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348 results for “Core data”
Palaeoecological data of KTG core, Katingan, Central Kalimantan, Borneo, Indonesia
<p>Southeast Asian peatlands, along with their various important ecosystem services, are mainly distributed in the coastal areas of Sumatra and Borneo. These ecosystems are threatened by coastal development, global warming and sea level rise (SLR). Despite receiving growing attention for their biodiversity and as massive carbon stores, there is still a lack of knowledge on how they initiated and evolved over time, and how they responded to past environmental change, i.e., precipitation, sea level and early anthropogenic activities. To improve our understanding thereof, we conducted multi-proxy palaeoecological studies in the Kampar Peninsula and Katingan peatlands in the coastal area of Riau and Central Kalimantan, Indonesia. The results indicate that the initiation timing and environment of both peatlands are very distinct, suggesting that peat could form under various vegetation as soon as there is sufficient moisture to limit organic matter decomposition. The past dynamics of both peatlands were mainly attributable to natural drivers, while anthropogenic activities were hardly relevant. Changes in precipitation and sea level led to shifts in peat swamp forest vegetation, peat accumulation rates, and fire regimes at both sites. We infer that the simultaneous occurrence of El Niño-Southern Oscillation (ENSO) events and SLR resulted in synergistic effects which led to the occurrenceere fires in a pristine coastal peatland ecosystem, however, it did not interrupt peat accretion. In the future, SLR, combined with the projected increase in frequency and intensity of ENSO, can potentially amplify the negative effects of anthropogenic peatland fires. This prospectively stimulates massive carbon release, thus could, in turn, contribute to worsening the global climate crisis especially once an as yet unknown threshold is crossed and peat accretion is halted, i.e., peatlands lose their carbon sink function. Given the current rapid SLR, coastal peatland managements should start develop fire risk reduction or mitigation strategies.</p>
Core-mantle boundary topography data from numerical simulation of instantaneous mantle flow
<p>Calculated CMB topography data for models (a) H0, (b) H0D1, (c) H4, (d) H4P3, (e) H4P6, (f) H4P3D1, (g) H4P3W, and (h) H4P3D1W. Negative (positive) values indicate topographic depression (elevation). See Figure 2 of Yoshida (2008).</p>
ODP Site 1249, ODP Site 1252, and IODP Site U1325: X-ray fluoresence core scanning, laser diffraction grain size, CNS elemental/isotopic, environmental magnetism, and age model data
<p>We present data used as part of an integrative early diagenesis study (submitted September 2022 to Marine Geology) focused on identifying zones of magnetite dissolution and pyrite precipitation in which magnetic susceptibility records are altered in gas-hydrate bearing sediments on the Cascadia Margin using archived cores from the Ocean Drilling Program (ODP) and Integrated Ocean Drilling Program (IODP). We analyzed the upper 85 to 100 m below seafloor (mbsf) from ODP Sites 1249 and 1252, and IODP Site U1325. ODP 1249 is at the summit of Hydrate Ridge in an area of active methane seepage and massive gas hydrate accumulations and ODP 1252 is in a nearby slope basin with little occurrence of hydrate. IODP Site U1325 is on the northern Cascadia Margin in a slope basin, with turbidite-hosted gas hydrate. We also include XRF data from the upper sections of ODP Site 1251, IODP U1327, and U1328.</p> <p>We measured X-ray fluorescence using an Avaatech core scanner at the IODP Gulf Coast Repository at Texas A&M University. We measured total carbon, total organic carbon (TOC), total nitrogen, and total sulfur using a Perkin Elmer 2400 Series CHNS/O Analyzer at the university of New Hampshire (ODP Site 1249 and 1252 only). A subset was analyzed for δ<sup>13</sup>C-TOC using a Costech ECS 4010 elemental analyzer interfaced with a Thermo Finnegan Delta Plus XP continuous flow isotope ratio mass spectrometer at Washington State University. Grain size was measured with a Malvern Mastersizer 2000 laser diffraction particle size analyzer and Hydro 2000G dispersal unit at the University of New Hampshire. Mass frequency-dependent magnetic susceptibility was measured using a Bartington MS2 Magnetic Susceptibility Meter and Bartington MS2B dual frequency sensor (Site U1325 only). Isothermal remanent magnetization and thermal demagnetization curves were measured using a HSM2 SQUID-based Spinner Magnetometer with an ASC Scientific IM-10-30 Impulse Magnetizer and ASC Scientific TD-48SC magnetically-shielded oven (Site U1325 only). Radiocarbon was measured on mixed planktic foraminifers at the Radiocarbon was measured at National Ocean Sciences Accelerator Mass Spectrometry (NOSAMS) facility at Woods Hole Oceanographic Institution (ODP Site 1252 and IODP Site U1325).. Radiocarbon ages were calibrated to calendar ages using CALIB 8.2 and the Marine20 calibration curve. For ODP Site 1252 we used a reservoir correction of 230 ± 50 years (Yaquina Bay, Oregon, USA) and for IODP Site U1325 we used a reservoir correction of 202 ± 50 years (Amphitrite Point, British Columbia, Canada). δ<sup>18</sup>O was measured on benthic foraminifer <em>Uvigerina peregrina</em> tests using a Finnegan MAT 252 isotope ratio mass spectrometer with Kiel III device at the Oregon State University Stable Isotope Laboratory (ODP 1252) and a Finnegan MAT 253 isotope ratio mass spectrometer with Kiel IV device at the University of Michigan Stable Isotope Laboratory (ODP Site 1249 and IODP Site U1325).</p>
Data from: A first draft of the core fungal microbiome of Schedonorus arundinaceus with and without its fungal mutualist Epichloë coenophiala
<p>Tall fescue (<em>Schedonorus arundinaceus</em>) is a cool-season grass that is commonly infected with the fungal endophyte <em>Epichloë</em> <em>coenophiala</em>. Although the relationship between tall fescue and <em>E</em>. <em>coenophiala</em> is well-studied, less is known about its broader fungal communities. We used next-generation sequencing of the ITS2 region to describe the complete foliar fungal microbiomes in a set of field-grown tall fescue plants over two years, and whether these fungal communities were affected by the presence of <em>Epichloë</em>. We used the Georgia 5 cultivar of tall fescue, grown in the field for six years prior to sampling. Plants were either uninfected with <em>E</em>. <em>coenophiala</em>, or they were infected with one of two <em>E</em>. <em>coenophiala</em> strains: the common toxic strain or the AR542 strain (sold commercially as MaxQ). We observed 3,487 amplicon sequence variants (ASVs) across all plants and identified 43 ASVs that may make up a potential core microbiome. Fungal communities did not differ strongly between <em>Epichloë</em> treatments but did show a great deal of variation between the two years. Plant fitness also changed over time but was not influenced by <em>E</em>. <em>coenophiala</em> infection.</p>
Southeast Greenland firn core FC-23-A - Depth, density, and oxygen isotope data
<p><br><br></p>
CORE Project - Task 5.2 Survey Data
<p><span>The datasets represents the raw data collected from the citizens surveys launched as part of the project's task 5.2, a study investigating the positive and negative aspects of the safety culture in different disaster scenarios, regions, and groups using multiple perspectives. </span></p>
nf-core/airrflow tutorial data
<p>Subsampled datasets to be used as test data for the nf-core/airrflow tutorial.</p>
Data for "Nanoparticle doping and molten-core methods towards highly thulium-doped silica fibers for 0.79 μm-pumped 2 μm fiber lasers – a fluorescence lifetime study"
<p>Includes data for basic characterization of the fibers (concentration profiles from EMPA, refractive index profiles of the preforms and fibers, attenuation of the fibers), as well as the measured fluorescence decay curves.</p>
Sea ice core temperature and salinity data collected during the 2022 SCALE Winter Cruise
Open the record for dataset details and reuse information.
Withdrawn: DZD Core Data Set - first Version published at DZD Website for internal use (obsoleted by DOI 10.21961/mdm:45923)
<p>The German Center for Diabetes Research (DZD) conducts large clinical multicenter studies in the field of diabetes and metabolic research. In this vein, a core data set (CDS) which contains a list of clinical parameters relevant for joint studies in diabetes research was established in 2021 and published for internal use at the DZD website (https://www.dzd-ev.de/en/). In 2022 a FAIRified version of the data set was published at MDM portal (https://medical-data-models.org/). This entry shows the very first version of the core data set, published as an excel file before the FAIRification. It is intended as a supplement for an article about the FAIRification process: "The Journey to a FAIR CORE DATA SET for Diabetes Research in Germany"</p>
LA_CRDS_WATER_ISOTOPE_ICE_CORES_RAW_DATA
<p>Raw data from LA-CRDS (Laser Ablation - Cavity Ring Down Spectroscopy) measurements on water isotope ice standards and ice core samples that were conducted at Ca'Foscari University of Venice.</p> <p>LA: Analyte Excite+, Teledyne Photon Machines, Bozeman MT, USA</p> <p>CRDS Water Isotope Analyzer: L-2130i, PICARRO, Santa Clara CA, US</p>
High-Level and Efficient Stream Parallelism on Multi-core Systems with SPar for Data Compression Applications
<p>The stream processing domain is present in several real-world applications that are running on multi-core systems. In this paper, we focus on data compression applications that are an important sub-set of this domain. Our main goal is to assess the programmability and efficiency of domain-specific language called SPar. It was specially designed for expressing stream parallelism and it promises higher-level parallelism abstractions without significant performance losses. Therefore, we parallelized Lzip and Bzip2 compressors<br> with SPar and compared with state-of-the-art frameworks. The results revealed that SPar is able to efficiently exploit stream parallelism as well as provide suitable abstractions with less code intrusion and code re-factoring.</p>
Data set associated with the paper "Implementation of the Vector Vorticity Dynamical Core on Cubed Sphere for Use in the Quasi-3-D Multiscale Modeling Framework"
<p>New data set associated with the revision of the paper "Development of a Global Quasi-3-D Multiscale Modeling Framework: <br> I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"</p> <p>The title of the paper has been changed to "Implementation of the Vector Vorticity Dynamical Core on Cubed Sphere for Use in the Quasi-3-D Multiscale Modeling Framework"</p> <p>New simulated data set of the advection test is in the folder ADVEC_NEW; New simulated data set of the barotropic instability test is in the folder BARO_NEW; New simulated data set of the baroclinic instability test is in the folder BCL_NEW</p>
Illustrative Darwin core archive to output data from a citizen science platform to a collection management system
<p>Illustrative DwC archive to send data back to a collection management system from a citizen sciences platform. This illustrative archive displays the specimens used for the trans-institutional and trans-platform pilot project held in the frame of ICEDIG.</p> <p>Further description of its content in the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Geochemical data of fine bed-sediment from downstream sediment cores and upstream source sub-catchments in a catchment-wide flood in the Brantian
<b>Description: </b><p>Geochemical datasets were obtained from fieldwork carried out in the Brantian catchment between June 2013 and November 2016 under the hydrology component of the SAFE Project. The project has two key components: (1) Geochemical profiles down historical sediment cores at seven downstream locations organised in a nested hierarchical arrangement; and (2) Geochemical data for sediment deposited by the single extreme high magnitude flood event of 12 September 2016 in all sub-catchment source areas sampled around the Brantian including all downstream sediment core locations referred to in (1) <br>Sample collection and preparation method:<br>Fluvial sediment cores were obtained from seven downstream sites located within a nested hierarchical (dendritic) arrangement with the study catchment outlet draining 377 km2 of the upper Brantian. Core sites 4 and 5 in the west were nested within core site 2; core sites 6 and 7 in the east were nested within core site 3; core sites 2 and 3 were in turn nested within core site 1 at the study catchment outlet. Areas upstream at each drainage hierarchy varied from 30-135km2 (core sites 4-7); 150-200km2 (core sites 2-3) and 377km2 (core site 1).<br>Sediment cores were obtained within the bankfull channel at sites inundated by high-flow events with the progressive accumulation of fine bed-sediment monitored by repeat measurements of surface profile. Pits were dug to create a shelf surface from which to obtain large (200g to 1100g) bulk samples of sediment integrated over depth intervals of 2cm. The shelf technique permits larger samples while depths are absolute and not affected by core liner compression. Core depths ranged from 102 cm to 210 cm.<br>Sediment samples deposited in the high-flow event of 12 September 2016 were obtained using pre-installed surface horizon marker grids. Surface-layer (0-2cm) scrape samples were composited over a 10-20 m2 area. At sites with depths of fresh sediment >2cm small sediment cores representing sediment deposited in that event were obtained using the shelf technique. Field replicates were obtained from the same elevation and at either higher or lower elevation within the channel.<br>All sediment samples were oven-dried at temperatures no higher than 40 C before dry-sieving to obtain the fine-sediment <63um size fraction. Other size fractions were obtained from nested sieve stacks in order to calculate bulk particle size distribution from the entire sample. Samples of dried <63um sediment were thoroughly mixed by hand (not ground) before a sub-sample transferred to a standard 32mm outer-diameter plastic pot pre-fitted with a 3um thick Prolene XRF analytical film window, compacted to 20Nm torque pressure and sealed. Mass (g) and total thickness (mm) of prepared samples were recorded. <br>Geochemical analysis method:<br>Total elemental concentration of each sample (ppm) was measured using a Niton XL3t GOLDD+ 900 Energy-Dispersive X-Ray Fluorescence (ED-XRF) analyser in a laboratory stand with count periods of 180 seconds for 'soils' (Compton scatter) mode (60s in each of three energy band filters: low medium and high). Measurements were also made in 'mining' mode (Fundamental Parameters calibration) using helium purging to obtain concentrations of light elements Mg-S. In both modes, two measurements were obtained for each sample by repositioning the sample window exposed to the XRF beam after the first measurement (position 1 and position 2). <br>ED-XRF analysis returns total elemental concentration (ppm) of elements Mg-U. Measurement error is reported by the Niton XRF in terms of 2sigma (two times standard deviation) for each element. All measurements were higher than limit of detection. Variability between the two measurement positions 1 and 2 reflects geochemical environmental variability in sediment (ie sampling error) and analytical error.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/133"><b>Assessing erosional impacts of logging and conversion to oil palm in the Brantian catchment using sediment fingerprinting and radioisotope dating.</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (Doctoral Training Grant, NE/L501827/1)</li><li>British Geomorphological Society (Postgraduate Research Grant)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000-2/2 JLD.3(149))</li><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000-2/2 JLD.5(145))</li><li>Sabah Forestry Department (Research licence 100-14/18/2KLT.29(37))</li><li>Maliau Basin Management Committee (MBMC) (Research licence 2015/29(165))</li><li>Sabah Biodiversity Council (Export licence JKM/MBS.1000-2/3 JLD.2(86))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3402746">here</a></p><p><b>Files: </b>This consists of 1 file: SamHigton_Geochem_fluvial_sediment_data.xlsx</p><p><b>SamHigton_Geochem_fluvial_sediment_data.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Sediment core geochemistry data</b> (described in worksheet Sediment_core_geochem_data)</p><p>Description: XRF analysis data of geochemical composition of sediment from 7 core sites. There is one sample for every 2cm depth interval down each core (with one set of bulk particle size data) and then two separate repeat XRF measurements of each sample.</p><p>Number of fields: 69</p><p>Number of data rows: 947</p><p>Fields: </p><ul><li><b>Core_number</b>: Core number (Field type: id)</li><li><b>Sample_Site</b>: Corresponds to sample site number in the locations tab (Field type: location)</li><li><b>Upstream_Area_km2</b>: Upstream area (Field type: numeric)</li><li><b>Sampling_date</b>: Date sediment sample taken (Field type: date)</li><li><b>Upper_Depth_cm</b>: Upper depth of sample slice relative to the sediment surface; for surface samples this will always be zero (Field type: numeric)</li><li><b>Lower_Depth_cm</b>: Lower depth of each sample slice relative to the sediment surface; in increments of 2cm (Field type: numeric)</li><li><b>BulkPS_<63um_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_63-125um_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_125um-2mm_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_>2mm_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>XRF_sample_mass_g</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>XRF_sample_thick_mm</b>: Mass of XRF sample analysed (Field type: numeric)</li><li><b>EDXRF_measurement_number_1or2</b>: Refers to XRF measurement 1 or 2 - each sample was analysed in two different positions across the sample measurement surface producing two measurements of element concentration and 2SD error for each sample (Field type: categorical)</li><li><b>Mg</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Al</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Si</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>P</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>S</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>K</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ca</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ti</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>V</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cr</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Mn</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Fe</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ni</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cu</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Zn</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>As</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Rb</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Sr</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Zr</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cd</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Sn</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Sb</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Te</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cs</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ba</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Pb</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Th</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>U</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Mg.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Al.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Si.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>P.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>S.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>K.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ca.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ti.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>V.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cr.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Mn.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Fe.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ni.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cu.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Zn.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>As.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Rb.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Sr.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Zr.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cd.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Sn.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Sb.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Te.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cs.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ba.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Pb.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Th.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>U.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li></ul></li><li><p><b>Sept2016 flood geochemical data</b> (described in worksheet Sept2016_flood_geochem_data)</p><p>Description: XRF analysis of geochemical composition of sediment from a single flood event around the Brantian. Most samples have depth of 0-2cm since this represents surface layer material, but at many sites there were also 'mini' cores which is why the depths vary. 28 elements analysed as above and each sample measured twice, with error columns and bulk particle size etc.</p><p>Number of fields: 70</p><p>Number of data rows: 222</p><p>Fields: </p><ul><li><b>Sample_Site</b>: Corresponds to sample site number in the locations tab (Field type: location)</li><li><b>Site_Type</b>: Site Type - U: Upstream site (sample taken at one of the upstream source sites 9 to 29) ; C: Core site (sample at a sediment core site which are only sites numbered 1 to 7); RC: Field replicate from the sediment core site itself; RL: Field replicate at that site but from a relatively lower elevation position; RH: Field replicate at that site but from a relatively higher elevation position) (Field type: categorical)</li><li><b>Upstream_Area_km2</b>: Total drainage area upstream of each sample site (Field type: numeric)</li><li><b>Sampling_date</b>: Date sediment sample taken (Field type: date)</li><li><b>Upper_Depth_cm</b>: Upper depth of sample slice relative to the sediment surface; for surface samples this will always be zero (Field type: numeric)</li><li><b>Lower_Depth_cm</b>: Lower depth of each sample slice relative to the sediment surface; in increments of 2cm (Field type: numeric)</li><li><b>BulkPS_<63um_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_63-125um_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_125um-1mm_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_1mm-2mm_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>BulkPS_>2mm_%</b>: % of bulk particle size distribution obtained by dry sieved mass (Field type: numeric)</li><li><b>XRF_sample_mass_g</b>: Mass of XRF sample analysed (Field type: numeric)</li><li><b>XRF_sample_thick_mm</b>: Thickness of prepared sample for XRF analysis (to nearest 0.5cm) (Field type: numeric)</li><li><b>EDXRF_measurement_number_1or2</b>: Refers to XRF measurement 1 or 2 - each sample was analysed in two different positions across the sample measurement surface producing two measurements of element concentration and 2SD error for each sample (Field type: categorical)</li><li><b>Mg</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Al</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Si</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>P</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>S</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>K</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ca</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ti</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>V</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cr</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Mn</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Fe</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ni</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cu</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Zn</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>As</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Rb</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Sr</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Zr</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cd</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Sn</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Sb</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Te</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Cs</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Ba</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Pb</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Th</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>U</b>: Total elemental concentration measured by ED-XRF (Energy Dispersive X-Ray Fluorescence) (Field type: numeric)</li><li><b>Mg.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Al.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Si.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>P.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>S.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>K.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ca.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ti.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>V.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cr.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Mn.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Fe.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ni.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cu.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Zn.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>As.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Rb.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Sr.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Zr.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cd.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Sn.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Sb.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Te.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Cs.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Ba.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Pb.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>Th.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li><li><b>U.Error</b>: Error for each measurement, equal to 2 times standard deviation (2 sigma) (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2013-06-21 to 2019-12-31</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Raw data of Antartic snow core BRTT6 UFRGS
<p>This spreadsheet contains the raw ion chromatography and resonant cavity spectrometry data resulting from the analysis of the snow core, BRTT-6, from the West Antarctica Ice Sheet.</p>
Model output data for compressible EULAG dynamical core in COSMO: convective-scale Alpine weather forecasts
<p>The archive contains model output data for article "Compressible EULAG dynamical core in COSMO: convective-scale Alpine weather forecasts" to be published in Monthly Weather Review.</p> <p>The article presents the semi-implicit compressible EULAGas a newdynamical core for convective-scale<br> numerical weather prediction. The core is implemented within the infrastructure of the<br> operational model of the Consortium for Small Scale Modeling (COSMO), forming the NWP<br> COSMO-EULAG model (CE). This regional high-resolution implementation of the dynamical<br> core complements its global implementation in the Finite-Volume Module of ECMWF’s Integrated<br> Forecasting System. The paper documents the first operational-like application of the dynamical<br> core for realistic weather forecasts. After discussing the formulation of the core and its coupling<br> with the host model, the paper considers several high-resolution prognostic experiments over<br> complex Alpine orography. Standard verification experiments examine the sensitivity of the CE<br> forecast to the choice of the advection routine and assess the forecast skills against those of the<br> default COSMO Runge-Kutta dynamical core at the 2.2 km grid showing a general improvement.<br> The skills are also compared using satellite observations for a weak-flow convective Alpine weather<br> case-study, showing favorable results. Additional validation of the new CE framework for partly<br> convection-resolving forecasts using 1.1 km, 0.55 km, 0.22 km, and 0.1 km grids, designed to<br> challenge its numerics and test the dynamics-physics coupling, demonstrates its high robustness in<br> simulating multi-phase flows over complex mountain terrain, with slopes reaching 85 degrees, and<br> the flow’s realistic representation.</p>
LA-ICP-MS line scan data and time-series analysis outputs for Baltic Sea sediment core F80
<p>The datafile contains two sheets: HTM and MCA, corresponding to geochemical data from the Holocene Thermal Maximum and Medieval Climate Anomaly intervals, respectively, of a sediment core from the Baltic Sea (site F80, 58°00.00N, 19°53.81E, water depth 191m, Fårö Deep, collected during the HYPER/COMBINE cruise of R/V Aranda, May/June 2009). In each sheet, columns A-J contain Laser Ablation (LA)-ICP-MS line scan data of element ratios in resin-embedded sediment (Mo/Al, Fe/Al and Br/P) presented in the time domain (Age in years BP). Dating of the sediment core is described in the accompanying manuscript and references therein. These profiles are presented in three forms: Raw= raw data resampled to 1 year resolution; Det= detrended and normalized to unit variance; Gau; Gaussian bandpass filter at a period of 20-100 years. Columns L-S contain time-series analysis results of the detrended, normalized elemental ratios in period domain, including power spectra of each ratio (Blackman-Tukey window, columns M-O) and cross-spectral analysis (Blackman-Tukey window, bandwidth 5 years) of Mo/Al vs Br/P (columns P-Q) and Mo/Al vs Fe/Al (columns R-S), respectively. All analyses were performed in Analyseries 1.1.1 (Paillard et al., 1996). Figures containing the data have been submitted as part of a manuscript to Geophysical Research Letters (Jilbert et al., forthcoming),</p> <p> </p> <p>Paillard, D., Labeyrie, L., & Yiou, P. (1996). Macintosh program performs time‐series analysis. <em>Eos, Transactions American Geophysical Union</em>,<em> 77</em>(39), 379-379. <a href="https://doi.org/10.1029/96EO00259">https://doi.org/10.1029/96EO00259</a></p> <p>Jilbert, T., Gustafsson, B.G., Veldhuijzen, S., Reed, D.C., van Helmond, N.A.G.M., Hermans, M., & Slomp, C.P (forthcoming). Iron-phosphorus feedbacks drive multidecadal oscillations in Baltic Sea hypoxia. Submitted to <em>Geophysical Research Letters</em></p>
Research data supporting "Surface dynamics and ligand-core interactions of quantum size photoluminescent gold nanoclusters"
<p>Experimental research raw data supporting the publication by Lin, Y. et al, 2018, Surface dynamics and ligand-core interactions of quantum sized photoluminescent gold nanoclusters, Journal of the American Chemical Society. DOI: 10.1021/jacs.8b04436</p> <p>Molecular simulation data is available upon reasonable request from irene.yarovsky@rmit.edu.au.</p>
Data from: Abundant-core thinking clarifies exceptions to the abundant-center distribution pattern
<p>Understanding variation in abundance within species' ranges is fundamental for ecological and evolutionary theory and applied conservation science. The abundant-center model provides a general hypothesis based on basic ecological principles and macroscale biogeographic patterns: abundance should peak near the center of a species' range, where environmental conditions are most favorable, and decline towards the periphery. Despite longstanding influence in ecological thinking, consistent support for the ubiquity of abundant-center distributions remains elusive, and recent assessments have questioned the value of this paradigm altogether. We suggest that revisiting the simplifying assumptions that underly the model provides a productive path forward by clarifying predictions and revealing expectations for alternative distribution patterns. Towards this end, we use standardized abundance surveys of North American birds to reassess the prevalence of abundant-center distributions in geographic and climate space, test whether deviations are associated with predictable violations of assumptions, and provide more robust expectations. After accounting for common methodological pitfalls, we find that geographic centrality is generally indicative of centrality in climate space (confirming a key model assumption) and that abundant-center distributions occurred in 71% of passerines. To better understand exceptions, we introduce the concept of abundant-core distributions, of which the abundant-center is a special case. We find that 87% of species fit abundant-core expectations, with abundances peaked and generally declining from a core region within the range. Abundance cores tended to deviate from geographic center where topographic features complicate correspondence between geography and environmental conditions (e.g. the climatically heterogenous West). Such deviations were often associated with truncated climatic availability, with core regions offset towards the continental edge or climate extremes. Overall, our analyses suggest that abundant-center thinking provides a useful generalization for understanding spatial variation in abundance for many species. However, as with any model, its assumptions must be assessed within the context of given applications.</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.