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5,906 results for “size”
SBC LTER: Reef: Kelp Forest Community Dynamics: Abundance and size of Giant Kelp (Macrocystis Pyrifera), ongoing since 2000
These data describe the abundance of giant kelp, Macrocystis pyrifera, and are part of the SBCLTER kelp forest monitoring program. The study was initiated in 2000, in the Santa Barbara Channel, California, USA, and this dataset is updated once per year. The number and size (number of fronds and diameter of holdfasts) of plants were recorded along permanent transects at nine reef sites located along the mainland coast of the Santa Barbara Channel and at two sites on the north side Santa Cruz Island. These sites reflect several oceanographic regimes in the channel and vary in distance from sources of terrestrial runoff. The time period of data collection varied among the 11 kelp forest sites. Sampling at BULL, CARP, and NAPL began in 2000, sampling at the other 6 mainland sites (AHND, AQUE, IVEE, GOLB, ABUR, MOHK) began in 2001 (transects 3, 5, 6, 7, 8 at IVEE were added in 2011). Data collection at the two Santa Cruz Island sites (SCTW and SCDI) began in 2004. See Methods for more information.
SBC LTER: Reef: Long-term experiment: Kelp removal: Urchin size frequency distribution
These data describe the size frequency distribution of red (Mesocentrotus franciscanus) and purple (Strongylocentrotus purpuratus) sea urchins within permanent plots of a long-term experiment designed to examine trajectories of change in the structure and productivity of kelp forest communities in response to changes in the frequency and severity of disturbance to giant kelp. The diameter of the test (shell without spines) was recorded to the nearest 0.5 cm for 50 red and 50 purple sea urchins located within a 40 m x 2 m area of each plot. Size frequency data of red and purple sea urchins are not collected in the continual kelp removal plots. When combined with size-mass relationships established in the laboratory these data were used to provide a non-destructive, in situ estimate of the dry mass per unit area of bottom for each species. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel and included an annual kelp removal treatment designed to simulate increases in the frequency and severity of winter wave disturbance and a continual kelp removal treatment that allowed the effects of giant kelp on the community to be evaluated. The last experimental removals of giant kelp occurred in winter 2016 or winter 2017, depending on the site. Data collection continued in all plots until spring 2023 to document the recovery trajectory of the reef fish community following the cessation of experimental kelp removal.
SBC LTER: Reef: Long-term experiment: Kelp removal: Abundance and size of Giant Kelp
These data describe the abundance and size of giant kelp (Macrocystis pyrifera) within permanent plots of a long-term experiment designed to examine trajectories of change in the structure and productivity of kelp forest communities in response to changes in the frequency and severity of disturbance to giant kelp. The number of giant kelp > 1 m tall were recorded within four contiguous 20 m x 1m permanent plots located within a 40 m x 2 m area. The number of fronds > 1 m tall were counted for each individual and used as an estimate of its size. The experiment was initiated in 2008 at five reef sites along the mainland coast of the Santa Barbara Channel and included an annual kelp removal treatment designed to simulate increases in the frequency and severity of winter wave disturbance and a continual kelp removal treatment that allowed the effects of giant kelp on the community to be evaluated. The last experimental removals of giant kelp occurred in winter 2016 or winter 2017, depending on the site. Data collection continued in all plots until spring 2023 to document the recovery trajectory of the reef fish community following the cessation of experimental kelp removal.
SBC LTER: Reef: Abundance, size and fishing effort for California Spiny Lobster (Panulirus interruptus), ongoing since 2012
Data on abundance, size and fishing pressure of California spiny lobster (Panulirus interruptus) are collected along the mainland coast of the Santa Barbara Channel. Spiny lobsters are an important predator in giant kelp forests off southern California. Two SBC LTER study reefs are located in or near the California Fish and Game Network of Marine Protected Areas (MPA), Naples and Isla Vista, both established as MPAs on 2012-01-01. MPAs provide a unique opportunity to investigate the effects of fishing on kelp forest community dynamics. Sampling began in 2012 and is ongoing. This dataset contains two tables. 1) Abundance and size data collected annually by divers in late summer before the start of the fishing season at five SBC LTER long term kelp forest study sites: two within MPAs (Naples and Isla Vista) and three outside (Arroyo Quemado, Mohawk and Carpinteria). 2) Fishing pressure, as determined by counting the number of commercial trap floats. Data are collected every two to four weeks during the lobster fishing season (October to March) at nine sites along the mainland, eight of which are also SBC LTER long-term kelp forest study reefs. See Methods for more information.
Sediment grain size in the Virginia coastal bays, 2022
This dataset contains sediment grain size distributions from benthic sediment cores collected from shallow sites across coastal bays of Virginia, USA. The samples were collected in July 2022 at 50 long-term sampling sites. Most sites were sampled in seagrass meadows (eelgrass Zostera marina), but some sites are unvegetated (bare substrate).
Supplementary information for Xing et al.: Hummingbird-sized dinosaur from the Cretaceous of Myanmar
<p>This contribution contains an interactive 3D model of HPG-15-3, the TNT nexus file for the phylogenetic analysis, comparative data, and R scripts/tree files to reproduce the following figures of Xing et al. (2019):</p> <p>Figure 02</p> <p>Figure 03</p> <p>Extended Data Figure 06</p> <p>Extended Data Figure 09.</p> <p>Data for Figure 03 were originally published in Schmitz and Motani (2011).</p>
PopDel identifies medium-size deletions jointly in tens of thousands of genomes - Variant call sets
<p>This data set contains the variant calls sets generated by different tools for the benchmarks in the paper <a href="https://www.nature.com/articles/s41467-020-20850-5">PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes</a>. It includes the VCFs/BCFs for the following test cases:</p> <ul> <li>Random deletion simulation on up to 1000 chromosome 21 samples</li> <li>1000 Genomes Project deletions inserted into simulated chromosomes 17 to 22 of up to 500 samples</li> <li>HG001 (NA12878)</li> <li>Trio of <a href="https://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/NIST_HiSeq_HG002_Homogeneity-10953946/">HG002</a> + <a href="https://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG003_NA24149_father/NIST_HiSeq_HG003_Homogeneity-12389378/">HG003</a> + <a href="https://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG004_NA24143_mother/NIST_HiSeq_HG004_Homogeneity-14572558/">HG004</a></li> <li><a href="https://github.com/Illumina/Polaris/wiki/HiSeqX-Diversity-Cohort">Polaris Diversity cohort</a></li> <li><a href="https://github.com/Illumina/Polaris/wiki/HiSeqX-Kids-Cohort">Polaris Kids cohort</a></li> </ul> <p>Further, the long and short read reference call sets for HG001 are provided. For HG002 the reference call set and the high confidence regions by the Genome in a Bottle consortium are provided.</p> <p>For details on how the files have been created, please refer to the paper and the script repository on <a href="https://github.com/kehrlab/PopDel-scripts">GitHub</a>.</p>
Combined continuous nanoparticle synthesis with chromatographic size classification
<p>In this paper, we report a combination of the continuous flow synthesis of gold nanoparticles (AuNPs) with subsequent purification and narrowing of the particle size distribution (PSD) by size-exclusion chromatography (SEC) by adapting the flow rates of synthesis and classification. First, we show scalability of chromatographic classification with respect to column dimension and the absence of irreversible nanoparticle adhesion on the column material. Two different syntheses lead to a large and widely distributed and a small and narrowly distributed AuNP dispersion, which are classified by a semipreparative column. The PSDs of individual fractions are characterized by analytical SEC. The broadly distributed AuNP dispersion was classified into three fractions with distinct PSDs. For the narrowly distributed AuNPs, the separation is almost independent of the mobile phase flow rate: coarse and fine fractions with almost identical PSDs and separation efficiency curves are observed irrespective of the flow rate. Even NP samples with narrow PSDs can be classified into multiple fractions with tailored PSDs while simultaneously removing dissolved impurities from the dispersion. With our study, we demonstrate the potential of a direct combination of continuous NP synthesis with chromatographic classification for the optimization of final PSDs and the simultaneous purification of nanoparticulate dispersions.</p><p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)– Project-ID 416229255 – SFB 1411</p>
Organic Matter, Geochemical, Visible Spectrocolorimetric Properties, Radiocesium Properties, and Grain Size of Potential Source Material, Target Sediment Core Layers and Laboratory Mixtures for Conducting Sediment Fingerprinting Approaches in the Mano Dam Reservoir (Hayama Lake) Catchment, Fukushima Prefecture, Japan
<p>The current dataset was compiled to study sediment fingerprintings practices, i.e tracer selection and contribution modelling. Organic matter, elemental geochemistry, visible difuse spectrocolorimetric properties, radiocesium properties, and grain size were analysed were analysed in potential source material that may supply sediment to coastal rivers, here the upper part of the Mano river, draining the main Fukushima radioactive pollution plume (Japan). Four potential soil source materials (<em>n</em> = 68) were considered: undecontaminated cropland (<em>n</em> = 24), as non-decontaminated soil before the application of local decontamination policies, remediated cropland (<em>n</em> = 10), as decontaminated soil after the application of local decontamination policies, forest soils (n = 24) and subsurface material originating from channel bank collapse or landslides (<em>n</em> = 10; referred to as subsoil). A sediment core was collected in the Mano Dam lake (Hayama lake) on the 6th June 2021 and was sectionned into 1-cm layers (<em>n</em> = 38). Laboratory mixtures (<em>n</em> = 27) were made to assess different contribution levels from the sources.</p> <p>The current dataset comprises four .csv files including data and metadata information and their respective descriptions of variables. The data set is composed of soil samples, sediment core layer and laboratory mixtures. Laboratory mixtures were prepared to provide a dataset to calibrate/validate un-mixing models implemented to address this research question and analysed in the same conditions and using the same equipment as the source/target material.</p> <p>Recommended encoding format: <strong>latin1</strong></p>
Economical routes to size-specific assembly of self-closing structures
<p>This data contains images related to a publication on the self-assembly of DNA origami particles (<a href="https://www.science.org/doi/10.1126/sciadv.ado5979">https://www.science.org/doi/10.1126/sciadv.ado5979</a>). In this work, we conduct self-assembly experiments with various unique subunit types that target two different diameters of tubule structures.</p> <p>We provide image data of tubules that are associated with the probability distributions reported across several figures in the main text. Images of tubules are in the ZIP archives and show the section of tubules we analyzed to produce the probability distributions in the manuscript. Each folder of images has an associated CSV file that relates an image name to the type of tubule that the image was identified as. Tubule types have "m" and "n" values.</p> <p>We provide full tomogram reconstruction data for the multicomponent tubules that are shown in Figure 2 of the main text. In the ZIP archive, each tubule image has two files associated with it: a REC file that contains the tomogram reconstruction data and an MDOC file that contains imaging metadata. REC files can be opened with the open-source software IMOD.</p> <p>We provide raw image data of pitch- and width-controlled tubules that have been labeled with gold nanoparticles. These accompany the representative images in Figure 4 in the main text. (Pitch Controlled 4-color with GNPs.zip, Width Controlled 4-color with GNPs.zip).</p> <p>We provide raw image data of length-controlled tubules. These images accompany Figure 5 in the main text. (Length Controlled Tubule Images.zip)</p> <p><strong>Associated publication citation:</strong></p> <div> <p><span>Thomas E. Videbæk <em>et al., </em></span><span>Economical routes to size-specific assembly of self-closing structures. </span><span><em>Sci. Adv. </em></span><span><strong>10</strong>, </span><span>eado5979 </span><span>(2024). </span><span>DOI:<a href="https://doi.org/10.1126/sciadv.ado5979">10.1126/sciadv.ado5979</a></span></p> </div>
Size distribution of neutral and charged particles smaller than 42 nm measured over the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition (ACE).
<p>The size distribution of neutral and charged particles was measured using a neutral cluster and air ion spectrometer (NAIS) instrument. The concentration was corrected for diffusional losses in the inlet.</p> <p>The concentration and temporal dynamics of small particles is fundamental to characterize the first step of new particle formation (NPF) and growth. Moreover, naturally charged particles and ions can provide information about the role of ion induced nucleation. Newly formed particles can grow to larger sizes where they act as cloud condensation nuclei, directly affecting the Earth radiative budget and cloud properties.</p> <p>Measurements were performed on the upper deck of icebreaker Akademik Tryoshnikov along the track of the Antarctic Circumnavigation expedition. Temporal coverage is from January 22, 2017 to April 11, 2017. The concentration is reported as dN/dlog(Dp) per cubic centimetre, where Dp indicates the corresponding diameter size bin. Data were collected with one-second time resolution and averaged automatically by the acquisition software to 120 seconds before January 31 2017 and to 90 seconds after that date. The instrument was calibrated before the campaign by the manufacturer and periodically cleaned during the campaign (one time per leg).</p> <p>Pollution from the ship exhaust and other human activities (e.g. helicopter flights) was identified as described in Schmale et al., 2019 (<a href="https://doi.org/10.1175/BAMS-D-18-0187.1">https://doi.org/10.1175/BAMS-D-18-0187.1</a>) and a corresponding flag was associated to the data (with 1 meaning clean data and 0 polluted data).</p> <p> </p> <p>***** Dataset contents *****</p> <p>- 01_neutral_particles_size_distribution.csv, data file, comma-separated values</p> <p>- 02_negative_ions_size_distribution.csv, data file, comma-separated values</p> <p>- 03_positive_ions_size_distribution.csv, data file, comma-separated values</p> <p>- 04_neutral_particles_size_distribution_header.txt, metadata, text</p> <p>- 05_negative_ions_size_distribution_header.txt, metadata, text</p> <p>- 06_positive_ions_size_distribution_header.txt, metadata, text</p> <p>- README.txt, metadata, text</p> <p>Data that were missing or bad because of instrumental problems were simply removed from the file (no entry).</p>
Inferring size-based functional responses from the physical properties of the medium
<p>Databases used to test the model described in the article "Inferring size-based functional responses from the physical properties of the medium", Frontiers in Ecology and Evolution. Please read the "Readme.pdf" file for detailed information. This file explains all the variables and provides full references for the data in each of the datasets.</p> <p>"Portalier_et_al_2021_Species_Speeds.csv" provides species speeds according to body size for numerous species in aquatic systems.</p> <p>"Portalier_et_al_2021_Predator_Prey_Interactions.csv" provides attack rates, capture probabilities and handling times for numerous predator-prey interactions in aquatic systems.</p>
XCT data of metallic feedstock powder with pore size analysis
<p><strong>X-Ray computed tomography (XCT) scan of 11 individual metallic powder particles, made of (Mn,Fe)<sub>2</sub>(P,Si) alloy</strong></p> <p>The data set consists of 4 single XCT scans which have been stitched together [3] after reconstruction.<br> The powder material is an (Mn,Fe)<sub>2</sub>(P,Si) alloy with an average density of 6.4 g/cm³. The particle size range is about 100 - 150 µm with equivalent pore diameters up to 75 µm. The powder and the metallic alloy are described in detail in [1, 2].</p> <p><strong>Data acquisition</strong></p> <p>The data was acquired using a Zeiss Xradia 620 Versa X-ray microscope which provides the opportunity of optical magnification.</p> <table> <caption><strong>Tomographic imaging parameters</strong></caption> <tbody> <tr> <td>XCT system</td> <td>Zeiss Xradia 620 Versa</td> </tr> <tr> <td>Voltage</td> <td>80</td> <td>kV</td> </tr> <tr> <td>Power</td> <td>10</td> <td>W</td> </tr> <tr> <td>Source filtering</td> <td>"<em>LE2</em>" (system specific)</td> <td>-</td> </tr> <tr> <td>Source-object distance</td> <td>10</td> <td>mm</td> </tr> <tr> <td>Object-detector distance</td> <td>10</td> <td>mm</td> </tr> <tr> <td>Geom. magnification</td> <td>2</td> <td>-</td> </tr> <tr> <td>Optical magnification</td> <td>20</td> <td>-</td> </tr> <tr> <td>Native pixel size</td> <td>13.5</td> <td>µm</td> </tr> <tr> <td>Binning</td> <td>2x2</td> <td>px</td> </tr> <tr> <td>Voxel size</td> <td>0.68</td> <td>µm</td> </tr> <tr> <td>No. of projections per scan</td> <td>801</td> <td>1</td> </tr> <tr> <td>No. of scans</td> <td>4</td> <td>-</td> </tr> <tr> <td>Exposure time per projection</td> <td>5</td> <td>s</td> </tr> </tbody> </table> <p> </p> <p><strong>Projection data</strong> (801 single TIFF-files each):</p> <ul> <li>proj_00</li> <li>proj_01</li> <li>proj_02</li> <li>proj_03</li> </ul> <p><strong>Reconstructed data</strong>:</p> <ul> <li>raw-volume (MnFePSi-Powder_80kV_10W_LE2_20x_5s_801_0p68_BHC=2_Stitch_U16_966x1020x2916.raw + header.txt)</li> <li>analyzed data as Volume Graphics Studio MAX 3.4.5 project</li> </ul> <p><strong>Stitched 2D data</strong> (images stitched with ImageJ-Plugin described in [3]<strong>:</strong></p> <ul> <li>Stitched_0deg_Projections.tif</li> <li>Pores+Particles_Analysis.tif</li> </ul> <p> </p> <p>[1] G.-R. Jaenisch, U. Ewert, A. Waske, and A. Funk, “Radiographic Visibility Limit of Pores in Metal Powder for Additive Manufacturing,” Metals, vol. 10, no. 12, p. 1634, Dec. 2020. https://doi.org/10.3390/met10121634</p> <p>[2] X. Miao et al., “Printing (Mn,Fe)2(P,Si) magnetocaloric alloys for magnetic refrigeration applications,” J. Mater. Sci., vol. 55, no. 15, pp. 6660–6668, May 2020. https://doi.org/10.1007/s10853-020-04488-8</p> <p>[3] S. Preibisch, S. Saalfeld, and P. Tomancak, “Globally optimal stitching of tiled 3D microscopic image acquisitions,” Bioinformatics, vol. 25, no. 11, pp. 1463–1465, Jun. 2009.</p>
Replication data for: "The hapax / type ratio: an indicator of minimally required sample size in productivity studies?"
<p>The dataset accompanies the scientific article "The hapax / type ratio: an indicator of minimally required sample size in productivity studies?" and can be used to reproduce the findings presented in this article. This dataset consists of two components, namely (i) the corpus data involving the Dutch semi-copular verb "raken" and (ii) an R analysis script to reproduce the computational steps.</p>
Sub-10 nm size-distribution data for "What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?"
<pre>Size-Distribution data from the CERN CLOUD experiment (Kirkby et al., 2011) measured with a DMA-train (Stolzenburg et al., 2017) Data acquired during the CLOUD10 (Fall 2015) and CLOUD12 (Fall 2017) campaigns. Data associated with the publication Kontkane et al. (2022). File name indicates the Experiment number as specified in Table 3, Kontkanen et al. (2022) and the internal CLOUD run numbers as given in Table S1, Kontaknen et al. (2022). Concentration of precursor gases are also given in these two Tables. Exp. 8 only used data from NAIS and is not included in this repository. Header indicates the diameter at which the size-distribution is measured. First column is time column with areadable timestamp in the format %Y-%m-%d %H:%M:%S. Data is dN/dlog_10 dp in unit cm^(-3). Full size-distribution (up to 400 nm) can be obtained from the author upon request. References: Kontkanen et al. (2022), What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?, Environ. Sci.: Atmos., accepted. Kirkby et al. (2011), Role of sulphuric acid, ammonia and galactic cosmic rays in atmospheric aerosol nucleation, Nature, 476, 429-433, http://dx.doi.org/10.1038/nature10343 Stolzenburg et al. (2017), A DMA-train for precision measurement of sub-10nm aerosol dynamics, Atmos. Meas. Tech., 10, 1639-1651, http://www.atmos-meas-tech.net/10/1639/2017/ </pre>
Chlorophyll a concentration, particulate organique carbon, and particle mean size index [gamma; 0.2 - 20 µm] measured using an hyperspectral spectrophotometer [ACS, Wetlabs] during the Tara Pacific Expedition 2016-2018
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples (Gorsky et al. 2019, Planes et al. 2019, Flores et al. 2020). The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide the continuous dataset originating from the hyperspectral and multispectral spectrophotometers [ACS] instruments acquiring continuously during the full course of the campaign. Surface seawater was pumped continuously through a hull inlet located 1.5 m under the waterline using a membrane pump (10 LPM; Shurflo), circulated through a vortex debubbler, a flow meter, and distributed to a number of flow-through instruments. An [ACS] spectrophotometer (WETLabs) measured hyper-spectral (4 nm resolution) attenuation and absorption in the visible and near infrared except between Panama and Tahiti where an AC-9 multispectral spectrophotometer (WETLabs) was used instead. The flow was automatically directed through a 0.2 µm filter for 10 minutes every hour before being circulated through the spectrophotometer to eliminate the impact of biofouling and instrument drift and estimate particulate absorption [ap] and attenuation [cp] (Slade et al. 2010). Chlorophyll a content was estimated from particulate absorption line height at 676 nm (Boss et al. 2001). The particulate organic carbon concentration [poc] was estimated using an empirical relation (Gardner et al. 2006) between measured [poc] and measured [cp]. An indicator for size distribution of particles between 0.2 and ~20 µm [gamma] was calculated from [cp] (Boss et al 2001). The data was processed with custom software for underway optical data (InLineAnalysis software available on GitHub). The detailed information regarding the data processing is given in the processing report attached with the data and in Lombard et al. (In prep.). These results are preliminary: no matchup with in-situ chlorophyll from HPLC or [poc] measurements were performed.</p>
Field and laboratory measurements of suspended-sediment particle size and concentration from nine rivers draining to the Great Barrier Reef
<p>Dataset includes in-situ (n = 144,912) and laboratory-dispersed (n = 64) particle size measurements collected using laser diffractometry from nine rivers discharging along 800 km of Great Barrier Reef, Queensland Australia coastline. Two field campaigns (24 February to 5 March 2021 and 24<sup>th</sup> to 31<sup>st</sup> of April 2021) were undertaken to collect vertical profiles of in-situ particle size, water velocity, turbidity, and salinity. Water samples were collected for analysis of laboratory-dispersed particle size and suspended-sediment concentration. Water samples were collected using US-P61 or Van-Dorn samplers deployed alongside a LISST 200x laser diffractometer and an EXO2 YSI multiparameter water quality sonde. Total depth and water velocity were measured using a Nortek Signature ADCP and Teledyne RiverRay ADCP during the first and second field campaigns, respectively. During both campaigns, measurements were undertaken during relatively high discharge events when discharge exceeded the 90<sup>th</sup> percentile of 2020/2021 gauged wet season flows.</p> <p>Data are provided in three csv files. "In_situ_data.csv" contains in situ measurements of particle size, turbidity, salinity, and depth along with estimates of shear rate. Shear rate is estimated from theory and measurements of ADCP-measured total depth and depth-averaged flow (see equation 2 of Livsey et al., 2022). "Lab_data_this_study.csv" contains particle size measurements of suspended-sediment following laboratory dispersion along with coeval measurements of in-situ particle size, turbidity, salinity, and shear rate averaged over the filling time of the US-P61 sampler. "Lab_data_DES_WQI.csv" contains laboratory dispersed particle size measurements collected by the Department of Environment and Science Water Quality Investigation Unit of Queensland Australia (Turner et al., 2013) and compared to data in "Lab_data_this_study.csv" in Livsey et al (2022). </p> <p>Further details of the data collection effort and interpretation of the data are published in Livsey et al (2022) at https://doi.org/10.1029/2021JC017988. </p> <p>Additional data from the 24 February to 5 March 2021 field campaign, funded by CSIRO Oceans and Atmosphere, are available from Crosswell et al (2022) at https://doi.org/10.25919/2vbh-cx08.</p> <p>References:</p> <p>Crosswell, Joey; Carlin, Geoff; Daniel, Livsey; Hillyer, Katie; Steven, Andy (2022): FNQ_2021_V01 Voyage dataset: Feb - March 2021; Biogeochemical and hydrodynamic obervations along the river-reef continuum of estuaries in eastern Cape York, Australia. v1. CSIRO. Data Collection. 10.25919/2vbh-cx08</p> <p>Livsey, D. L., Crosswell, J. R., Turner, R. R., Steven, A. D. L., & Grace, P. R. (2022) Flocculation of riverine sediment draining to the Great Barrier Reef, implications for monitoring and modelling of sediment dispersal across continental shelves. Journal of Geophysical Research: Oceans. https://doi.org/10.1029/2021JC017988</p> <p>Turner. R, Huggins. R, Wallace. R, Smith. R, Vardy. S, Warne. M St. J. (2013). Total suspended solids, nutrient, and pesticide loads (2010-2011) for rivers that discharge to the Great Barrier Reef Great Barrier Reef Catchment Loads Monitoring 2010-2011 Department of Science, Information Technology, Innovation and the Arts, Brisbane.</p> <p> </p> <p> </p>
Sediment size dataset for Australia
<p>This repository contains a dataset of median grain size (d50) for the Australian coastline.</p> <p>The sediment samples were collected by <a href="https://www.sydney.edu.au/science/about/our-people/academic-staff/andrew-short.html">Professor Andrew D. Short</a> during field campaigns between 1979 and 1999. This dataset includes all the <em>sand</em> samples collected in the <em>swash zone</em><em>. </em>More information about this dataset can be found in <a href="https://link.springer.com/book/10.1007/978-3-030-14294-0#bibliographic-information">Australian Coastal Systems book</a>. The beach sand sample collection is physically stored at Geoscience Australia (Canberra) and can be viewed by contacting <a href="mailto:AusGeoSamples@ga.gov.au">AusGeoSamples@ga.gov.au</a>.</p> <p><strong>Dataset description</strong></p> <p>The data is contained in the file <strong>Australia_dataset.geojson</strong>. This geospatial layer contains a linestring for each individual beach/embayment. The coordinate system of the geospatial layer is WGS84.<br> <br> This geospatial layer matches and complements the Australian beach-face slope dataset published here <a href="https://doi.org/10.5281/zenodo.5606216">https://doi.org/10.5281/zenodo.5606216</a> and described in <em><a href="https://doi.org/10.5194/essd-14-1345-2022">Vos et al. 2022</a>. Note that not every beach in the layer contains a sediment size value.</em></p> <p>Each feature has the following attributes:</p> <p><strong>Grain-size and location attributes</strong><br> - <em>d50</em>: Median grain-size in millimetres. Obtained by sieving the sand samples. The original sand samples were donated to Geoscience Australia.<br> - <em>beach_id</em>: Database id for each beach, e.g., aus0001, aus0002, …, aus5255 (same as in <a href="http://doi.org/10.5281/zenodo.5606216">Vos et al. 2022</a>),<br> - <em>ABSAMP_id</em>: id of the sample in the ABSAMP database, e.g., nsw0001, tas001, qld001 etc. See the <a href="https://ecat.ga.gov.au/geonetwork/srv/api/records/d14b2b5b-332d-4e8f-b732-3d01a06866b2">Smartline</a> from Geoscience Australia for the location of each id.<br> - <em>distance_to_sample</em>: Distance in metres between the linestring in the ABSAMP database and the linestring in this layer.<br> - <em>latitude</em>: Latitude of the centroid of the beach in WGS84.<br> - <em>longitude</em>: Longitude of the centroid of the beach in WGS84.<br> - <em>beach_length</em>: Length of the beach or embayment, very long beaches (>50km) were split to optimise memory usage.<br> - <em>coastal_region</em>: Database id corresponding to the 23 coastal regions as identified by <a href="https://www.sciencedirect.com/science/article/pii/S0964569117306129">Thom et al. (2018)</a>.<br> - <em>primary_comp_id</em>: Database id corresponding to the 100 primary sediment compartments as identified by <a href="https://www.sciencedirect.com/science/article/pii/S0964569117306129">Thom et al. (2018)</a>.<br> - <em>secondary_comp_id</em>: Database id corresponding to the 361 secondary sediment Compartments as identified by <a href="https://www.sciencedirect.com/science/article/pii/S0964569117306129">Thom et al. (2018)</a>.</p> <p><strong>Wave climate and tide range attributes</strong><br> - Hs<em>_mean</em>: Mean Significant Wave Height at the closest grid point in the ERA5 re-analysis dataset (computed using 6-hourly time-series between 2010 and 2020).<br> - Hs<em>_max</em>: Max Significant Wave Height at the closest grid point in the ERA5 re-analysis dataset (computed using 6-hourly time-series between 2010 and 2020).<br> - <em>Tp_mean</em>: Mean Peak Wave Period at the closest grid point in the ERA5 re-analysis dataset (computed using 6-hourly time-series between 2010 and 2020).<br> - <em>Wdir_mean</em>: Mean Wave Direction at the closest grid point in the ERA5 re-analysis dataset (computed using 6-hourly time-series between 2010 and 2020).<br> - <em>Wdir_weighted_average</em>: More robust estimator of the Mean Wave Direction obtained by computing the average wave direction weighted by wave energy flux.<br> - <em>hsig_median</em>: Median Significant Wave Height from the closest grid point in the CAWCR re-analysis dataset<br> - <em>mstr</em>: Mean Spring Tide Range at the beach calculated from the closest grid point in the FES2014 global tide model</p> <p><strong>Beach-face slope attributes (same as in <a href="http://doi.org/10.5281/zenodo.5606216">Vos et al. 2022</a>)</strong><br> - <em>beach_slope_average</em>: Average of the beach-face slope at the site, weighted by the width of the confidence bands, value between 0.01 and 0.2<br> - <em>width_ci_average</em>: Average width of confidence band over the comprised transects, value between 0 and 0.19<br> - <em>quality_flag</em>: Quality flag indicating the confidence in the slope estimate at this transect (High, Medium or Low)<br> - <em>prc_msrt_obs</em>: percentage of the Mean Spring Tide Range observed by the satellite-derived shorelines<br> - <em>min_tide_obs</em>: Lowest tide level observed by the satellite-derived shorelines<br> - <em>max_tide_obs</em>: Highest tide level observed by the satellite-derived shorelines<br> - <em>sl_points_average</em>: Average number of datapoints in the shoreline time-series over the comprised transects</p>
Zinc Doped Zeolite 13X DIAD X-Ray Computed Tomography - 0.54 micron pixel size RAW
<p>This repository contains raw data for the zinc-doped zeolite 13X sample imaged on the DIAD beamline at Diamond Light Source. Data is stored as a .nxs file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 0.54 microns. A script containing the savu process list and code used to perform the 3D reconstruction is provided.</p> <p>A detailed data descriptor pre-print can be found at https://arxiv.org/abs/2409.07322#</p> <p> </p> <p>The size of the .h5 file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset:</p> <blockquote> <p>curl dataset_url > 43334_raw.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</p>
Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 1.625 micron pixel size
<p>This repository contains data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Data is stored as a .h5 file which can be loaded using ImageJ/Fiji. The size of this dataset is 2510x2510x2110 with a pixel-size of 1.625 microns.</p> <p>This data is one of four resolutions obtained.</p> <p>A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322#</p> <p>The size of the .h5 file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset:</p> <blockquote> <p>curl dataset_url > 169067_recon.h5</p> </blockquote> <p>Please replace dataset_url with the url you copied.</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)
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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.