans facilitate invasion through successive epithelial cells without host damage" by Lachat et al, 2022.</p> <p>Resolution: 10 nm x,y, 100 nm Z.</p> <p>Datasets description and quantification can be found in the Supplementary information.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.