Skip to main content
Powered by ShareScore

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.

430

datasets available to search

ShareScore release 0.9.0

Reset

Dataset results

430 results for “Hydration”

Learn how ShareScore rates datasets ↗
zenodo36/100

MD simulation trajectory of a fully hydrated DPPG bilayer @298K: SLIPIDS, Gromacs 5.0.4. 2017.

<p>MD simulation trajectory files, for fully hydrated DPPG bilayer [288 DPPG, 11232 WAT and 288 NA ions]. The SLIPIDS force field was used with Gromacs 5.0.4. Conditions: T=298K. 400 ns trajectory, last 100 ns uploaded for analysis.</p>

opencc-by-4.0Apr 2017View details →
zenodo36/100

MD simulation trajectory of a fully hydrated POPG bilayer: SLIPIDS, Gromacs 5.0.4. 2017.

<p>MD simulation trajectory files, for fully hydrated POPG bilayer [288 POPG, 10664 WAT and 288 NA ions]. The SLIPIDS force field was used with Gromacs 5.0.4. Conditions: T=298K. 250 ns trajectory, last 100 ns uploaded for analysis.</p>

opencc-by-4.0Apr 2017View details →
zenodo36/100

MD simulation trajectory of a fully hydrated DPPE bilayer: SLIPIDS, Gromacs 5.0.4. 2017.

<p>MD simulation trajectory files, for fully hydrated DPPE bilayer [288 DPPE, 9386 WAT]. The SLIPIDS force field was used with Gromacs 5.0.4. Conditions: T=336K. 200 ns trajectory, last 100 ns uploaded for analysis.</p>

opencc-by-4.0Apr 2017View details →
zenodo36/100

MD simulation trajectory of a fully hydrated DPPG bilayer @314K: SLIPIDS, Gromacs 5.0.4. 2017.

<p>MD simulation trajectory files, for fully hydrated DPPG bilayer [288 DPPG, 11232 WAT and 288 NA ions]. The SLIPIDS force field was used with Gromacs 5.0.4. Conditions: T=314K. 200 ns trajectory, last 100 ns uploaded for analysis.</p>

opencc-by-4.0Apr 2017View details →
zenodo36/100

MD simulation trajectory of a fully hydrated DOPS bilayer: SLIPIDS, Gromacs 5.0.4. 2017.

<p>MD simulation trajectory files, for fully hydrated DOPS bilayer [288 DOPS, 11232 WAT and 288 NA ions]. The SLIPIDS force field was used with Gromacs 5.0.4. Conditions: T=303K. 200 ns trajectory, last 100 ns uploaded for analysis.</p>

opencc-by-4.0Apr 2017View details →
zenodo36/100

Dataset for "Mix and measure - combining in situ X-ray powder diffraction and microtomography for accurate hydrating cement studies" paper

<p>Dataset for paper (doi: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.cemconres.2023.107370" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.cemconres.2023.107370</a>) with the abstract: "It is reported an innovative methodology based on in situ MoKa1 laboratory X-ray powder diffraction (LXRPD) and microtomography (&mu;CT) avoiding any sample conditioning. The pastes are injected in 2.0 mm capillaries and the extremes are just sealed. The measurements take place in the same region of the hydrating paste. Thick capillaries are key to avoiding self-desiccation, which dictates the need of high-energy X-ray radiation for the diffraction study. This approach has been tested with a PC 42.5 R paste having w/c=0.50. &mu;CT data were collected at 12 hours and 1, 3, 7 and 79 days. LXRPD data were acquired at 1, 3, 7 and 77 days. In this proof-of-principle research, the same paste was also cured ex situ. Portlandite contents obtained by thermal analysis, ex situ powder diffraction, in situ mass balance calculation and in situ powder diffraction were 13.8, 13.1, 13.1 and 12.5 wt%, respectively. From the &mu;CT study, the grey value histogram evolution with time showed a crossing point which allowed us to distinguish (appearing) hydrated products from (dissolving) unhydrated cement particles. Segmentations were carried out by global thresholding and the random forest approach (one type of supervised Machine Learning). The comparison of the segmented results for the unhydrated cement fraction and the Rietveld quantitative phase analysis outputs gave an agreement of 2%. The potential of this methodology to deal with more complex binders is also presented."</p>

opencc-by-4.0Jun 2023View details →
dryad36/100

Diffusely charged polymeric zwitterions as loosely hydrated marine antifouling coatings

<p>This dataset accompanies the article "Diffusely Charged Polymeric Zwitterions as Loosely Hydrated Marine Antifouling Coatings" by Shawn D. Mengel, Wen Guo, Guangyao Wu, John A. Finlay, Peter Allen, Anthony S. Clare, Riddhiman Medhi, Zhan Chen, Christopher K. Ober, and Rachel A. Segalman in Langmuir in 2023 (LINK). The article demonstrates that more diffusely charged polymeric zwitterions have improved surface hydration, which correlates with improved fouling resistance to <em>Ulva linza</em>. This dataset contains the necessary experimental data to reproduce the main text and supporting figures. Nuclear magnetic resonance spectra, vibrational sum frequency generation spectra, marine fouling assays, and contact angle measurements are included.</p>

opencc-zeroJan 2024View details →
dryad36/100

Data from: Production of low-carbon amorphous precipitated silica and hydrated magnesium carbonate from olivine

<p>A novel process is reported that produces amorphous silica and nesquehonite (MgCO<sub>3</sub>.3H<sub>2</sub>O) from the magnesium silicate mineral olivine ((Mg,Fe)<sub>2</sub>.SiO<sub>4</sub>). The amorphous silica formed is a supplementary cementitious material (SCM) for use in concrete. The formation of nesquehonite sequesters carbon and means that the overall process is carbon negative. Nesquehonite can also be used to form low-carbon construction products such as bricks, blocks, and boards. This paper reports on key process optimisation studies. The potential for amorphous precipitated silica derived from olivine to produce carbon-negative concrete is discussed.</p>

opencc-zeroFeb 2024View details →
zenodo36/100

Normalized water permeability of hydrate-bearing sediments

<p>This dataset include some normalized water permeability, hydrate saturation, porosity and the water saturation of hydrate-bearing sediments&nbsp;</p>

opencc-by-4.0Mar 2024View details →
zenodo36/100

Data from: Two stressors are worse than one: combined heatwave and drought affect hydration state and glucocorticoid levels in a temperate ectotherm

<p>Data sets from the paper:&nbsp;<a href="https://doi.org/10.1242/jeb.243777">https://doi.org/10.1242/jeb.243777</a>&nbsp;&quot;Two stressors are worse than one: combined heatwave and drought affect hydration state and glucocorticoid levels in a temperate ectotherm&quot; by Dezetter et al. 2022 ; Journal of Experimental Biology</p> <p>&nbsp;</p>

opencc-by-4.0Feb 2022View details →
zenodo36/100

Data for "Sea-level stability over geologic time owing to limited deep subduction of hydrated mantle"

<p>This repository contains data to reproduce the results displayed in the manuscript : &quot;Sea-level stability over geologic time owing to limited deep subduction of hydrated mantle&quot; by Cerpa, N. G., Arcay, D., &amp; Padron-Navarta J. A.<br> The data includes the P-T paths and the slab-water retention for the 56 modeled subduction transects. &nbsp;</p>

opencc-by-4.0Mar 2021View details →
zenodo36/100

Investigation of the Hydration Behavior of Different Sugars by Time Domain‐NMR

<p>Tables containing characterization data of hydration behavior of different sugars by various complementary methods (Spin-Spin Relaxation Times (T<sub>2</sub>), Self-Diffusion Coefficient and&nbsp;Use of Magic Sandwich Echo (MSE) Sequence for Crystallinity Measurements)</p>

opencc-by-4.0Apr 2022View details →
zenodo36/100

Dataset for "Impact of C-S-H seeding on hydration and strength of slag blended cement"

<p>The data come along with the publication &quot;Impact of C-S-H seeding on hydration and strength of slag blended cement&quot; submitted to Cement and Concrete Research. 2022</p> <p>The file 00_mixdesign.csv contains the indexing and the labels, the same as Table 5 from the manuscript.</p> <p>Units of the values are not always specified in the dataset, but can be found in the manuscript.</p> <p>To understand the structure and labels of the dataset, please refer to the paper.&nbsp;</p> <p>April. 2022<br> Xuerun Li, Julien Bizzozero and Christoph Hesse<br> BASF Construction Additives GmbH, Dr-Albert-Frank-Strasse 32, 83308 Trostberg, Germany</p>

opencc-by-4.0Apr 2022View details →
zenodo36/100

Jang and Santamarian Hydrate-bearing Sediments and Frozen Ground

<p>Hydrate-bearing Sediments and Frozen Ground</p> <p>Data availability</p>

opencc-by-4.0Jul 2022View details →
zenodo36/100

Supplemental information data from: "Evidence for amorphous sulfates as the main carrier of soil hydration in Gale crater, Mars"

<p>This dataset includes the target name and chemical composition of each ChemCam sequence used in the&nbsp;above-mentioned article. The quantification for each ChemCam spectrum in the soils of the Bradbury, Rocknest and Yellowknife Bay area are in percentage mass fractions (wt.%) for most major oxides (i.e., SiO2, TiO2, Al2O3, FeOT , MgO, CaO, Na2O, K2O). Sulfur and hydrogen abundances are expressed respectively in peak area (normalized) and ICA H scores.</p>

opencc-by-4.0Aug 2022View details →
zenodo36/100

The coupling of the hydrated proton to its first solvation shell

<p>This repository contains tabulated raw data for figures 1, 2, and 4 for the manuscript entitled &quot;The coupling of the hydrated proton to its first solvation shell&quot;.&nbsp; Also, all necessary inputs and instructions to reproduce the data are provided.</p>

opencc-by-4.0Sep 2022View details →
zenodo36/100

Raw experimental data for hydrated arsenic(III)-oxide intercalation compound with ammonium chloride including raw diffraction data for CSD 2202825

<p>pg171_WW4_NH4ClAs2O3H2O.7z: raw diffraction data for CSD 2202825</p> <p>PXRD.7z: raw powder X-ray diffraction pattern for a sample containing intercalate <strong>Y<sub>NH<sub>4</sub>Cl</sub></strong> and its dehydrated counterpart</p> <p>WW9_TG-MS_DSC.7z: raw TG-MS and DSC data for intercalate <strong>Y<sub>NH<sub>4</sub>Cl</sub></strong></p> <p>15NssNMR.7z: raw ssNMR data for intercalates <strong>Y<sub>NH<sub>4</sub>Cl</sub></strong>, <strong>P<sub>NH<sub>4</sub>Br</sub></strong> and P<strong><sub>NH<sub>4</sub>I</sub></strong></p> <p>&nbsp;</p>

opencc-by-4.0Aug 2022View details →
zenodo36/100

Rietveld quantitative phase analysis of Oil Well Cement: in situ hydration study at 150 bars and 150ºC

<p>Raw data for:&nbsp;Rietveld Quantitative Phase Analysis of Oil Well Cement: In Situ Hydration Study at 150 Bars and 150 &deg;C;</p> <p>doi:&nbsp;<a href="https://doi.org/10.3390/ma12121897">https://doi.org/10.3390/ma12121897</a></p> <p>Oil well cements are multimineral materials that hydrate under high pressure and temperature. Its overall reactivity at early ages is studied by a number of techniques including the consistometer. However, for a proper understanding of the performances of these cements in the field, the reactivity of every component, at the field conditions, must be analysed. So far, <em>in situ</em> high energy synchrotron powder diffraction studies of hydrating oil well cement pastes have been carried out but the quality of the data was not appropriated for Rietveld quantitative phase analyses. Therefore, the phase reactivities were followed by the inspection of the evolution of non-overlapped diffraction peaks. Very recently, we have developed a new cell specially designed to rotate under high pressure and temperature. Here, this spinning capillary cell is used to <em>in situ</em> study the hydration of a commercial oil well cement paste at 150 bars and 150 &ordm;C. The powder diffraction data have been analysed by the Rietveld method to quantitatively determine the reactivities of each component phase. The reaction degree of alite was 90% after 7 hours and that of belite was 42% at 14 hours. These analyses are accurate as the <em>in situ</em> measured crystalline portlandite content at the end of the experiment, 12.9 wt%, compares relatively well with the value determined <em>ex situ</em> by thermal analysis, 14.0 wt%. The crystalline calcium silicates forming at 150 bars and 150 &ordm;C are also discussed.</p>

opencc-by-4.0Apr 2019View details →
zenodo36/100

Supplementary material for publication "Pore-scale salinity effects on methane hydrate dissociation" by Almenningen et al

<p>Supplementary materials for publication &quot;Pore-scale salinity effects on methane hydrate dissociation&quot;</p> <p>Abstract:&nbsp;Sedimentary methane gas hydrates may become a significant source of methane gas in the global energy mix for the next decades. The widespread distribution of methane hydrates, primarily in subsea sediments on continental margins, makes the crystalline compound attractive for countries with shorelines that seek self-sustainable energy. Fundamental understanding of pore-level methane hydrate distribution and dissociation pattern is important to anticipate the gas production from hydrate reservoirs. Especially the effect of local salinity gradients on dissociation characteristics must be understood as the aqueous phase in most reservoirs is saline. We evaluate the pore-level salinity effect on hydrate dissociation experimentally using silicon-wafer micro-models capable of withstanding high internal pressures. Methane hydrates were formed with brines for a range of salinities (0.0, 2.0, 3.5 and 5.0 wt% NaCl), and we study hydrate dissociation during both depressurization and thermal stimulation, which currently are the most cost-effective production methods. The laboratory results show how initial pore-scale hydrate distribution prior to dissociation affect the melting and mobilization of gas. The local pore-water salinities influenced the stability of the hydrate structure, and led to distinct dissociation patterns due to water freshening.</p>

opencc-by-4.0Apr 2018View details →
zenodo36/100

CT image volume, gray values and pore structure parameters of hydrate formation in sands

<p>The dataset consists of CT raw volume and gray values of hydrate formation (hydrate saturation 0, 14%, 35% and 51%), methane hydrate growth pattern, and the pore structure parameters. The dataset associated with the manuscript entitled &quot;&nbsp;Investigation on the multi-parameter of hydrate-bearing sands using nano-focus X-ray computed tomography&quot; .</p> <p>&nbsp;</p>

opencc-by-4.0Jul 2018View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record