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.

8

datasets available to search

ShareScore release 0.9.0

Reset

Dataset results

8 results for “Grain-size”

Learn how ShareScore rates datasets ↗
zenodo44/100

Grain-size data from the loess profiles Ostrau and Gleina in Saxony (Germany)

<p><strong>Grain-size data from the loess profiles Ostrau and Gleina in Saxony (Germany)</strong></p> <p>The samples were taken between 2009 and 2010 in the framework of the DFG project <a href="https://gepris.dfg.de/gepris/projekt/46526743"><em>&quot;Rekonstruktion der Umweltbedingungen des Sp&auml;tpleistoz&auml;ns in Mittelsachsen anhand von L&ouml;ss-Pal&auml;obodensequenze</em>n&quot; (DFG FU 417/7-1 and FA 239/13-1</a>)&nbsp;from the loess records Ostrau and Gleina. Both located in the Saxonian-Loess-Region in Germany. For further details on the project profiles (with further references therein), we refer to Meszner et al. (2011,2013), Kreutzer et al. (2012), Meszner (2015)&nbsp;and Zech et al. (2017).&nbsp;</p> <p>Samples for the data reported here were selected in 2015. 212 samples were taken from the loess profile Ostrau and 269 samples from the loess profile Gleina. Full details on sampling and sample preparation can be found in the Grassl (2016) (unpublished master thesis in Germany, available upon request). The most relevant details are extracted below. &nbsp;&nbsp;</p> <p><strong>Preparation and measurements</strong></p> <p>Sample preparation and measurements were carried out at the GFZ in Potsdam (Germany). Thirty-nine samples from the profile Ostrau were separated into eight equal parts to obtain representative samples. The samples were labeled with &quot;G&quot; for Gleina and &quot;O&quot; for Ostrau. All other samples were sampled without applying this separation method.&nbsp;<br> For samples from the profile Ostrau, the suffix &quot;mT&quot; (with separation) and &quot;oT&quot; (without separation) indicates whether this&nbsp;<br> separation method was used.&nbsp;</p> <p>The samples were treated with HCl (10 %, 12 h to 20 h) and rinsed in the demineralized water. To suspend the samples, NO<sub>3</sub>P0<sub>4</sub>&nbsp;was used on twelve pars of H<sub>2</sub>O<sub>2</sub>.&nbsp;</p> <p>A <em>Retch Laser Scattering Particle Size Distribution Analyzer (HORIBA LA- 950)</em>&nbsp;was used for the grain-size measurements.&nbsp;Details&nbsp;<br> on the settings are reported separately in each file.</p> <p><br> <strong>The data in the repository&nbsp;</strong></p> <p>Grainsize_data.zip&nbsp;This folder contains 4,853 ASCII TXT-files with the raw granulometric data. Filenames are unique timestamps &nbsp;(measurement date and time in the format <em>YYYYMMDDHHMMSS</em>&nbsp;CET). Each file comes with a header with relevant metadata and the measurement data. The metadata also contains the sample name, e.g., <em>O_55_oT</em>&nbsp;reads &quot;O&quot; for Ostrau, &quot;55&quot; sampling depth in cm, and &quot;oT&quot; for &quot;ohne Teiler&quot; (without separator, while &quot;mT&quot;, &quot;mit Teiler&quot; would stand for with separator). For files for the profile Gleina, a &quot;G&quot; is used followed by the sampling depth range (two numbers, e.g., <em>G_380_382</em>) in cm.&nbsp;</p> <p>The files <em>Gleina_depth.txt</em>, <em>Ostrau02_depth.txt</em>, and <em>Ostrau03_depth.txt</em>&nbsp;allow&nbsp;a correlation with the profiles graphs published in Meszner (2015).&nbsp;</p> <p><br> <strong>References</strong></p> <p>Grassl, W., 2016. End-Member-Modellierungsanalyse an hochaufl&ouml;senden Korngr&ouml;&szlig;en der L&ouml;ssprofile Ostrau und Gleina, Lommatzscher Pflege, Sachsen. unpublished Master thesis, TU Dresden.</p> <p>Kreutzer, S., Fuchs, M., Meszner, S., Faust, D., 2012. OSL chronostratigraphy of a loess-palaeosol sequence in Saxony/Germany using quartz of different grain sizes. Quaternary Geochronology 10, 102&ndash;109. doi:10.1016/j.quageo.2012.01.004</p> <p>Meszner, S., Fuchs, M., Faust, D., 2011. Loess-Paleosol-Sequences from the loess area of Saxony (Germany). E &amp; G, Quaternary Science Journal 60, 47&ndash;65.</p> <p>Meszner, S., 2015. Loess from Saxony. A reconstruction of the Late Pleistocene landscape evolution and palaeoenvironment based on loess-palaeosol sequences from Saxony (Germany). Dresden. PhD thesis. TU Dresden.&nbsp;</p> <p>Meszner, S., Kreutzer, S., Fuchs, M., Faust, D., 2013. Late Pleistocene landscape dynamics in Saxony, Germany: &nbsp;Paleoenvironmental reconstruction using loess-paleosol sequences. Quaternary International 296, 95&ndash;107. doi:10.1016/j.quaint.2012.12.040</p> <p>Zech, M., Kreutzer, S., Zech, R., Goslar, T., Meszner, S., McIntyre, C., H&auml;ggi, C., Eglinton, T., Faust, D., Fuchs, M., 2017. Comparative 14C and OSL dating of loess-paleosol sequences to evaluate post-depositional contamination of n-alkane biomarkers. Quaternary Research 87, 180&ndash;189. doi:10.1017/qua.2016.7</p>

opencc-by-4.0Jan 2021View details →
zenodo40/100

ChRM and grain-size data of deep-sea sediments in the Central Philippine Sea

<p>The Philippine Sea is a typical region of eolian dust reposition and is located within the Western Pacific Warm Pool. Here, we use the paleo-magnetic stratigraphy and the grain-size distributions of Quaternary abyssal deposits in the Central Philippine Sea to investigate the factors controlling regional sedimentary and paleoenvironmental changes. Our principal results are as follows: (1) A reliable geochronologic framework for Quaternary sediments in the Central Philippine Sea is established. (2) An eastward expansion of the regional depocenter in the Middle Pleistocene is observed. (3) The mean grain size of the abyssal sediments is 7&ndash;8 &mu;m, and there are only minor differences between the sites. Comparison of the geochronological framework with various paleoenvironmental events during the Mid-Pleistocene Transition shows that sedimentary processes can be correlated to a major transition in global climate which affected regions from the Asian interior to the tropical Pacific, and that changes in aeolian sedimentation are likely the predominant factor responsible. A derived grain-size proxy of the sedimentary dynamics and its comparison with various paleoenvironmental proxies show that the relative contributions are roughly estimated as 23%, 9%, and 68% for aeolian inputs, oceanic circulation, and the tropical Pacific zonal SST gradient, respectively in the studied region. The relative importance of tropical processes in abyssal sedimentary dynamics highlights the possibility of the long-term influence of (sub)mesoscale eddies in the upper ocean, via regional upwelling and unique submarine topography, on the deepest part (&gt;5000 m) of the Central Philippine Sea, from meteorological to geological timescales.</p>

opencc-by-4.0Feb 2022View details →
zenodo40/100

Frictional Properties of Opalinus Clay: Influence of Humidity, Normal Stress and Grain-size on Frictional Stability

<p>We designed frictional experiments to characterize the effect exerted by humidity, grain size and normal stress on frictional behaviour of the Opalinus clay fault gouge. We explored a wide range of normal stresses, ranging from 5 to 70 MPa performing velocity up-steps from 1 to 300 &mu;m/s and slide-hold-slide from 1 to 3000s.&nbsp;Our experiments confirms that the OPA clay is&nbsp;weak, with friction coefficients at steady-state of ~0.35 and ~0.41, for 100% RH and 25% RH experiments, respectively. The&nbsp;OPA clay is&nbsp;velocity strengthening&nbsp;over the entire range of applied normal stress. We observe a direct relationship between frictional parameter&nbsp;<em>(a-b)</em>&nbsp;and slip velocity up to 35 MPa where, from there on,&nbsp;<em>(a-b)</em>&nbsp;parameter seems to be velocity independent. As evidenced by the microstructural analysis, we suggest that this behaviour is due to the progressive transition with normal stress, from strain&nbsp;localization&nbsp;and grain size reduction to&nbsp;distributed deformation&nbsp;on well-developed&nbsp;phyllosilicate networks. The amount of relative&nbsp;humidity&nbsp;does not affect deformation mechanisms (i.e. localized or distributed), whereas decreases fault strength and increases fault stability. We hypothesize that this is due to a&nbsp;possible interplay of OPA clay&nbsp;swelling&nbsp;and lubrication, caused by the&nbsp;weakening of chemical bonds between phyllosilicate foliae.&nbsp; Notably, the initial grain size (&lt; 63 &micro;m or 63 &lt; g.s. &lt; 125 &micro;m) does not affect either the frictional strength or stability, with similar values of dilation upon velocity up-step.&nbsp;Collectively, our mechanical and microstructural observations have allowed us to build a conceptual model that summarizes the main mechanical features of the OPA clay fault gouge. In the context of deep geological repositories (DGR), our results confirm that slow aseismic slip is the most likely slip behaviour for a fault gouge hosted in the OPA clay, with similar mineralogical composition and clay fabric as our samples.&nbsp;Beyond the context of deep geological repositories, this study has also implications for carbon capture and geological storage in the deep subsurface. Indeed, OPA has the characteristics of a low permeability caprock, but faulted, and the integrity of a sealing caprock overlying a storage reservoir can evolve after fault reactivation, potentially generating undesired seismicity and new hydraulic pathways.</p> <p>The data are uploaded are structured as follow:</p> <p>1) A&nbsp;.txt file of the datafile that is recorded from the machine (raw data)</p> <p>2) A&nbsp;file in .txt format containing the elaborated data (data_rp)&nbsp;&nbsp;</p> <p>The data are analyzed using rawPy that can be found at&nbsp;<a href="https://github.com/marcoscuderi/rawPy">https://github.com/marcoscuderi/rawPy</a></p> <p>For any additional information please do not hesitate to contact the corresponding author Nico Bigaroni&nbsp;at nico.bigaroni@uniroma1.it</p> <p>&nbsp;</p>

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

Supporting data tables and Python scripts for the paper: "Multi Grain-Size Total Sediment Load Model Based on the Disequilibrium Length"

<p>This repository contains all the data tables and Python scripts necessary to generate the results presented in Le Minor et al.&nbsp;(2022):&nbsp;&quot;Multi Grain-Size Total Sediment Load Model Based on the Disequilibrium&nbsp;Length&quot;.</p>

opencc-by-4.0Oct 2022View details →
zenodo32/100

Grain-size control on detrital zircon cycloprovenance in the late Paleozoic Paradox and Eagle basins, USA

<p>Detrital zircon U-Pb and grain size data for JGR: Solid Earth: &quot;Grain size control on detrital zircon cycloprovenance in the late Paleozoic Paradox and Eagle basins, USA&quot; by Ryan J. Leary,&nbsp;M. Elliot Smith, and Paul Umhoefer.&nbsp;</p>

opencc-by-4.0May 2020View details →
zenodo32/100

Field Observations (2018; 2021), Grain-Size Measurements, and Componentry of the Cleetwood Eruption of Mount Mazama

<p>This dataset accompanies the paper &quot;Using Eruption Source Parameters and High-Resolution Grain-Size Distributions of the 7.7 ka Cleetwood Eruption of Mount Mazama to Reveal Primary and Secondary Eruptive Processes&quot;.</p>

opencc-by-4.0Jan 2022View details →
zenodo24/100

Field Observations (2018; 2021), Grain-Size Measurements, and Componentry of the Cleetwood Eruption of Mount Mazama

<p>This dataset accompanies the paper &quot;Combining Eruption Source Parameters and High-Resolution Grain-Size Distributions of the 7.7 ka Cleetwood Eruption of Mount Mazama to Reveal Primary and Secondary Eruptive Processes&quot;.</p> <p>Version 2 includes High-resolution&nbsp;grain-size distributions&nbsp;spreadsheet (Cltwd_GSDs.xlsx)</p> <p>Wiejaczka and Giachetti, 2022</p>

opencc-by-3.0-usJan 2022View details →
zenodo24/100

Mixed eolian–longshore sediment transport in the late Paleozoic Arizona shelf and Pedregosa basin, USA: a case study in grain-size analysis of detrital-zircon datasets

<p>Detrital zircon and zircon grain size for &quot;Mixed eolian&ndash;longshore sediment transport in the late Paleozoic Arizona shelf and Pedregosa basin, USA: a case study in grain-size analysis of detrital-zircon datasets&quot; published in JSR.&nbsp;</p>

opencc-by-4.0Sep 2022View 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