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74 results for “Sediment Core”
Scanned data in support of "Paleomagnetic Study of Antarctic Deep-Sea Cores: Paleomagnetic study of sediments in a revolutionary method of dating events in Earth's History"
<p>Data files contain paleomagnetic data in an archival format, scanned into PDF documents. Data files contain 11 columns and include:</p> <p>Column 1) Sample identifier with cruise, core, and sample depth</p> <p>Column 2) Demagnetization field or temperature</p> <p>Column 3) Sample Magnetization</p> <p>Column 8) Declination</p> <p>Column 9) Inclination</p> <p> </p> <p>PDFs include paleomagnetic data for:</p> <p>V16-132</p> <p>V16-133</p> <p>V16-134</p> <p>V18-72</p> <p>V16-57</p> <p>V16-60</p> <p>V16-66</p>
Radiometric dating of sediment cores from three alpine lakes in Utah, United States, with stable isotope data
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Testing alternative hypotheses for the decline of cichlid fish in Lake Victoria using fish fossils time series from sediment cores
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Chemical and sediment characteristics of ice cores collected from the ablation zones of Canada, Commonwealth, Howard, Hughes, and Seuss Glaciers in the McMurdo Dry Valleys, Antarctica from 2015 to 2019
This data package contains chemical and sediment characteristics of ice cores collected from the ablation zones of five glaciers in Taylor Valley, located in the McMurdo Dry Valleys region of Antarctica, during the 2015-16, 2016-17, 2017-18, and 2018-19 austral summers. Specifically, shallow ice cores were collected from the ablation zones of Hughes, Howard, Seuss, Commonwealth, and Canada Glaciers in order to characterize the spatial and temporal evolution of ice chemistry and sediment concentration across Taylor Valley. Cores were collected in triplicate from each sampling location and measured 79 mm in diameter and up to 1 m in depth. Cores were sectioned in 5 cm (0-25 cm depth) and 25 cm increments (25 cm to the maximum depth of the core) prior to analyzing chemical and sediment characteristics.
Fertilized and reference tidal creek sediment chlorophyll a and pheophytin concentrations following whole core incubations, Ipswich and Rowley, MA
Salt marsh ecosystems serve as critical nutrient filters by removing reactive nitrogen (N) through denitrification. We examined the influence of long-term fertilization on N transformation and removal in a salt marsh tidal creek ecosystem fringing the Plum Island Sound estuary in northern Massachusetts, USA. Sediment oxygen demand was within the range of other marsh systems (1271.9 to 7855.0 µmol m-2 h-1) and was not significantly different between the fertilized and reference creek. Net N2 fluxes ranged from net N fixation of -402.7 µmol N2-N m-2 h-1 in the reference creek to net denitrification of 524.9 µmol N2-N m-2 h-1 in the fertilized creek. Net N2 flux and nitrate uptake were significantly higher in the fertilized creek, and in both creeks, net denitrification appeared to be nitrate limited. We calculated rates of dissimilatory nitrate reduction to ammonium (DNRA) and found it to be significantly higher in the fertilized creek, representing 45 and 11% of the total nitrate uptake in the fertilized and reference creeks, respectively. Additionally, there was a strong relationship between ammonium and nitrite fluxes in both creeks. These results suggest that DNRA may outcompete denitrification at high nitrate concentrations. Increased anthropogenic nutrient loading may therefore have a detrimental effect on the N removal capacity of salt marsh ecosystems. (From: Vieillard and Fulweiler (2012) Marine Ecology Progress Series 147: 11-22. DOI:10.3354/meps10013).
Sediment oxygen, di-nitrogen (gas), nitrate, nitrite, ammonium, phosphate, and silicate flux from sealed, whole sediment core incubations from a fertilized (Sweeney) and reference (West) creek in the Plum Island Estuary, Massachusetts.
Salt marsh ecosystems serve as critical nutrient filters by removing reactive nitrogen (N) through denitrification. We examined the influence of long-term fertilization on N transformation and removal in a salt marsh tidal creek ecosystem fringing the Plum Island Sound estuary in northern Massachusetts, USA. Sediment oxygen demand was within the range of other marsh systems (1271.9 to 7855.0 µmol m-2 h-1) and was not significantly different between the fertilized and reference creek. Net N2 fluxes ranged from net N fixation of -402.7 µmol N2-N m-2 h-1 in the reference creek to net denitrification of 524.9 µmol N2-N m-2 h-1 in the fertilized creek. Net N2 flux and nitrate uptake were significantly higher in the fertilized creek, and in both creeks, net denitrification appeared to be nitrate limited. We calculated rates of dissimilatory nitrate reduction to ammonium (DNRA) and found it to be significantly higher in the fertilized creek, representing 45 and 11% of the total nitrate uptake in the fertilized and reference creeks, respectively. Additionally, there was a strong relationship between ammonium and nitrite fluxes in both creeks. These results suggest that DNRA may outcompete denitrification at high nitrate concentrations. Increased anthropogenic nutrient loading may therefore have a detrimental effect on the N removal capacity of salt marsh ecosystems. (From: Vieillard and Fulweiler (2012) Marine Ecology Progress Series 147: 11-22. DOI:10.3354/meps10013).
Water column nitrate and ammonium concentrations, sediment oxygen, di-nitrogen (gas), nitrate, nitrite, ammonium, phosphate, and silicate flux from sealed, whole core incubations, Rowley River, Rowley, MA.
Tidal flats are critical components of coastal estuarine ecosystems characterized by high rates of benthic primary productivity and biogeochemical cycling. In order to investigate the impact of anthropogenic nutrient loading on tidal flat biogeochemistry we carried out a two-week fertilization experiment. Throughout the course of the study we conducted two light-dark, whole-core incubations and took measurements of three indicators of microphytobenthos activity in addition to quantifying the resident eastern mud snail (Ilyanassa obsoleta) population.
Lead 210 profiles in sediment cores in South Bay, Virginia
This dataset includes 210Pb (dpm g) and bulk carbon (mg/ml) data from four 20-cm sediment cores collected in South Bay, Virginia. Two of the cores were collected in 2011, one from within the restored Zostera marina (eelgrass) meadow and one from a nearby bare site. The other two cores were collected in 2014 from a different meadow site and a different bare site. Both meadow cores were collected within original restoration seed plots. The SB-5 bare core was collected north of the restored meadow. The 2-bare core was collected west of the meadow, near Man and Boy Channel. 210Pb and carbon profiles from the meadow sites were used to establish meadow carbon accumulation rates in 2011 and 2014, respectively. Bare areas in this system appear to be non-depositional. Excess 210Pb was not observed in the profiles from the two bare cores.
Validation of the UK'37 paleotemperature proxy in the South Brazilian Bight from core-top sediments
<p>Alkenone unsaturation index, water depth temperature, and nutrient concentration from the South Brazilian Bight (SBB). Temperature and nutrient content were retrieved from the World Ocean Atlas 2018. Residual analyses were performed using six paleoequations against observed temperature from WOA18. The PCA analysis was performed using alkenone unsaturation index, temperature, and nutrient data from each data point.</p>
Sedimentary structure discrimination with hyperspectral imaging in sediment cores
<p>The LDB17_P11Ax (IGSN: TOAE0000000243); Datation Age 1040 +/- 30 to 2017 CE by core correlation, 14C, lamina counting) core from the Bourget Lake (France) was analyzed in 2018 by hyperspectral imaging. We studied the potential of hyperspectral sensor to image a sediment cores and created machine learning models. The hyperspectral images were acquired in order to develop quantitative (estimating particle size and loss on ignition) and qualitative (detection of instantaneous events or lamina) methods.<br> All these methods allow to reconstruct the past environment and climate at high resolution (pixel size: 50-250 microns) and without destroying the sample for archiving for future analysis.<br> These images have been valorized in publications for the detection of instantaneous events with hyperspectral and combined with XRF data, for the combination of the two images into a composite image.<br> image (.hdr, .dat, .jpg)</p>
Individual glass and secondary standard analyses for tephra described in sediment cores from Skilak Lake, Alaska
<p>This dataset comprises individual glass shard and secondary standard analyses for tephra collected in sediment cores from Skilak Lake, Alaska. These geochemical analyses were used in the research article in Sedimentology titled “Unravelling a 2300 year long sedimentary record of megathrust and intraslab earthquakes in proglacial Skilak Lake, south-central Alaska” by Praet et al. (2022). DOI: <a href="https://doi.org/10.1111/sed.12986">10.1111/sed.12986</a></p>
Organic matter properties of the Rincon del Bonete and Palmar sediment cores (Uruguay)
<p>This database presents the results of organic matter measurements performed on cores collected in the Rincon del Bonete and Palmar reservoirs (Uruguay)</p> <p>IRMS analyses were conducted on dry sediment for determining organic matter properties, including elemental concentrations (Total Organic Carbon – TOC, Total Nitrogen –TN, both expressed in %) and stable isotope measurements (δ<sup>13</sup>C and δ<sup>15</sup>N, expressed in ‰). These measurements were performed with a continuous flow Elementar® VarioPyro cube analyzer coupled to a Micromass® Isoprime IRMS available at the Alysés platform of the Institut de Recherche pour le Développement (Bondy, France)</p> <p>Sediment cores were collected on 2019/09/01 in the Palmar (PA-02) and Rincon del Bonete (RDB-01) reservoirs (Uruguay).</p> <p>Corresponding authors: anthony.foucher@lsce.ipsl.fr</p>
Results of fallout radionuclides in the Rincon del Bonete and Palmar sediment cores (Uruguay)
<p>This database presents the results of fallout radionuclides performed on cores collected in the Rincon del Bonete and Palmar reservoirs (Uruguay) the 2019/09/01. Link to the publication https://www.nature.com/articles/s41893-023-01074-z</p> <p>Gamma spectrometry measurements were obtained using coaxial N- and P- type HPGe detectors (Canberra/Ortec) available at the Laboratoire des Sciences du Climat et de l’Environnement (Gif-sur-Yvette, France) . All activities were decay-corrected to 2020/06/01</p> <p>Corresponding authors: anthony.foucher@lsce.ipsl.fr</p> <p> </p>
Geochemical and isotopic analyses of a ~14 000 year old sediment core collected from Lake Simcoe, Canada
<p>This dataset contains measurements of modern water and ancient core materials from Lake Simcoe, the fourth largest lake wholly in Ontario, Canada. These data consist of: <em>(i)</em> oxygen, hydrogen and carbon isotope (<em>δ</em><sup>18</sup>O, <em>δ</em><sup>2</sup>H and <em>δ</em><sup>13</sup>C) compositions for modern water samples; <em>(ii)</em> physical measurements of one piston core, PC-5; <em>(iii) δ</em><sup>13</sup>C and <em>δ</em><sup>18</sup>O values of ostracods collected from PC-5, and <em>(iv) δ</em><sup>13</sup>C and <em>δ</em><sup>18</sup>O values of ancient DIC and water, respectively, inferred from item <em>(iii)</em>. Physical measurements performed on core PC-5 include magnetic susceptibility, mineralogy and grain size. Mass accumulation rates are also reported.</p>
SedDNA results along the sediment core of the Dzoumogné reservoir, Mayotte Island, Comoros Archipelago, France
<p>This repository is made up of two main folders containing information about the metadata and the database variables:</p> <ul> <li><em>Metadata_SedDNA_dzoumogne_reservoir_afoucher.xlsx</em> (description of the metadata associated with the sediment samples)</li> <li><em>Variable_description_dzoumogne_reservoir_afoucher.xlsx</em> (description of the database variables)</li> </ul> <p>as well as 6 folders containing the data sets for each genetic marker:</p> <ul> <li><em>Balitzki_12S-afoucher.xlsx</em></li> <li><em>Hardy_18S-afoucher.xlsx</em></li> <li><em>Kelly_16S-afoucher.xlsx</em></li> <li><em>Pornon_ITS-afoucher.xlsx</em></li> <li><em>Prosser_rtbl-afoucher.xlsx</em></li> <li><em>White_ITS-afoucher.xlsx</em></li> </ul> <p>Related publication: Foucher et al. submitted</p> <p>---------------------------------------------------------</p> <p><strong>Context:</strong> Sediment DNA was extracted from 12 layers along a sediment core (<strong>IGSN number: 10.58052/IEFOU0003</strong>) collected in 2021 in the Dzoumogné reservoir (Mayotte Island, Comoros Archipelago, France), covering the 2012-2021 period. The aim of these analyses was to evaluate the consequences of landscape defragmentation, induced by deforestation and intensification of agricultural practices, on changes in biological communities.</p> <p><strong>Sample collection, storage and subsampling:</strong> Sediment core was collected using an Uwitec gravity corer equipped with a 63mm PVC liner. The core was stored at 4°C in a cold and dark room for 3 weeks prior to subsampling. The sediment core was opened and subsampled in a previously sterilized hoot (DNA cleaner and UV) in a dedicated biological room at the University of Paris-Saclay. Each sample was extracted from the sediment core using sterilized metal plates inserted on either side of the layer to be sampled (the central part of this sample was then sub-sampled to remove the part of the sediment in contact with the PVC tube of the core and the cutting tools). During this process the operators were equipped with gloves, masks and headgear to avoid any risk of contamination. A blank was placed in the fume cupboard to control contamination during core opening and sampling.</p> <p><strong>Extraction and assignation:</strong><em> Sed</em>DNA was retrieved using a standardized protocol that combines the use of commercial kits (Nucleospin soil – Macherey-Nagel) and the extracellular DNA pre-treatment described in Taberlet et al. (1). In order to limit amplification biases and to improve the metabarcoding sensitivity, a combination of 7 genetic markers was used to PCR-amplify each targeted taxonomic group (plants - 50-70 bp – trnL (2), plants - 150 bp – rbcL (3), plants/fungi – 320 bp –ITS (4), plants – 380 bp – ITS , metazoa – 200 bp – 16S (5), Eukaryotes – 200 bp – 18S (6), vertebrates – 200 bp – 12S (7)). PCRs were realized in octuplicates with positive and negative controls. Extraction controls were also amplified with the dedicated primers. PCR products were cleaned using the MinElute purification kit (QIAGEN) and all purified amplicons were pooled in equimolar concentrations at the exception of the extraction and the PCR negative controls as they were not concentrated enough. Then, each sample pool was run on an Agilent Bioanalyzer using the DNA High Sensitivity LabChip kit (Agilent Technologies, Santa Clara, CA, USA) to verify amplicon length and each sample was finally quantified with a Qubit fluorometer (Invitrogen, Carlsbad, CA, USA). <br>Libraries were generated with a PCR-free protocol in order to tremendously reduce the number of chimeras induced by the metabarcoding approach. One microgram of each purified PCR products pool was submitted to molecular end-repair and then ligated to Illumina adapters prior to sequencing on Illumina platforms (Next-Generation Sequencing; Fasteris, Switzerland) using a NovaSeq instrument (Illumina, San Diego, CA, USA) with a paired-end read mode covering the amplicon lengths. The objective was to obtain approximately 1 million reads per marker and per sample. <br>Bioinformatics analyses were performed using the OBITools (8) and QIIME2 (9) pipelines. Reads were first demultiplexed using OBITOOLS. Then erroneous sequences were removed, and singletons were filtered out. Sequences were then clustered at 97% identity using VSEARCH (10). Sequences that were found in the extraction blanks were removed from the downstream analysis. Taxonomic assignment was realized in two steps. First, sequences were assigned by blasting them against a custom database generated from the GenBank latest release curated and restricted to all the documented taxa. Sequences with over 95% identity match were kept and their last common ancestor was computed using MEGAN (11). A second assignment pass was made using a Naïve Bayesian Classifier using QIIME’s rescript plugin (12).<em><br><br></em></p> <p><strong>Data validation:</strong> Discuss in the related paper.</p> <p> ----------------------------------------------------------</p> <p>References:</p> <p><br>1. P. Taberlet, S. M. Prud’Homme, E. Campione, J. Roy, C. Miquel, W. Shehzad, L. Gielly, D. Rioux, P. Choler, J. C. Clément, C. Melodelima, F. Pompanon, E. Coissac, Soil sampling and isolation of extracellular DNA from large amount of starting material suitable for metabarcoding studies. Mol. Ecol., doi: 10.1111/j.1365-294X.2011.05317.x (2012).<br>2. P. Taberlet, E. Coissac, M. Hajibabaei, L. H. Rieseberg, Environmental DNA. Mol. Ecol. 21, 1789–1793 (2012).<br>3. S. W. J. Prosser, P. D. N. Hebert, Rapid identification of the botanical and entomological sources of honey using DNA metabarcoding. Food Chem. 214, 183–191 (2017).<br>4. T. J. White, T. J. White, T. Bruns, S. Lee, J. Taylor, Amplification and Direct Sequencing of Fungal Ribosomal RNA Genes for Phylogenetics (Academic press, New York, USA, 1990).<br>5. R. P. Kelly, Making environmental DNA count. Mol. Ecol. Resour. 16, 10–12 (2016).<br>6. C. M. HARDY, E. S. KRULL, D. M. HARTLEY, R. L. OLIVER, Carbon source accounting for fish using combined DNA and stable isotope analyses in a regulated lowland river weir pool. Mol. Ecol. 19, 197–212 (2010).<br>7. B. Balitzki-Korte, K. Anslinger, C. Bartsch, B. Rolf, Species identification by means of pyrosequencing the mitochondrial 12S rRNA gene. Int. J. Legal Med. 119, 291–294 (2005).<br>8. F. Boyer, C. Mercier, A. Bonin, Y. Le Bras, P. Taberlet, E. Coissac, obitools : a unixinspired software package for DNA metabarcoding. Mol. Ecol. Resour. 16, 176–182 (2016).<br>9. E. Bolyen, J. R. Rideout, M. R. Dillon, N. A. Bokulich, C. C. Abnet, G. A. Al-Ghalith, H. Alexander, E. J. Alm, M. Arumugam, F. Asnicar, Y. Bai, J. E. Bisanz, K. Bittinger, A. Brejnrod, C. J. Brislawn, C. T. Brown, B. J. Callahan, A. M. Caraballo-Rodríguez, J. Chase, E. K. Cope, R. Da Silva, C. Diener, P. C. Dorrestein, G. M. Douglas, D. M. Durall, C. Duvallet, C. F. Edwardson, M. Ernst, M. Estaki, J. Fouquier, J. M. Gauglitz, S. M. Gibbons, D. L. Gibson, A. Gonzalez, K. Gorlick, J. Guo, B. Hillmann, S. Holmes, H. Holste, C. Huttenhower, G. A. Huttley, S. Janssen, A. K. Jarmusch, L. Jiang, B. D. Kaehler, K. Bin Kang, C. R. Keefe, P. Keim, S. T. Kelley, D. Knights, I. Koester, T. Kosciolek, J. Kreps, M. G. I. Langille, J. Lee, R. Ley, Y.-X. Liu, E. Loftfield, C. Lozupone, M. Maher, C. Marotz, B. D. Martin, D. McDonald, L. J. McIver, A. V. Melnik, J. L. Metcalf, S. C. Morgan, J. T. Morton, A. T. Naimey, J. A. Navas-Molina, L. F. Nothias, S. B. Orchanian, T. Pearson, S. L. Peoples, D. Petras, M. L. Preuss, E. Pruesse, L. B. Rasmussen, A. Rivers, M. S. Robeson, P. Rosenthal, N. Segata, M. Shaffer, A. Shiffer, R. Sinha, S. J. Song, J. R. Spear, A. D. Swafford, L. R. Thompson, P. J. Torres, P. Trinh, A. Tripathi, P. J. Turnbaugh, S. Ul-Hasan, J. J. J. van der Hooft, F. Vargas, Y. Vázquez-Baeza, E. Vogtmann, M. von Hippel, W. Walters, Y. Wan, M. Wang, J. Warren, K. C. Weber, C. H. D. Williamson, A. D. Willis, Z. Z. Xu, J. R. Zaneveld, Y. Zhang, Q. Zhu, R. Knight, J. G. Caporaso, Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).<br>10. T. Rognes, T. Flouri, B. Nichols, C. Quince, F. Mahé, VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).<br>11. D. H. Huson, A. F. Auch, J. Qi, S. C. Schuster, MEGAN analysis of metagenomic data. Genome Res. 17, 377–386 (2007).<br>12. M. S. Robeson, D. R. O’Rourke, B. D. Kaehler, M. Ziemski, M. R. Dillon, J. T. Foster, N. A. Bokulich, RESCRIPt: Reproducible sequence taxonomy reference database management. PLOS Comput. Biol. 17, e1009581 (2021).</p>
Sediment core dataset for "Shift away from Nile incision at Luxor ~4,000 years ago impacted ancient Egyptian landscapes"
<p>This dataset contains all sedimentary core information used for reconstructing the Holocene evolution of the Nile Valley landscape near Luxor in Egypt.</p> <p>Sedimentary information from 81 sediment cores retrieved by a combination of hand-operated Eijkelkamp augers and a gasoline-powered Cobra TT percussion corer was used to investigate the Nile’s Holocene fluvial deposits in its valley near Luxor, Egypt. Sediment samples were studied in ~10 cm intervals and had their characteristics such as sedimentary texture (conforming to USDA standards), grain size, Munsell colour, degree of sorting, mica occurrence and rhizolith percentages logged on-site. Boreholes reached to an average depth of ~8 m — with many penetrating >10 m. Their spacing varied from ~20 to 200 m, depending on the heterogeneity of the subsurface. The cross-section was strategically placed to span the entire valley, perpendicular to the main axis of the Nile Valley and the current river, while following governmental policies and regulatory procedures working in and around Egyptian Antiquities areas. Coring locations were recorded in UTM36N and the Survey of Egypt vertical datum using a Leica RTK-GNSS positioning system, and subsequently stored together with the sedimentary logs for future reference. Subsequently, UTM36N coordinates were converted to degrees, minutes, seconds for publication purposes. </p> <p>This dataset consists of one Microsoft Excel workbook:</p> <p>1_sedimentary_core_dataset.xlxs</p> <p>Further details can be found in Peeters et al. "Shift away from Nile incision at Luxor ~4000 years ago impacted ancient Egyptian landscapes" (Nature Geoscience, 17, July 2024, p. 645–653, <a href="https://www.nature.com/articles/s41561-024-01451-z" target="_blank" rel="noopener">https://www.nature.com/articles/s41561-024-01451-z</a>). </p> <p>Contact information:</p> <p>Dr Angus Graham (angus.graham@arkeologi.uu.se)</p> <p>Dr Jan Peeters (j.peeters1.uu@gmail.com)</p>
Dataset of sediment cores and box cores collected in Central Basin, Ross Sea (Antarctica)
<p>dataset of sediment cores and box cores collected in Central Basin, Ross Sea (Antarctica)</p>
Holocene and glacial individual foraminiferal analyses (IFA) of stable isotopes in Globigerinoides ruber tests from Line Islands sediment cores (central equatorial Pacific)
<p>This dataset contains individual foraminiferal analyses (IFA) stable isotopic (δ¹⁸O and δ¹³C) measurements of planktic foraminifera <em>Globigerinoides ruber</em> tests from modern and Last Glacial Maximum (LGM; ~20 ka) sediments from offshore the Line Islands, located in the central equatorial Pacific Ocean.</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>
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>
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