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.
236
datasets available to search
ShareScore release 0.9.0
Dataset results
236 results for “Surfactants”
Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 13C-1H solid-state NMR and molecular dynamics simulations"
<p>Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 1H-13C solid-state NMR and molecular dynamics simulations" Langmuir 2014, 30 (2), pp 461–469 http://dx.doi.org/10.1021/la404684r</p> <p>System: 60 wt% C12E5, T=320K</p> <p>Other files available: http://dx.doi.org/10.6084/m9.figshare.861071</p>
Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 13C-1H solid-state NMR and molecular dynamics simulations"
<p>Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 1H-13C solid-state NMR and molecular dynamics simulations" Langmuir 2014, 30 (2), pp 461–469 http://dx.doi.org/10.1021/la404684r</p> <p>System: 60 wt% C12E5, T=298K</p> <p>Other files available: http://dx.doi.org/10.6084/m9.figshare.861071</p>
Data supporting the study "The evolution of surface structure during atmospheric ageing of nano-scale coatings of an organic surfactant aerosol proxy" by Milsom et al.
<p>Reduced neutron reflectometry (NR) data associated with the study "The evolution of surface structure during atmospheric ageing of nano-scale coatings of an organic surfactant aerosol proxy" by Milsom et al.. One folder contains the raw data for fitted parameters obtained from NR curves and supporting figure 3 in the study. The other contains a set of sub-folders which have reduced NR data along with python scripts which were used to create and fit the interfacial model to the data. Fitting bounds for each parameter are found in these scripts. </p>
Data for publication "A unified surface tension model for multi-component salt, organic and surfactant solutions"
<p>This repository contains the data of the publication:</p> <p>Title: "A unified surface tension model for multi-component<br>salt, organic and surfactant solutions"<br>Authors: Judith Kleinheins, Claudia Marcolli, Cari Dutcher, Nadia Shardt<br>Date: 2024</p>
Dataset for manuscript entitled "The effects of a synthetic and biological surfactant on the community composition and metabolic activity of a freshwater biofilm"
<p>The following datasets were used for the 16s rRNA analysis in the manuscript entitled " The effects of a synthetic and biological surfactant on the community composition and metabolic activity of a freshwater biofilm". BZ2 files were obtained from next generation sequencing with the Illumina Mi-Seq. Mothur was used to analyze the BZ2 files, creating the listed excel documents.</p>
Dataset for the Manuscript: Surfactants Control Optical Trapping Near a Glass Wall
<p>This repository includes datasets supporting our manuscript that will be transferred to <em>the Journal of Physical Chemistry C</em>. This Version 2 includes extensive new results conducted during the revision process. </p> <ul> <li><strong>'Videos.zip'</strong>: Recordings of trapped particles, estimated trajectories, and calculated MSDs<strong>.</strong></li> <li><strong>'Dynamic Light Scattering.zip'</strong>: Measurement data using dynamic light scattering (DLS). It includes the conductivity, zeta potentials, and hydrodynamic size measurements.</li> <li><strong>'Raw data manual.html'</strong>: A data manual explaining the data details and visualizing the results.</li> </ul> <p>Notes:</p> <p>For finding particle trajectories, we used a Python package, <a href="http://soft-matter.github.io/trackpy/v0.4.2/index.html">Trackpy</a>, developed by Allan et al. We simply followed <a href="http://soft-matter.github.io/trackpy/v0.4.2/tutorial/walkthrough.html">their walkthrough</a> to find particle locations in a video recording and link them to a particle trajectory. The details of our trajectory outputs (_traj.csv files) can be found in <a href="http://soft-matter.github.io/trackpy/v0.4.2/generated/trackpy.locate.html#trackpy.locate">their API reference</a>. </p>
Progeny Project DFT and DFTB data of MW9 and MW10 surfactant molecules
<p>DFT and DFTB (see dftb.org) data for the MW9 and MW10 PDI based surfactant molecules of the Progeny project.</p> <p>See README.txt in each subdirectory for more details.</p>
Progeny Project Gromacs MD Simulations of the MW8 surfactant
<p>"MW8" thiophene based surfactant molecule at the water-vacuum interface, data from the PROGENY project ,<br> gromacs 2021.2 input and output.<br> From the trajectories contained in this archive density profiles and surface tensions can be extracted.</p> <p>Different number of molecules per surface in each sub directory. See README.txt.</p>
Progeny Project MW1 surfactant at water-vacuum interface simulation data, 1 to 48 molecules per surface
<p>"MW1" terthiophene based surfactant molecule at the water-vacuum interface from the PROGENY project ,<br> gromacs 2021.2 input and output.<br> From the trajectories contained in this archive density profiles and surface tensions can be extracted.</p> <p>Different number of molecules per surface in each sub directory. See Readme.txt.</p>
Progeny Project MW1 surfactant at water-vacuum interface simulation data, 60 to 154 molecules per surface
<p>"MW1" terthiophene based surfactant molecule at the water-vacuum interface from the PROGENY project ,<br> gromacs 2021.2 input and output.<br> From the trajectories contained in this archive density profiles and surface tensions can be extracted.</p> <p>Different number of molecules per surface in each sub directory. See Readme.txt.</p>
Progeny Project MW1 surfactant at water-vacuum interface simulation data, 161 to 175 molecules per surface
<p>"MW1" terthiophene based surfactant molecule at the water-vacuum interface from the PROGENY project ,<br> gromacs 2021.2 input and output.<br> From the trajectories contained in this archive density profiles and surface tensions can be extracted.</p> <p>Different number of molecules per surface in each sub directory. See Readme.txt.</p>
Progeny Project Gromacs input and trajectories of a C12E6 surfactant model at the vacuum-water interface
<p>This is gromacs 2021.2 input and output for an all-atom C12E6 surfactant molecule at the water-vacuum<br> interface with TIP4P-ew and SPC/E water models.</p>
Progeny Project DFT data for MW1, MW2 and MW8 surfactant molecules
<p>DFT data, i.e. geometries, electronic eigenvalues, wave function "molden format" data and RESP charges</p> <p>for the MW1, MW2 and MW8 surfactants of the Progeny Project.</p> <p>See Readme.txt for more details.</p>
Surfactant Proteins SP-B and SP-C in Pulmonary Surfactant Monolayers: Physical Properties Controlled by Specific Protein–Lipid Interactions - Simulation dataset
<p>Simulation data related to the article:</p> <p>Liekkinen, J., Olzynska, A., Cwiklik, L., Bernardino de la Serna, J., Vattulainen, I., & Javanainen, M. Surfactant Proteins SP-B and SP-C in Pulmonary Surfactant Monolayers: Physical Properties Controlled by Specific Protein–Lipid Interactions. <em>bioRxiv</em>, pp.2022-12. (Pre-print available at https://doi.org/10.1101/2022.12.12.520108)</p> <p> </p> <p>DPPC/POPC/POPG/CHL1 (60/20/10/10 mol-%) monolayers simulated in non-equilibrium conditions. The monolayer is compressed or expanded at a constant rate at different temperatures. Surfactant proteins SP-B and SP-C are included in the monolayers for comparison. The Charmm36 lipid model [1] is used together with the 4-point OPC water model [2]. Each system consist of two monolayers with 169 lipids each. The monolayers are separated by a water slab, and surrounded by vacuum.</p> <p>All trajectories are simulated for 5000 ns with Gromacs 5.1.x [3] using the default Charmm36 monolayer simulations parameters given in the mdp file. Topology (.top) and index (.ndx) files are also included. The topologies (.itp) for the lipids can be obtained from Charmm-GUI and for the OPC water model from https://bioinformatics.cs.vt.edu/~izadi/</p> <p>[1] DOI: 10.1021/jp101759q</p> <p>[2] DOI: 10.1021/jz501780a</p> <p>[3] DOI: 10.1016/j.softx.2015.06.001</p>
Data Set Accompanying "Free Energy Decompositions Illuminate Synergistic Effects in Interfacial Binding Thermodynamics of Mixed Surfactant Systems"
<p>This data set accompanies "Free Energy Decompositions Illuminate Synergistic Effects in Interfacial Binding<br> Thermodynamics of Mixed Surfactant Systems" by Colin K. Egan and Ali Hassanali. It includes example GROMACS<br> input files for all simulations analyzed in the paper, as well as example data sets and analysis scripts. See<br> https://doi.org/10.26434/chemrxiv-2023-h11k5 for the preprint manuscript.</p>
Chemical upcycling of polyethylene, polypropylene, and mixtures to high-value surfactants
<p>Conversion of plastic wastes to fatty acids is an attractive means to supplement the sourcing of these high-value, high-volume chemicals. Herein, we report a method for transforming polyethylene (PE) and polypropylene (PP) at ~80% conversion to fatty acids with number average molar masses up to ~700 Da and 670 Da, respectively. The process is applicable to municipal PE and PP wastes and their mixtures. Temperature-gradient thermolysis is the key to controllably degrading PE and PP into waxes and inhibiting producing small molecules. The waxes are upcycled to fatty acids by oxidation over manganese stearate and subsequent saponification. PP ꞵ-scission produces more olefin wax and yields higher acid-number fatty acids than PE. We further convert the fatty acids to high-value, large-market-volume surfactants. Industrial-scale technoeconomic analysis suggests economic viability without subsidies.</p>
Dataset of experiment measurements of plastic particle settling induced by clay. and surfactants
<p>Dataset giving experiment input parameters (clay type, surfactant, densities, added mass, etc), measured mass of particles at the surface and bottom, and calculated percent of total mass recovered with breakdown into percent of input plastic mass associated with floating and settled partlcles.</p>
Premature Newborns Treated With Less Invasive Surfactant Administration Under Heated Humidified High-flow
ClinicalTrials.gov study NCT06398691. IPD Sharing: YES. Countries: 1. Publications: 3.
Trial of Late Surfactant to Prevent BPD: A Pilot Study in Ventilated Preterm Neonates Receiving Inhaled Nitric Oxide
ClinicalTrials.gov study NCT00569530. IPD Sharing: NO. Countries: 1. Publications: 4.
Trial of Late Surfactant for Prevention of Bronchopulmonary Dysplasia
ClinicalTrials.gov study NCT01022580. IPD Sharing: Not stated. Countries: 1. Publications: 7.
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.