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3,720 results for “Concentration”
Adsorption kinetics data sets, compiled from the literature. As used in the research article "A revised pseudo-second order kinetic model for adsorption, sensitive to changes in adsorbate and adsorbent concentrations"
<p>Data sets reporting experimental adsorption kinetics, compiled from the literature. These data sets were subjected to empirical analysis in the development of our revised pseudo-second order rate equation (the rPSO model) as discussed in the ChemRxiv pre-print "<a href="https://chemrxiv.org/articles/preprint/A_Revised_Pseudo-Second_Order_Kinetic_Model_for_Adsorption_Sensitive_to_Changes_in_Sorbate_and_Sorbent_Concentrations/12008799">A Revised Pseudo-Second Order Kinetic Model for Adsorption, Sensitive to Changes in Sorbate and Sorbent Concentrations</a>".</p>
Dataset: Submicron‐ and Nanoplastic Detection at Low Micro‐ to Nanogram Concentrations Using Gold Nanostar‐Based Surface‐Enhanced Raman Scattering (SERS) Substrates
<p>ABSTRACT</p> <p>The presence of submicron- (1 µm – 100 nm) and nanoplastic (< 100 nm) particles within various sample matrices, ranging from marine environments to foods and beverages, has become a topic of increasing interest in recent years. Despite this interest, very few analytical techniques remain that allow for the detection of these small plastic particles in the low concentration ranges that they are anticipated to be present at. Research focused on optimizing surface-enhanced Raman scattering (SERS) to enhance signal obtained in Raman spectroscopy has been shown to have great potential for the detection of plastic particles below conventional resolution limits. In this study, we produce SERS substrates composed of gold nanostars and assess their potential for submicron- and nanoplastic detection. The results show 33 nm polystyrene could be detected down to 1.25 µg/mL while 36 nm poly(ethylene terephthalate) was detected down to 5 µg/mL. These results confirm the promising potential of the gold nanostar-based SERS substrates for nanoplastic detection. Furthermore, combined with findings for 121 nm polypropylene and 126 nm polyethylene particles, they highlight potential differences in analytical performance that depend on the properties of the plastics being studied.</p>
Artificial Neural Networks-generated Dataset: pH, Total Alkalinity, and Hydrogen Ion Concentration in Ría de Vigo (NW Spain), 1995–2020
<p>This dataset comprises input data from INTECMAR and the predicted outcomes. The variables and their units are as follows:</p> <p>station: 'Station ID [1-6]'</p> <p>year: 'Year [1995-2020]'</p> <p>month: 'Month [1-12]'</p> <p>day: 'Day'</p> <p>latitude: 'Latitude (decimal degrees)'</p> <p>longitude: 'Longitude (decimal degrees)'</p> <p>depth: 'Depth (meters)'</p> <p>temperature: 'Temperature (degrees Celsius)'</p> <p>salinity: 'Salinity (psu)'</p> <p>phosphate: 'Phosphate (umol/kg)'</p> <p>nitrate: 'Nitrate (umol/kg)'</p> <p>silicate: 'Silicate (umol/kg)'</p> <p>cweek: 'Cosine week'</p> <p>sweek: 'Sine week'</p> <p>TA: 'Total Alkalinity predicted (umol/kg)'</p> <p>NTA: 'Normalized Total Alkalinity (umol/kg)'</p> <p>NAT_st: 'Normalized per station Total Alkalinity (umol/kg)'</p> <p>NTA_gl: 'Normalized globally Total Alkalinity (umol/kg)'</p> <p>pHTS_insitu: 'pH insitu (pH units)'</p> <p>HT: 'Hydrogen ion concentration predicted (nmol/kg)'</p> <p> </p> <p>The authors gratefully acknowledge the financial support by the Programa de axudas á etapa predoutoral da Xunta de Galicia (Axencia Galega de Innovación) (Grant nº IN606A-2022/025). F.F.P. and A.V. were supported by REDEIRA (TED2021-132188B-I00) project, funded by MCIN/AEI/10.13039/501100011033. The authors also express their gratitude to the Instituto Tecnolóxico para o Control do Medio Mariño de Galicia (INTECMAR), for the analyses and production of the database used to make predictions.</p>
Global Surface Ozone Concentration Dataset 1990-2017 Mapped at Fine Resolution through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output
<p>This global surface ozone concentration dataset corresponds to the data developed in this paper:</p> <p>DeLang, M. N., J. S. Becker, K.-L. Chang, M. L. Serre, O. R. Cooper, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, S. Cleland, E. Collins, M. Brauer, and J. J. West (2021) Mapping yearly fine resolution global surface ozone through the Bayesian Maximum Entropy data fusion of observations and model output for 1990-2017, <em>Environmental Science & Technology</em>, 55, 4389-4398, doi: 10.1021/acs.est.0c07742.</p> <p>Ozone concentrations are estimated as described in the paper, with output shown for the Ozone Season Daily Maximum 8-hr metric (OSDMA8) for each year between 1990 and 2017, at 0.1 degree spatial resolution. Ozone is estimated through data fusion of output from several global models, with observations of ozone collected by TOAR. The data fusion involves application of the M3Fusion method to create a multi-model composite of several global models, followed by BME data fusion, as described in the paper. </p> <p>The *.nc file contains the latitude, longitude, ozone concentration estimate, and estimated variance for each 0.1 x 0.1 degree grid cell.</p> <p>Please contact Jason West (jasonwest@unc.edu) with questions about the dataset. We'd like to hear from you to know how you're using the data!</p> <p> </p> <p> </p>
Influence of cation concentration and valence on the structure and texture of spray-dried supraparticles from colloidal silica dispersions
<p>These datasets display the raw data for the manuscript: Huanhuan Zhou, Philipp Groppe, Thomas Zimmermann, Susanne Wintzheimer, Karl Mandel, Influence of cation concentration and valence on the structure and texture of spray-dried supraparticles from colloidal silica dispersions, Journal of Colloid and Interface Science, Volume 658,<br>2024, Pages 199-208, https://doi.org/10.1016/j.jcis.2023.12.051.</p> <p>The data connection file serves as an explanation for all datasets and their connection to the data displayed in the manuscript.</p>
Code to reproduce the analyses of "Multiple stressors alter greenhouse gas concentrations in streams through local and distal processes"
<p>Streams are significant contributors of greenhouse gases (GHG) to the atmosphere, and the increasing number of stressors degrading freshwaters may exacerbate this process, posing a threat to climatic stability. However, it is unclear whether the influence of multiple stressors on GHG concentrations in streams results from increases of in-situ metabolism (i.e., local processes) or from changes in upstream and terrestrial GHG production (i.e., distal processes). Here, we hypothesize that the mechanisms controlling multiple stressor effects vary between <span>carbon dioxide (</span>CO<sub>2</sub>) and <span>methane (</span>CH<sub>4</sub>), with the latter being more influenced by changes in local stream metabolism, and the former mainly responding to distal processes. To test this hypothesis, we measured stream metabolism and the concentrations of CO<sub>2</sub> (<em>p</em>CO<sub>2</sub>) and CH<sub>4</sub> (<em>p</em>CH<sub>4</sub>) in 50 stream sites that encompass gradients of <span>nutrient enrichment, oxygen depletion, thermal stress, riparian degradation and discharge</span>. Our results indicate that these stressors had additive effects on stream metabolism and GHG concentrations, with stressor interactions explaining limited variance. Nutrient enrichment was associated with higher stream heterotrophy and <em>p</em>CO<sub>2</sub>, whereas <em>p</em>CH<sub>4</sub> increased with oxygen depletion and water temperature. Discharge was positively linked to primary production, respiration and heterotrophy but correlated negatively with <em>p</em>CO<sub>2.</sub> Our models indicate that CO<sub>2</sub>-equivalent concentrations can more than double in streams that experience high nutrient enrichment and oxygen depletion, as compared to those with oligotrophic and oxic conditions. Structural equation models revealed that the effects of nutrient enrichment and discharge on <em>p</em>CO<sub>2</sub> were related to distal processes rather than local metabolism. In contrast, <em>p</em>CH<sub>4</sub> responses to nutrient enrichment, discharge and temperature were related to both local metabolism and distal processes. Collectively, our study illustrates <span>potential climatic feedbacks resulting from freshwater degradation and </span>provides insight into the processes mediating stressor impacts on the production of GHG in streams.</p>
Global Surface Ozone Concentration Dataset 1990-2017 Generated by Bayesian Maximum Entropy Data Fusion With RAMP Bias Correction
<p>This dataset reports estimates of surface ozone concentration at fine spatial resolution for 1990 to 2017, at 0.5 degree horizontal resolution. Also reported is the variance. Estimates correspond to this paper:</p> <p><span>Becker, J. S.</span><span>, DeLang, M. N., K.-L. Chang, M. L. Serre, O. R. Cooper, <u>H. Wang</u>, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, M. Brauer, and <span>J. J. West</span> (2023) Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration, <em>Elementa Science of the Anthropocene</em>, 11: 1, doi: 10.1525/elementa.2022.00025.</span></p> <p>The dataset reports estimates of surface ozone for the OSDMA8 metric (the 6-month ozone-season average of the daily maximum 8-hr concentration), estimated through a data fusion of ozone observations from the Tropospheric Ozone Assessment Report (TOAR) database, and output from multiple global atmospheric models. Estimates are created in each year by a combination of M3Fusion to create a multi-model composite, Regional Air Quality Model Performance (RAMP) regional and nonlinear bias correction, and Bayesian Maximum Entropy (BME) data fusion in space and time. The estimates here are the final results using a weighted RAMP bias correction. </p>
Concentrating solar power (CSP) plants AI-training dataset for flux density measurements.
<p>In this dataset, the tools required for the training of a neural net in the context of flux density measurements in concentrating solar power (CSP) plants are included. An Excel file with 931 meteorological conditions and the positions of the power plant and the receiver is included, as well as 15928 pairs of images resulting from ray-tracing in Solarturm Juelich (STJ) each of these conditions with 17 different combinations of heliostats. <br> <br>This dataset is part of the WP1 of TOPCSP european project (funded by HORIZON MSCA Doctoral Network, Project number 101072537).</p>
Supplementary Data from, "A Mechanistic Model of Annual Sulfate Concentrations in the U.S."
<p>These data are used to perform the analysis contained in, "A Mechanistic Model of Annual Sulfate Concentrations in the United States," by Wikle, Hanks, Henneman, and Zigler. This is purely for archival purposes, to facilitate access and replication of the aforementioned analysis. All data were obtained from the following publicly available sources:</p> <p>1) AMPD Unit Data (U.S. EPA, "Air markets program data," https://ampd.epa.gov/ampd)</p> <p>2) 2010 U.S. Population Density (U.S.G.S., http://dx.doi.org/10.5066/F74J0C6M)</p> <p>3) SO4 Concentrations (Randall Martin Atmospheric Composition Analysis Group's North American Regional Estimates, version V4.NA.02, https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.NA.03)</p> <p>4) North American Regional Reanalysis Meteorological Data (NOAA, https://psl.noaa.gov/data/gridded/data.narr.monolevel.html)</p> <p>Code and supplementary material from this analysis are available at: https://github.com/nbwikle/mechanisticSO4-supp_material</p>
Dataset of plasma non-esterified fatty acid concentrations response of a suckling cow exposed to a feed restriction
<p>The detaset describes the response of a suckler cow in terms of plasma non-esterified fatty-acids (NEFA) concentrations, that was exposed to a feed restriction that consisted in the reduction of net energy requirements by 50%. The dataset has two columns, one for time (t) in days (d) and another column for plasma NEFA concentrations (g·L<sup>-1</sup>). The feed restriction started at t = 1 d and lasted untill t = 4 d. Negative values for t represent the pre-challenge period. </p>
Emissions from building materials - concentration of micropollutants and heavy metals in stormwater runoff of two new development areas in Berlin (Germany)
<p>This dataset includes concentrations of micropollutants (27) and heavy metals (7) for stormwater runoff from different sampling points at two test sites (A and B) in Berlin, Germany. Both sites are new development areas of similar size that were both constructed in 2017 (1 – 1.5 years prior to the start of the monitoring campaign). Composite samples of individual rain events were taken at three sampling points of each test site: façade runoff, roof runoff and corresponding stormwater runoff from the catchment area. Samples were taken as part of the research project BaSaR (<a href="http://www.kompetenz-wasser.de/en/forschung/projekte/basar/">www.kompetenz-wasser.de/en/forschung/projekte/basar/</a>) of Kompetenzzentrum Wasser Berlin, Ostschweizer Fachhochschule and Berliner Wasserbetriebe. More information including sampling and analytical methods are detailed in the corresponding journal paper "Emissions from building materials – a thread for the environment?", submitted to the MDPI-journal <em>Water</em>.</p> <p><strong>Description of fields:</strong></p> <ul> <li><strong>SiteID</strong>: site identifier <ul> <li>A: new development site with typical architecture for multi-storey apartment buildings with plastered and painted facades in northern part of Berlin (124 apartments)</li> <li>B: new development site with typical architecture for multi-storey apartment buildings with plastered and painted facades in southeastern part of Berlin (122 appartments)</li> </ul> </li> <li><strong>SamplingPoint</strong> <ul> <li>facade runoff: runoff from plastered facade collected with gutters during individual rain events</li> <li>roof runoff: roof runoff collected from one downpipe during individual rain events</li> <li>storm sewer: stormwater runoff sampled during individual rain events in a manhole receiving runoff from the entire catchment (A or B)</li> </ul> </li> <li><strong>LocalDateTime_StartRain</strong>: start time of sampled rain event (CET / CEST)</li> <li><strong>LocalDateTime_EndRain</strong>: end time of sampled rain event (CET / CEST)</li> <li><strong>CardinalDirection</strong>: only relevant for facade runoff <ul> <li>N: runoff from facade oriented to the north</li> <li>W: runoff from facade oriented to the west</li> </ul> </li> <li><strong>VariableName</strong>: name of analysed substance/parameter</li> <li><strong>CensorCode</strong>: either "lt" (less than) for concentration below detection limit (value is detection limit) or "nc" (not censored) for concentration above detection limit</li> <li><strong>UnitsAbbreviation</strong>: either "ug/L" (microgram per litre) or "mg/L" (milligram per litre)</li> <li><strong>DataValue</strong>: measured value (if censor code is lt, value indicates detection limit)</li> </ul> <p>One data file is provided in comma separated format:<br> "BaSaR_data.csv" contains concentrations of all samples.</p>
Long-term measurements of aerosol precursor concentrations in the Finnish sub-Arctic boreal forest
<p>This data set is connected to the article: </p> <p>Jokinen, T., Lehtipalo, K., Thakur, R. C., Ylivinkka, I., Neitola, K., Sarnela, N., Laitinen, T., Kulmala, M., Petäjä, T., and Sipilä, M.: Measurement report: Long-term measurements of aerosol precursor concentrations in the Finnish sub-Arctic boreal forest, Atmos. Chem. Phys., 2022</p>
Anion concentrations for LOD/LOQ determination
<p>This is a small dataset of different anion concentrations measured using a Metrohm ion chromatography compact IC. A standard of 0.1 mg/L was measured five times in order to determine the LOD and LOQ of the apparatus for these specific anions.</p>
Satellite monthly surface chlorophyll-a concentration, particulate backscattering, Secchi Disk depth, Mixed Layer Depth, Sea Surface Temperature at 25 km resolution optimally interpolated for the North Atlantic Ocean (1998-2018)
<p>Satellite monthly records of surface chlorophyll-a concentration (CHL), particulate backscattering at 443nm (bbp), Secchi Disk depth (zsd), Mixed Layer Depth (MLD), Sea Surface Temperature (SST) at 25 km resolution optimally interpolated via Multivariate Singular Spectrum Analysis (MSSA) for the North Atlantic Ocean for the period 1998-2018. This dataset has been used for the article "Ultra-oligotrophic waters expansion in the North Atlantic Subtropical Gyre revealed by 21 years of satellite observations" Leonelli et al. 2022, where details of interpolation method are fully explained.</p>
Data for the publication "Retrieving ice-nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations"
<p>This repository contains the data for the paper:</p> <p>Wieder, J., Ihn, N., Mignani, C., Haarig, M., Bühl, J., Seifert, P., Engelmann, R., Ramelli, F., Kanji, Z. A., Lohmann, U., and Henneberger, J.: Retrieving ice nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-67, in review, 2022.</p> <p>More information can be found in the README files.</p> <p>Note that the scripts to reproduce the figures of the publication are available on request.</p>
Building and characterizing a fluorescence setup to measure very low concentrations of analytes/biomarkers
<p>This training report is the result of my internship in the B-Phot Brussels Photonics team of the VUB<br> that took place between February 3 and April 3, 2020. The project of this internship nds its context<br> in the European SensApp project which regroups several European research institutes and universities,<br> including the VUB. The goal of this project is to develop a method to diagnose the Alzheimer's disease<br> in a faster and non-invasive manner, simply through a blood test, which is currently not possible because<br> the concentration of biomarkers of the Alzheimer's disease in the blood is too low. During this internship,<br> I was lead to build, align and calibrate a uorescence detection setup. Using this setup, I made mea-<br> surements of the uorescence intensity of low concentrations of dye solutions. From those measurements,<br> I performed calculations of the signal to noise ratio in order to determine the limit of detection of the<br> setup. Finally, I studied the kinetics of photobleaching in order to get to a better understanding of its<br> impact on the measurements.</p>
Data from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional concentrated vertical live load applied at mid-span
<p>For each one of the simulations performed from the parametric analysis of masonry buttressed arches with limit analysis subjected to vertical self-weight plus a proportional concentrated vertical live load applied at mid-span, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry buttressed arch. Finally, the .png file presents the collapse mechanism obtained. </p>
Satellite-derived chlorophyll-a concentrations for Lake Harsha (USA) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery
<p>This dataset contains satellite-derived chlorophyll-a data of Lake Harsha (USA) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery.</p> <p>Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following:</p> <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. <em>Remote Sensing of Environment, 270</em>, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. <em>Frontiers in Remote Sensing, 1</em></li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. <em>Remote Sensing of Environment, 240</em>, 111604</li> </ul>
Dimethylsulfoniopropionate-derived compound concentrations, volatile organic compound concentrations, and microorganism abundances around two corals and a seaweed in the reefs of Moorea (French Polynesia)
<p>These data belong in the paper: </p> <p>M. Masdeu-Navarro, J-F. Mangot, L. Xue, M. Cabrera-Brufau, S.G. Gardner, D.J. Kieber, J.M. González, R. Simó (2022). Spatial and diel patterns of volatile organic compounds, DMSP-derived compopunds and planktonic microorganisms around a tropical scleractinian coral colony. <em>Frontiers in Marine Science</em>.</p> <p>Concentrations of DMSP, acrylate, DMSO, DMS, DMDS, COS, CS2, isoprene, CH3I, CH2ClI, CH2Br2 and CHBr3 in seawater samples around colonies of the corals Acropora pulchra and Pocillopora sp., and the brown seaweed Turbinaria ornata. Abundances of high-DNA and low-DNA bacteria, Prochlorococcus, Synechococcus, picoeukaryotes and nanoeukaryotes in the same samples, as determined by flow cytometry. All samples were collected in April 2018 in the coral reefs of Mo'orea, French Polynesia. </p> <p>The upper set of data contains concentrations at the distance of 0.5 cm from the coral polyps on the branch tips or verrucae, as well as from the seaweed thalli (samples IN), and 2 m away, downcurrent (samples OUT). The second set of data corresponds to A. pulchra only, and contains seawater samples IN, OUT and AL, the latter being sampled at 0.5 cm from the base of the dead branches colonized by a turf alga. IN, OUT and AL samples were collected over an entire diel cycle, every 6 hours for a period of 30 hours.</p>
Nanoscale mapping of point defect concentrations with 4D-STEM
<p>The following 4D-STEM data sets were collected on the ThemIS and TitanX scanning transmission electron microscopes located at the National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. </p> <p>1• Au_thermal_beforeHT_17C.dm4, Au_thermal_HT_800C.dm4, Au_thermal_HT_1000C.dm4 and Au_thermal_afterHT_17C.dm4 are datasets from 4D-STEM measurements conducted <em>in situ</em> on an FEI ThemIS image corrected microscope at 300 kV during a thermal cycling experiment. These digital micrograph (.dm4) files were collected at 17 C before heat treatment, 800 C during heat treatment, 1000 C during heat treatment, and 17 C after heat treatment, respectively. Nano-diffraction data was collected using a Gatan K2-IS (2k x 2k) detector at 400 frames per second. Each dataset contains a set of electron diffraction patterns taken at each scan position with a ~ 1 nm probe step size. Approximately 80 x 80 scan positions were recorded from each region with a dwell time of 0.0025 seconds per frame. A custom 40µm patterned “bullseye” circular probe forming aperture was used to enhance the accuracy of 4D-STEM strain analysis by facilitating the identification of the center of diffraction discs. A convergence angle of 3.20 milli-radians, spot size of 8, and diffraction pixel size of 0.16 Å<sup>-1</sup> was used in micro-probe lens configuration. The data was machine and software binned to 512 x 512 pixels to increase the signal to noise ratio before computational analysis. Data processing were performed using strain mapping scripts provided in the open source py4DSTEM software package. Au_thermal_calibration.h5 contains the py4DSTEM calibration and diffraction standard data from the analysis. Polycrystalline Al standard sample was used to calibrate the reciprocal space pixel size as well as measure the elliptical distortion present in the data set.</p> <p>2• Al_irradiated.dm4 is a dataset from 4D-STEM measurement conducted <em>in situ </em>on an FEI TitanX microscope equipped to do high-angle STEM tomography and operating at 300 kV. Nano-diffraction data was acquired using a Gatan Orius 830 (2k x 2k) detector capable of collecting 30 frames per second. Each dataset contains a stack of convergent beam electron diffraction (CBED) patterns taken at each scan position with maximum resolution equivalent to 1.6 nm probe size. Approximately 50 x 50 frame scan regions were recorded with a dwell time of 0.01 seconds per frame. A custom 70 µm patterned “bullseye” circular C2 aperture was used to greatly enhance the accuracy of 4D-STEM strain analysis by facilitating the identification of the center of diffraction discs. A convergence angle of 2.7 milli-radians, spot size 10, and camera length 195 mm was used in micro-probe lens configuration. With a measured screen current of 300 pA in this configuration, the total sum of electrons incident in a region of the sample, commonly known as the fluence (total dose), was determined at 67,100 electronsÅ<sup>-2</sup> per 4D-STEM scan. The 4D-STEM data was machine and software binned to 512 x 512 pixels to increase the signal to noise ratio before computational analysis. Al_irradiated_calibration.h5 contains the py4DSTEM calibration and diffraction standard data from the analysis. Polycrystalline Al standard sample was used to calibrate the reciprocal space pixel size as well as measure the elliptical distortion present in the data set.</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.