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.
5
datasets available to search
ShareScore release 0.9.0
Dataset results
5 results for “Spin Coating”
The top performer: towards optimized parameters for Reduced graphene oxide uniformity by Spin coating
<p>This dataset contains the raw data used for the publication:</p> <p>-------------------------------------------------------------------------------------------------------------------------------------------------------<br> "The top performer: towards optimized parameters for Reduced graphene oxide uniformity by Spin coating"<br> by C. Reiner-Rozman, R. Hasler, J. Andersson, T. Rodrigues, A. Bozdogan and P. Aspermair<br> --------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><br> It consists of the SEM images (in .tif format) and the determined surface coverages (in .dat format) as well as the measured electrical data (in .dat format) of the prepared graphene field-effect transistor chips. Headers/information in the data files are in English. When using this data in any form please refer to the above-mentioned publication.</p> <p>The data is structured according to the figures of the paper. Each folder contains the data relevant to validate the results presented in the respective figure of the publication. The files are labeled according to the following description:</p> <p>"measurement-type"_"chip-number"_"GO-concentration"_"spin-coating speed"</p> <p>"measurement-type": SEM, IDVG, baseline<br> "chip-number": an increasing number of fabricated device (only used when needed)<br> "GO-concentration": 143/214/285 µg/mL of graphene oxide (GO) in solution<br> "spin-coating speed": in rpm</p>
Two-step spin-coating of vacancy-ordered double perovskites enables growth of thin films for electronic devices
Open the record for dataset details and reuse information.
Pole Figures for Response to Technical Comment on "Spin Coating Epitaxial Films"
<p>Pole figures for spin coated films. In each figure <strong>(A)</strong> is the data as reported in <em>Science</em> <strong>364</strong>, 166-169 (2019), and <strong>(</strong><strong>B)</strong> is the data that is re-scaled with fewer contours so as to minimize that background intensity that is due primarily to the substrate.</p>
The spin-coating process of the control (top) and ZW-treated (bottom) 2D perovskite films.
<p>The spin-coating process of the control (top) and ZW-treated (bottom) 2D perovskite films.</p>
Semifluorinated Polymer Membranes by Ring-Opening Metathesis Polymerization during Spin Coating
<p>Date: 18 November, 2024</p> <p> </p> <p>Dataset Title: Semifluorinated Polymer Membranes by Ring-Opening Metathesis Polymerization during Spin Coating</p> <p> </p> <p> </p> <p>Dataset Creators: Arun Srikanth Sridhar</p> <p> </p> <p>Dataset Contact:</p> <p>Arun Srikanth Sridhar: askforarun@gmail.com</p> <p>Prof. Clare McCabe: C.MCCABE@hw.ac.uk</p> <p> </p> <p> </p> <p>Funding: Division of Materials Research (Award #2119575) , Graduate Research Fellowship Program, HWU high performance computing facility (DMOG)</p> <p> </p> <p> </p> <p>Key Points:</p> <p>- Molecular simulations and experiments agree that fluorocarbon side chains align parallel to the surface in the bulk but normal to the surface at the interface. </p> <p>- Molecular simulations show preferential segregations of CF<sub>3</sub> groups over CF<sub>2</sub> and CH<sub>2</sub> groups.</p> <p>- Fractional free volume increases upon flourination</p> <p> </p> <p> </p> <p>Research Overview:</p> <p> </p> <p>Molecular dynamics (MD) simulations were utilised to validate the theoretical and semi-empirical approaches employed and provide molecular level insight to the experientially observed behavior.</p> <p> </p> <p> </p> <p>Methodology:</p> <p>All simulations were conducted in NPT, NVT ensembles using GROMACS 2023.2. MDanalysis, mdtraj, and gromacs utility functions were used for postprocessing atomic trajectories.</p> <p> </p> <p>Files contained here:</p> <p> </p> <p>The PNBFN_SI folder contains two subfolders, PNBFN_signac and thickfilm_signac, a python file, analysis.py and a Jupyter notebook file, final_results.ipynb.</p> <p> </p> <p>Inside the PNBFN_signac folder you will find workspace folder. Each folder inside workspace folder corresponds to a specific polymer system. The information regarding the system is contained in signac_statepoint.json file. </p> <p> </p> <p>Inside any folder within the workspace folder of PNBFN_signac you will find</p> <p>1) all the necessary GROMACS files to reproduce the simulations of bulk polymer systems.</p> <p>2) The bash scripts used to run the simulations (for example 21stepbulk.sh is the script used to create bulk polymer systems ) in the cluster.</p> <p>3) The free volume folders, freevol_300_0_2.8, freevol_300_500000_2.8, freevol_300_100000_2.8 contain the necessary files to reproduce the calculations carried out using pore blazer. Here, 300 refers to the temperature, the second item (0,500000,100000) refers to the time stamp (in ps), the third item 2.8 refers to the probe diameter.</p> <p>4) log files.</p> <p> </p> <p>Inside the thickfilm_signac folder you will find workspace folder. Each folder inside workspace folder corresponds to a specific polymer film system.</p> <p> </p> <p>Inside any folder within the workspace of thickfilm_signac folder you will find</p> <p>1) all the necessary GROMACS files to reproduce the simulations of polymer film systems.</p> <p>2) The bash scripts used to run the simulations (for example 21stepfilm.sh is the script used to create polymer film systems) in the cluster.</p> <p>3) log files</p> <p> </p> <p>analysis.py in PNBFN_signac contains the python codes for running the simulations (generating the bashscripts) and the codes used in post processing.</p> <p> </p> <p>The final_results.ipynb calls the functions in analysis.py and contains the codes for generating the figures in the manuscript. Each code block in final_results.ipynb corresponds to a figure in the manuscript. The code blocks are commented for clarity.</p> <p>Use and Access:</p> <p>To use the signac framework of this project, MOSDEF suite (https://mosdef.org), SIGNAC (<a href="https://signac.io/">https://signac.io</a>) and other python packages need to be installed. These python packages can be found in analysis.py and final_results.ipynb.</p> <p>The trajectories require large disk space and are not provided but the simulations can be easily extended/reproduced using the following commands. Execute these commands within any folder inside workspace to extend the simulations by 1000 ps.</p> <p>gmx convert-tpr -s X.tpr -extend 1000 -o next.tpr</p> <p>gmx mdrun -s next.tpr -cpi X.cpt -noappend </p> <p>where X.tpr is the tpr file (topology file) and X.cpt is the check point file</p> <p> </p> <p>The full signac framework will be made available upon request.</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.