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”
Data set for manuscript titled "Morphological, physiological and metabolic responses of diverse barley inbreds to dry down and moderate drought stress"
<p>The primary aim of the study was to understand the genotypic diversity on plant morphology, photosynthetic responses, metabolite shift and their relationship in diverse barley inbreds under dry down (DD) and moderate drought (MD) stress using 23 genetically diverse parental inbreds. The data were collected from over a period of 28 days after the start of stress treatment. The publised data set indcludes the emmeans of all the evaluated characters. Metabolite profiling was done in samples collected from 7 d and 12 d after the start of DD and MD stress.</p>
Data for manuscript
<p>All data form Results and Supplementary matrial in "Considering the asymmetrical configuration of a landslide greatly shifts slope stability assessment" </p>
Raw and analyzed data for manuscript "In vitro eradication of Candida albicans biofilm through cold atmospheric plasma: Unravelling the interdependence of exposure and voltage in the antifungal mode of action."
<p><strong><span>Raw and analysed data for the manuscript, to be submitted to Journal of Infection and Public Health.</span></strong></p> <p> </p> <p><strong><span>Abstract:</span></strong></p> <p><strong><span>Introduction:</span></strong><span> Since <em>Candida spp</em>.</span> <span>is the fourth leading cause of healthcare-associated infections globally, the need for novel antifungal agents is increasing among scientists. This study investigates the potential of cold atmospheric plasma for <em>C. albicans</em> biofilm treatment. <strong>Methods:</strong> Our research focused on <em>in vitro</em> <em>C. albicans</em> biofilm response to varying parameters of plasma application, specifically the impact of treatment duration and input voltage. Evaluation of plasma influence on <em>C. albicans</em> was assessed with viability, membrane integrity, and oxidative stress measurements, along with observations of biofilm chemical composition and hyphae growth after plasma treatment. <strong>Results and Discussion:</strong> The higher plasma input voltage and increased exposure tme resulted in lower <em>C. albicans</em> cell viability, with complete reductions observed after 5 min plasma treatment duration across all input voltages tested. The effect of plasma treatment was further confirmed with a microscopic examination after BacLight<sup>® </sup>staining.</span> <span>Low (8 V) CAP exposure could potentially lead to a phenomenon known as hormesis, which was observed in <em>C. albicans</em> 24 h growth measurements. Additionally, intracellular oxidative stress assessment further proved that with prolonged treatment times, the intensity of oxidative stress, increased. Plasma treatment also affected the hyphae, which exhibited signs of contraction and compression. The chemical characteristics revealed that increasing plasma voltage and exposure time also have a distinguished impact on lipids, proteins, and carbohydrates, typical constituents of fungi biofilms. <strong>Conclusion:</strong> This study underscores the potential of plasma as a promising approach in combating <em>Candida spp</em>. biofilms, shedding light on the intricate dynamics of its impact on biofilm viability, morphology and composition.</span></p>
Additional Supplementary Tables for PTMNavigator Manuscript
<p>Additional supplementary tables that did not fit into the initial submission</p>
The dataset of the manuscript "GPU-HADVPPM4HIP V1.0: higher model accuracy on China's domestically GPU-like accelerator using heterogeneous compute interface for portability (HIP) technology to accelerate the piecewise parabolic method (PPM) in an air quality model (CAMx V6.10)"
<p><strong>bcfile.zip:</strong> the clean boundary condition files.</p> <p><strong>CAMxv6x_cpp.zip: </strong>the source code of CAMx-HIP version which coupled with HIP-HADVPPM scheme.</p> <p><strong>data.zip:</strong> final data tables used to plot figures.</p> <p><strong>emisfile.zip: </strong>the emission files.</p> <p><strong>icfile.zip:</strong> the clean initial condition files.</p> <p><strong>tuvfile.zip </strong>and <strong>o3mapfile.zip:</strong> the photolysis files.</p> <p><strong>outputfile.zip:</strong> the computation results outputted by CAMx model for Fortran version on the Intel Xeon E5-2682 v4 CPU, CUDA version on the NVIDIA K40m and V100 clusters, and HIP version on the China' s domestically heterogeneous cluster A.</p> <p><strong>wrfcamx.zip:</strong> the meteorological files.</p> <p><strong>offline_test_cuda.zip: </strong>the advection module code written in CUDA C language</p> <p><strong>offline_test_fortran.zip:</strong> the advection module code written in Fortran language</p> <p><strong>offline_test_hip.zip: </strong>the advection module code written in HIP C language</p>
Dataset (Global C-responses for CSES and CSES+Swarm+Obs database) presented in the recently submitted AGU manuscript "Electrical conductivity of mantle transition zone and water content revealed by the magnetic data of China Seismo-electromagnetic Satellite".
<p>Dataset (Global C-responses for CSES and CSES+Swarm+Obs database) presented in the recently submitted AGU manuscript "Electrical conductivity of mantle transition zone and water content revealed by the magnetic data of China Seismo-electromagnetic Satellite".</p>
Dataset for the manuscript "Osmotic Energy Conversion in Serpentinite-Hosted Deep-Sea Hydrothermal Vents"
Open the record for dataset details and reuse information.
Dataset underlying the manuscript: Universal control of four singlet-triplet qubits
<p>Original dataset for the paper "Universal control of four singlet-triplet qubits" by Xin Zhang et al (https://arxiv.org/abs/2312.16101). </p>
MS data linked to manuscript: https://doi.org/10.1111/1751-7915.14215
Open the record for dataset details and reuse information.
Raw data files for the "The Mechanism of Rapid and Green Metal–Organic Framework Synthesis by In Situ Spectroscopy and Diffraction" manuscript
<p>All files are organized by figures in the main text of the original publication: https://doi.org/10.1021/acs.chemmater.4c00879, in the form of .csv files.</p>
Data used in manuscript "High-resolution geophysical monitoring of moisture accumulation preceding slope movement – a path to improved early warning"
<p>Data used in the study titled "High-resolution geophysical monitoring of moisture accumulation preceding slope movement – a path to improved early warning" published in Environmental Research Letters</p>
Final data used in JAMES manuscript 2018MS001305
<p>Final processed data used to generate all figures in the submitted manuscript (2018MS001305) to Journal of Advances in Modeling Earth Systems.</p> <p>Most data are in self descriptive netCDF formats.</p> <ul> <li>OBS directory contains data from observations.</li> <li>WRF directory contains data from model simulations. <ul> <li>WRF/LGdm.MORR2011 contains data from Morrison simulations.</li> <li>WRF/LGdm.THOM2011 contains data from Thompson simulations.</li> </ul> </li> </ul>
Coding data of manuscript "How Do Developers Utilize Source Code from Stack Overflow?"
<p>This is the coding data for the manuscript "How Do Developers Utilize Source Code from Stack Overflow?".</p>
Experimental data used in the manuscript (Jagiella et al., Cell Systems, 4(2), 2017)
<p>This folder contains raw and processed data used in the manuscript:</p> <p>Jagiella, N. and Rickert, D. and Theis, F. J. and Hasenauer, J. (2017). Parallelization and High-Performance Computing Enables Automated Statistical Inference of Multi-scale Models. Cell Systems, 4(2):194-206.</p>
Active thermography to estimate leaf heat transfer - leaf level data to manuscript
<p>Active thermography is an informative methodology to measure that we adopted to plant sciences to measure leaf heat transfer and derive spatial maps of thermal responsiveness of leaves. This method and its usability is described in a publication in 'Frontiers of Plant Sciences'.</p> <p>Here we publish the data-set of active thermography measurements that were used in this publication. The Mat-lab code that was developed with this publication is also published at Zenodo under the doi 10.5281/zenodo.1195869</p>
Raw data for figures used in manuscript - Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures
<p>Raw Image files used for generating the figures (Fig 2, Fig3, Fig4, Fig5 and Fig6, Supplementary figures, files needed for background subtraction and image processing tutorial (docker image)) in the manuscript - Highly parallel single-molecule identification of proteins in zeptomole-scale mixtures</p> <p>Use command tar xfz[v] *.tar.gz to retain the file structure. </p> <p>Docker image in image processing tutorial works on Linux platforms only.</p> <p>File structure after un-compressing each *.tar.gz is as follows - </p> <p>1. acetylated_background_signalsFiles.tar.gz</p> <p> - Folders for the different experiments with the name expt[1..30]</p> <p> - *SIGNALS.pkl - Pickle file (python encoded) containing the information of the histogram of the peptide step-drops</p> <p> - acetylated_backgroundFiles_list.csv (file formatted for performing iterative_background.py)</p> <p> - README.txt (information on the contents and the use of the files in the directory)</p> <p>2. fig2.tar.gz</p> <p> - fig2A/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig2B/ (contains raw image folders, processed_results and README.txt)</p> <p>2. fig3and4.tar.gz</p> <p> - acPeptide_label-2-5/ (contains raw image folders, processed_results)</p> <p> - bocPeptide_label-2-5/ (contains raw image folders, processed_results)</p> <p> - README.txt</p> <p>3. fig5.tar.gz</p> <p> - fig5A_panel1/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5A_panel2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_A2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_A3/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_B1/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5B_B2/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5C/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig5D/ (contains raw image folders, processed_results and README.txt)</p> <p>5. fig6.tar.gz</p> <p> - fig6B_top/ (contains raw image folders, processed_results and README.txt)</p> <p> - fig6B_bottom/ (contains raw image folders, processed_results and README.txt)</p> <p>6. fig_supplementary08.tar.gz</p> <p> - (contains raw image folders, processed_results and README.txt)</p> <p>7. fig_supplementart12.tar.gz</p> <p> - supplementary_fig14A/ (contains raw image folders, processed_results and README.txt)</p> <p> - supplementary_fig14B/ (contains raw image folders, processed_results and README.txt)</p> <p>8. imageProcessingTutorial.tar.gz</p> <p> - walkthrough_docker_image.tar.xz (contains the docker image with necessary code pre-installed. Includes small example dataset; Works only in linux docker and not macOS)</p> <p> - README.txt (information on the image processing tutorial). </p>
Manuscript dataset - Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children's hospital in Cambodia
<p>This is a dataset associated with submission of the manuscript "Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a children's hospital in Cambodia"</p>
MANGO data part of Lyons et al, 2018 manuscript
<p>These data are obtained from the Mid-latitude All-sky-imaging Network for Geospace Observations (MANGO). MANGO is a 7-camera network observing 630nm airglow across the continental United States. The data uploaded here are from two sites - Hat Creek Observatory in northern California and Capitol Reef Field Station in Utah. They are from four different nights, all of which display large-scale travelling ionospheric disturbances that have origins in the auroral precipitation at high latitudes. The manuscript, which includes these data, outlines the possible causal mechanisms for observing these disturbances by combining them with other observational data.</p>
SHEMAT-Suite output directories for WRR manuscript 2018WR023374
<p>The directories in the compressed archives contain the raw data used in the Water Resources Research publication:</p> <blockquote> <p>Comparing seven variants of the Ensemble Kalman Filter: How many<br> synthetic experiments are needed?</p> </blockquote> <p>The article can be found under</p> <p> <a href="https://doi.org/10.1029/2018WR023374">https://doi.org/10.1029/2018WR023374</a></p> <p><br> <strong>Extracting the data</strong><br> </p> <p>At first, extract the directory of the figure:</p> <pre><code class="language-bash"> tar -xzvf figure_1.tar.gz</code></pre> <p>Move the compressed archives to a directory of the form:</p> <p>1) Tracer:</p> <pre><code class="language-bash"> $HOME/shematOutputDir/wavereal_output/</code></pre> <p>2) Well:</p> <pre><code class="language-bash"> $HOME/shematOutputDir/wavewell_output/</code></pre> <p><br> Then run a command of the following form</p> <pre><code class="language-bash"> tar -xzvf 2010_01_30.tar.gz</code></pre> <p>or, to suppress output and return to the command line immediately</p> <pre><code class="language-bash"> tar -xzf 2010_01_30.tar.gz &</code></pre> <p>Now the output files of 2010_01_30.tar.gz are readable for the Python<br> Scripts.</p> <p><br> <strong>Python Scripts</strong><br> </p> <p>The Python Scripts for generation of the figures in the manuscripts can be found in the Github repository:</p> <pre><code> pyshemkf</code></pre> <p>under</p> <p> <a href="https://doi.org/10.5281/zenodo.1344336">https://doi.org/10.5281/zenodo.1344336</a></p> <p>or for the up-to-date version of pyshemkf:</p> <p> <a href="https://github.com/jjokella/pyshemkf">https://github.com/jjokella/pyshemkf</a></p> <p>Documentation of and links to the scripts can be found under:</p> <p> <a href="https://github.com/jjokella/pyshemkf#manuscript-scripts">https://github.com/jjokella/pyshemkf#manuscript-scripts</a></p> <p> </p>
TGA data for the manuscript "Synergistic fire-retardancy properties of melamine coated ammonium poly(phosphate) in combination with rod-like mineral filler attapulgite for polymer-modified bitumen roofing membranes"
<p>The file "SynergisticFireRetardencyPaper_data" containns the raw data and data tretment used in the manuscript<br> "Synergistic fire-retardancy properties of melamine coated ammonium poly(phosphate) in combination with rod-like mineral filler attapulgite for polymer-modified bitumen roofing membranes" for the the TGA part of the work.<br> The file "TGA_Data_and_Data_treatment.xls" contains the TGA raw data used in the manuscript and the recaluclation to a common temperature scale. The file "TGA_plots_20180611.opju" caontian the contruction of the plots shown in the paper.</p> <p> </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.