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18 results for “total organic carbon”
Dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) from river, lagoon, and open ocean sites along the Alaska Beaufort Sea coast, 2018-ongoing
Multiple water types (river, lagoon, ocean) from the North Slope of Alaska and nearshore Beaufort Sea are sampled seasonally by the Beaufort Lagoon Ecosystems LTER (BLE LTER) Core Program to investigate biogeochemical linkages between terrestrial, lagoon, and open ocean ecosystems. Water samples are collected during full ice cover (April), ice break-up (mid-June to early July), and open water (late July and August) periods and analyzed for dissolved organic carbon and total dissolved nitrogen content.
Total dissolved nitrogen (TDN), dissolved organic carbon (DOC), radiocarbon (14C-DOC), and stable carbon (13C-DOC) of surface waters from the Canning River watershed, 2019 and 2021
Sites along the Canning River mainstem and contributing streams near the Kavik River Camp, Alaska, were visited to track changes in stream and river total dissolved nitrogen (TDN) concentration, dissolved organic carbon (DOC) concentration, and the stable carbon (13C) and radiocarbon (14C) isotopic composition of DOC across transitions between the Brooks Range, Brooks foothills, and Arctic Coastal Plain. The dataset also includes water samples collected from lakes, springs, groundwater, and streams and rivers outside the Canning River watershed. Water samples were collected in late April and early August 2019 and in late July and early August 2021. Data include measurements of individual samples for TDN (milligrams nitrogen per liter), DOC (milligrams carbon per liter), carbon-13 of DOC (reported as delta-13C, per mil), carbon-14 of DOC (reported as fraction modern), and analytical error in the fraction modern values. Additional water chemistry data for these samples can be found in Koch et al. (2024). References: Koch, J. C., Connolly, C. T., Repasch, M., Best, H. R., Couvillion, C. S., Hunt, A. (2024). [Dataset] Hydrochemistry and age date tracers from springs, streams, and rivers in the Arctic National Wildlife Refuge, 2019-2022, U.S. Geological Survey data release, https://doi.org/10.5066/P95CXJIT.
PARAGON 1 - KM2112 - Total Organic Carbon In Situ Measurements
<p>This dataset contains measurements of total organic carbon concentrations (TOC) collected during the PARAGON 1 expedition (KM2112) in the North Pacific Subtropical Gyre. Measurements come from daily collections of whole seawater at 150 m using trace metal clean techniques. Samples for measurements of TOC concentrations were collected by aliquoting 40 mL of whole seawater into pre-combusted borosilicate vials. Samples were acidified with 27 µL of 12N HCl (Optima grade, Fisher), capped with Teflon-lined silicone septa lids, and stored in the dark at room temperature until analysis on shore. Total organic carbon concentrations were determined by high temperature combustion on a modified Shimadzu TOC analyzer according to Carlson et al. (2010). Timestamp is in UTC.</p>
PARAGON 1 - KM2112 - Total Organic Carbon Timecourse Incubations
<p>This dataset contains measurements of total organic carbon concentrations (TOC) collected during the PARAGON 1 expedition (KM2112) in the North Pacific Subtropical Gyre. Measurements come from time-course incubation experiments initiated with whole seawater collected at 150 m using trace metal clean techniques and modified with various additions of organic carbon, iron, and/or nitrogen. Samples for measurements of TOC concentrations were collected at multiple time points for each experimental biological replicate by aliquoting 40 mL of whole seawater from polycarbonate incubation bottles into pre-combusted borosilicate vials. Samples were acidified with 27 µL of 12N HCl (Optima grade, Fisher), capped with Teflon-lined silicone septa lids, and stored in the dark at room temperature until analysis on shore. Total organic carbon concentrations were determined by high temperature combustion on a modified Shimadzu TOC analyzer according to Carlson et al. (2010). Timestamp is in UTC. Version 2 corrects formatting errors in the timestamp.</p>
PARAGON 2 - KM2209 - Total Organic Carbon Timecourse Incubations
<p>This dataset contains measurements of total organic carbon concentrations (TOC) collected during the PARAGON 2 expedition (KM2209) in the North Pacific Subtropical Gyre. Measurements come from time-course incubation experiments initiated with whole seawater collected at 150 m using trace metal clean techniques and modified with various additions of organic carbon, iron, and/or nitrogen. Samples for measurements of TOC concentrations were collected at multiple time points for each experimental biological replicate by aliquoting 40 mL of whole seawater from polycarbonate incubation bottles into pre-combusted borosilicate vials. Samples were acidified with 27 µL of 12N HCl (Optima grade, Fisher), capped with Teflon-lined silicone septa lids, and stored in the dark at room temperature until analysis on shore. Total organic carbon concentrations were determined by high temperature combustion on a modified Shimadzu TOC analyzer according to Carlson et al. (2010). Timestamp is in UTC.</p>
Dissolved Organic Carbon (DOC) and Dissolved Total Nitrogen (DTN) from Northeast U.S. Shelf Long Term Ecological Research (NES-LTER) Transect cruises, ongoing since 2022
Dissolved organic carbon and dissolved total nitrogen are measured from discrete bottle samples collected during CTD rosette casts on Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) transect cruises (ongoing since 2022). Sampling frequency is approximately seasonal. Sample collection is paired with particulate organic carbon at surface, subsurface chlorophyll max, and sometimes a third depth. Samples are filtered directly from the CTD rosette and acidified in the field, then analyzed using a Shimadzu TOC-LCPH total organic carbon analyzer coupled to a TNM-L analyzer for total nitrogen. Values are reported in micromoles per liter.
Dissolved oxygen, temperature, chlorophyll-a, total phosphorus, total nitrogen, and dissolved organic carbon at multiple depths in 822 lakes from 1921-2022
Rapid changes in climate and land use are having substantial and interacting impacts on lake water quality around the world. Here, we synthesized time-series data for dissolved oxygen, temperature, chlorophyll-a, total phosphorus, total nitrogen, and dissolved organic carbon at multiple depths in 822 lakes to facilitate analyses of these changes. The dataset extends from 1921–2022, with a median data duration of 29 years (range 5-102) and a median of 5 unique sampling dates per year at each lake. Lakes in the dataset have a median depth of 12.5 m (range 1.5–480 m), median surface area of 85.4 ha (range: 0.5–237000 ha) and median elevation of 264 m (range: -215–2804). The lakes are located in 18 countries across 5 continents, with latitudes ranging from -42.6 to 68.3. To facilitate interoperability with other large-scale datasets, each lake is linked to a unique hydroLAKES lake ID when possible (n = 683).
Global derived datasets for use in k-NN machine learning prediction of global seafloor total organic carbon
<p>This dataset includes 663 predictor grids used for k-NN global prediction of seafloor total organic carbon.</p> <p>663 predictor grids available in netCDF4 HDF5 file format. Grids are cell-centered sized 4320 x 2160. File names adhere to the naming conventions discussed below. The naming structure is partioned by underscores and periods in the following order: interface to which the gridded values refer to, quantity of values contained within the grid, units and reference values/units (e.g. meters below sea level), data source, statistic calculated (if applicable), grid pitch, and file extension.</p> <p>Possible interfaces from the top – down:</p> <p>SS – Sea surface – atmosphere interface (may also be average of the entire water column)</p> <p>SF – Seafloor – water interface (may also be denoted by GL)</p> <p>GL – Ground level (e.g. bottom of pure liquid, top of dirt)</p> <p>SC – Sediment – crust interface (e.g. sediment above, igneous/metamorphic below)</p> <p>CM – Crust – mantle interface (e.g. Mohorovicic discontinuity)</p> <p>Appropriate reference naming marker (bold), original data source, and date of last access:</p> <p><strong>Becker</strong></p> <p>Becker, J. J., Wood, W. T., & Martin, K. M. (2014). <em>Global crustal heat flow using random decision forest prediction</em>, Abstract NG31A-3788 presented at 2014 Fall Meeting, AGU, San Francisco, California, U.S.A. Last access: 06/23/2015.</p> <p><strong>CRUST1</strong> </p> <p>Pasyanos, M.E., Masters, G., Laske, G. & Ma, Z. (2012). <em>LITHO1.0 - An Updated Crust and Lithospheric Model of the Earth Developed Using Multiple Data Constraints</em>, Abstract T11D-09 presented at 2012 Fall Meeting, AGU, San Francisco, California, U.S.A. Last access: 07/01/2014.</p> <p><strong>CRUST1_NOAA</strong></p> <p> As the NOAA sediment thickness database is globally not complete, data gaps in the NOAA grid with this have been supplemented by the CRUST1 sediment thickness (see above citation).</p> <p>Whittaker, J., Goncharov, A., Williams, S., Müller, R. D., & Leitchenkov, G. (2013) Global sediment thickness dataset updated for the Australian-Antarctic Southern Ocean, <em>Geochemistry, Geophysics, Geosystems. </em>https://doi.org/10.1002/ggge.2018.<em> </em>Last access: 09/02/2018.</p> <p><strong>GVP</strong></p> <p>Global Volcanism Program (2013) Volcanoes of the World. In E. Venzke (ed.). (Vol. 4.7.3). Smithsonian Institution. https://doi.org/10.5479/si.GVP.VOTW4-2013. Last access: 09/22/2014.</p> <p><strong>ETOPO2v2</strong></p> <p>National Geophysical Data Center (2006). 2-minute Gridded Global Relief Data (ETOPO2) v2. National Geophysical Data Center, NOAA. DOI: 10.7289/V5J1012Q. Last access: 02/06/2013.</p> <p><strong>PLATES</strong></p> <p>Coffin, M.F., Gahagan, L.M., & Lawver, L.A. (1998). Present-day Plate Boundary Digital Data Compilation. University of Texas Institute for Geophysics Technical Report (No. 174, pp. 5). Last access: 09/15/2014.</p> <p><strong>ONRL</strong></p> <p>Ludwig,W., Amiotte-Suchet, P., & Probst, J. L. (2011). ISLSCP II Global River Fluxes of Carbon and Sediments to the Oceans. In F. G. Hall, G. Collatz, B. Meeson, S. Los, E. Brown de Colstoun, and D. Landis (Eds.), <em>ISLSCP Initiative II Collection</em>. Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.3334/ORNLDAAC/1028. Last Access: 02/15/2015.</p> <p><strong>Muller</strong></p> <p>Müller, R. D., Sdrolias, M., Gaina, C., & Roest, W. R. (2008). Age, spreading rates, and spreading asymmetry of the world’s ocean crust, <em>Geochemistry, Geophysics, Geosystems</em>, 9(4), Q04006. https://doi.org/10.1029/2007GC001743. Last accessed: 07/19/2011.</p> <p><strong>Woa13x</strong></p> <p>Boyer, T.P., Antonov, J. I., Baranova, O. K., Coleman, C., Garcia, H. E., Grodsky, A., et al. (2013) World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), <em>NOAA Atlas NESDIS 72, Technical Ed</em>. Silver Spring, MD. http://doi.org/10.7289/V5NZ85MT. Last Access: 09/18/2014.</p> <p><strong>KIM</strong></p> <p>Kim, S.S. & Wessel, P. (2011). New global seamount census from the altimetry-derived gravity data, <em>Geophysical Journal International</em>, 186, 615-631. https://doi.org/10.1111/j.1365-246X.2011.05076.x. Last access: 09/22/2014.</p> <p><strong>HYCOM</strong></p> <p>The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data.Last access: 03/19/2014.</p> <p><strong>NCEDC</strong></p> <p>NCEDC (2016). Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC. Last access: 09/21/2014.</p> <p><strong>Wei2010</strong></p> <p>Wei, C.-L., Rowe, G. T., Escobar-Briones, E., Boetius, A., Soltwedel, T., Caley, M. J., et al.(2010). Global patterns and predictions of seafloor biomass using random forests. <em>PLoS ONE</em>,5(12), e15323. https://doi.org/10.1371/journal.pone.0015323 Last access: 06/20/2016.</p> <p><strong>NGA_egm2008</strong></p> <p>Pavlis, N.K., Holmes, S. A., Kenyon, S. C., & Factor, J. K. (2008). <em>The</em> <em>EGM2008 Global Gravitational Model</em>, Abstract 2008AGUFM.G22A..01P presented at the 2008 General Assembly of the European Geosciences Union, Vienna, Austria. Last access: 07/10/2014.</p> <p><strong>WAVEWATCH3</strong></p> <p>The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data. Last access: 03/19/2014.</p> <p>Updated global seafloor porosity grid using our k-nearest neighbors algorithm using 5 nearest neighbors. Observed data used for prediction from Martin et al. (2015). </p> <p>Martin, K. M., Wood, W. T., & Becker, J. J. (2015). A global prediction of seafloor sediment porosity using machine learning. <em>Geophysical Research Letters</em>, 42(24), 10640. https://doi.org/10.1002/2015GL065279</p> <p>Other grids which have been generated by empirical means are latitude (and derivatives), longitude (and derivatives), Coriolis, coast_is_1.0, and the random noise grids. </p> <p>Units referenced are as follows:</p> <p>KGM3 - kilogram per cubic meter<br> MS - meters per second<br> KM - kilometer<br> M_ASL - meters above sea level (i.e. meters referenced to sea level)<br> MWM2 - milliwatt per square meter<br> TGCYR - terragram of carbon per year<br> TGYR - terragram per year<br> MA - megaannum<br> M - meters<br> MGCM2 - milligram of carbon per square meter<br> DEG - degree<br> S - seconds</p> <p>Statistics grids are calculated within a given radius (e.g. 10km, 50km, 125km, 250km, 500km, 1000km) of the respective cell-centered value. The statistics grids include mean (.men), average absolute deviation from the mean (.aad), and the common logarithm (.log) of the absolute value of the mean (.mlg). Additionally, some grids are a weighted count for given radii (e.g. seamounts) where weight is a cosine taper from the center of the grid cell. </p> <p>The grid pitch for this dataset is uniformly at 5-arc minute denoted by “.5m”. Additionally, the extension used (netCDF4) is denoted by “.nc”.</p>
Total organic carbon, total nitrogen, and iron-bound organic carbon in surficial sediment and settling particulate material from Falling Creek and Beaverdam Reservoirs in 2019 and 2021
This dataset includes measurements of sediment properties (total organic carbon, total nitrogen, and iron-bound organic carbon) in surficial sediment and sedimenting material from two reservoirs: Falling Creek and Beaverdam Reservoirs, both located in Vinton, VA, USA. To measure surficial sediment properties, sediment cores were collected at the deepest site in each reservoir using a gravity corer, and the top 1 cm was frozen then lyophilized. Sediment cores were collected approximately once per month in both reservoirs throughout the stratified period (May–November) in 2019 and 2021, though sampling frequency and duration varied by reservoir and year. Sedimenting material was sampled using sediment traps suspended approximately 1 m above the sediment in both reservoirs. Iron-bound organic carbon was measured using the citrate-bicarbonate-dithionite method, and we used a CN analyzer (Elementar VarioMax, Ronkonkoma, NY, USA) to determine the amount of OC per unit mass of sediment.
Total dissolved organic carbon and nitrogen measurements at selected depths in the water column from CCE LTER process cruises in the California Current System, 2006 - 2021 (ongoing).
Water column bottle samples at multiple depths are taken during CCE Process cruises (since 2006, ongoing) at various CTD stations, and measurements of total organic carbon (TOC) and total nitrogen (TN) are performed onshore in the lab. TOC includes both dissolved and particulate organic carbon (DOC and POC, respectively). TN includes particulate and dissolved organic nitrogen as well as dissolved inorganic nitrogen species. In open ocean waters, POC is subtracted from TOC, and likely provides an accurate estimate of DOC because particles are typically small and homogeneously distributed in the sample. In coastal waters, and at stations where relatively high chlorophyll concentrations are present, the TOC measurement is not easily converted to DOC by subtracting POC values. Experience has shown that particles in these regions are large and inhomogeneously distributed. Therefore, samples collected in the CCE are reported as TOC and TN, expressed as micromoles of carbon (nitrogen) per liter of sea water.
Total dissolved organic carbon measurements at standard depths in the water column from nine CalCOFI cruises, 2008-2017.
Water column bottle samples at multiple depths are taken during CalCOFI cruises (since 2006, ongoing) at various CTD stations, and measurements of total organic carbon (TOC) are performed onshore in the lab. TOC includes both dissolved and particulate organic carbon (DOC and POC, respectively). In open ocean waters, POC is subtracted from TOC, and likely provides an accurate estimate of DOC because particles are typically small and homogeneously distributed in the sample. In coastal waters, and at stations where relatively high chlorophyll concentrations are present, the TOC measurement is not easily converted to DOC by subtracting POC values. Experience has shown that particles in these regions are large and inhomogeneously distributed. Therefore, samples collected in the CCE are reported as TOC, expressed as micromoles of carbon per liter of sea water.
Total data for global pattern of organic carbon pools in forest soil
<p>Understanding the mechanisms of soil organic carbon (SOC) sequestration in forests is vital to ecosystem carbon budgeting, and helps gain insight in the functioning and sustainable management of world forests. An explicit knowledge of the mechanisms driving global SOC sequestration in forests is still lacking because of the complex interplays between climate, soil and forest type in influencing SOC pool size and stability. Based on a synthesis of 1179 observations from 292 studies across global forests, we quantified the relative importance of climate, soil property and forest type on total SOC content and the specific contents of physical (particulate vs. mineral-associated SOC) and chemical (labile vs. recalcitrant SOC) pools in upper 10 cm mineral soils, as well as SOC stock in the O horizons. The variability in the total SOC content of the mineral soils was better explained by climate (47~60%) and soil factors (26%~50%) than by NPP (10~20%). The total SOC content and contents of particulate (POC) and recalcitrant SOC (ROC) of the mineral soils all decreased with increasing mean annual temperature because SOC decomposition overrides the C replenishment under warmer climate. The content of mineral-associated organic carbon (MAOC) was influenced by temperature, which directly affected microbial activity. Additionally, the presence of clay and iron oxides physically protected SOC by forming MAOC. The SOC stock in the O horizons was larger in the temperate zone and Mediterranean regions than in the boreal and sub/tropical zones. Mixed forests had 64% larger SOC pools than either broadleaf or coniferous forests, because of i) higher productivity, and ii) litter input from different tree species resulting in diversification of molecular composition of SOC and microbial community. While climate, soil and forest type jointly determine the formation and stability of SOC, climate predominantly controls the global patterns of SOC pools in forest ecosystems.</p>
Total data for global pattern of organic carbon pools in forest soil
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Dataset for the Global Prediction Of Total Organic Carbon In Marine Sediments Using Deep Neural Networks (nn-toc)
<p>The data folder contains the raw features and labels used for training machine learning models to predict total organic carbon in marine sediments. </p> <p>The data folder has three subfolders:</p> <ol> <li>raw : contains the labels, features and other data used to train the machine learnign models</li> <li>interim : transformed data, which has to be reproduced</li> <li>output : output from the models, used for analysis and visualisation</li> </ol> <p>The data folder has to be integrated in the Git repository nn-toc, to execute the code.</p> <p> </p> <p> </p>
Excess 210Pb, 137C, total organic carbon content, HBI biomarkers (IPSO25, HBI III) and biogenic silica of marine deposits from sediment core 2018_R2_2F from Sheldon Cove, Antarctic Peninsula
<p><strong>Description</strong>: Sediment core 2018_R2_2F was collected from Sheldon Cove, Antarctic Peninsula (67.55°S 68.27°W) from a water depth of 177 m, in December 2018 as part of expedition JR18003 by the British Antarctic Survey aboard RV James Clark Ross (Sands et al. 2019). Total core length was 25 cm. The dataset presented here consists of: gamma spectrometry measurements of excess 210Pb (calculated as a difference between the total 210Pb and the average of 214Pb and 214Bi) and 137Cs; total organic carbon (TOC) content; biogenic silica (BSi) content; and HBI biomarker (IPSO25, HBI III) concentrations. The excess 210Pb and 137Cs were measured at the Institute of Geology at Adam Mickiewicz University in Poznań, Poland, using a gamma detector Canberra BE3830, cooled with cryostat Cryo-Pulse®5 plus. The detector is placed in 10 cm thick lead shield walls and is equipped with a remote detector chamber option (RDC-6 inches) for low energy background reduction. The detector was commercially characterized by ISOCS (In-Situ Object Calibration Software) and LabSOCS (Laboratory Sourceless Object Calibration Software). Efficiencies for measured geometries were determined using LabSOCS code applying all corrections for sample geometry, matrix, and container type, and were verified with IAEA standards measurements. The results (spectra) were analyzed in Canberra GENIE-2000 v. 3.3 gamma spectrometry software and are presented with 2-sigma uncertainty ranges (Szczuciński, submitted). TOC concentrations (given in %) were measured at the Faculty of Earth Sciences, University of Silesia, Poland, using an Eltra CS-500 IRanalyzer with a Total Inorganic Carbon module according to the procedure described in Racka et al. (2010). TOC was calculated as the difference between TC (total carbon) and TIC (total inorganic carbon). Each TOC sample was analysed in duplicate. Analytical precision and accuracy were better than ±2% for TC and ±3% for TIC. HBI biomarker preparation and analysis followed slightly modified (Pieńkowski et al. 2021) standard protocols (Belt 2012). HBI concentrations are given per weight of sediment (ng/g sed), and organic carbon content (μg/g OC) (Belt et al. 2012). Biogenic (opaline) silica (BSi) analysis on dried, homogenised samples followed Heiri et al. (2001) and Bechtel et al. (2007). Each BSi and TOC sample was analysed in duplicate; values are given in %. BSi and TOC standard deviation calculations are based on data from the replication.</p> <p><strong>References</strong> <br><br>* Bechtel, A., Woszczyk, M., Reischenbacher, D., Sachsenhoffer, R., Gratzer, R., Püttmann, W. Spychalski, W., 2007: Biomarkers and geochemical indicators of Holocene environmental changes in Lake Sarbsko (Poland). Org. Geoch. 38, 1112–1131. <br>* Belt, S.T., Brown, T.A., Navarro Rodriguez, A., Cabedo Sanz, P., Tonkin, A., Ingle, R. 2012. A reproducible method for the extraction, identification and quantification of the Arctic sea ice proxy IP25 from marine sediments. Anal. Methods 4, 705-713. <br>* Heiri, O., Lotter, A. F., Lemcke, G., 2001. Loss on ignition as a method for estimating organic and carbonate content in sediments: reproducibility and comparability of results. J. Paleolimnol. 25, 101-110. <br>* Pieńkowski, A.J., Husum, K., Belt, S.T., Ninnemann, U., Köseoğlu, D., Divine, D.V., Smik, L., Knies, J., Hogan, K., Noormets, R. 2021. Seasonal sea ice persisted through the Holocene Thermal Maximum at 80°N. Commun. Earth Environ. 2, 124. <br>* Racka, M., Marynowski, L., Filipiak, P., Sobstel, M., Pisarzowska, A., Bond, D.P.G., 2010: Anoxic Annulata events in the Late Famennian of the Holy Cross Mountains (Southern Poland): geochemical and palaeontological record. Palaeogeography, Palaeoclimatology, Palaeoecology 297(3-4), 549-575. <br>* Sands, C.J., Annett, A., Apeland, B., Barnes, D.K.A, Bascur, M., Bruning, P., Costa, M., Dadd, G., De Lecea, A., Ensor, N., Featherstone, A., Flint, G., Goodger, D., Guzzi, A., Howard, F., Hunter, D., Jenkins, S., Kender, S., Lincoln, B., Munoz-Ramirez C., Pienkowski, A., Retallick, K., Roman-Gonzalez, A., Scourse, J., Sheen, K., Whitaker, T., Williams, J., Zhao, L., Zwerschke, N., 2019: JR18003 Cruise Report. British Antarctic Survey, 132 pp. <br>* Szczuciński, W. (submitted): Applications of gamma-emitting isotopes (210Pb and 137Cs) for assessment of sedimentary processes – insights from studies of lake, deltaic and continental shelf deposits. <br><br><strong>Projects</strong> <br><br>* CHARME: CHanging AntaRctic Marine Environments, <strong>Web</strong>: <a title="Follow link" href="https://charme.amu.edu.pl/" target="_blank" rel="nofollow noopener">https://charme.amu.edu.pl/</a>, <strong>Award</strong>: Norwegian Financial Mechanism 2014-2021, UMO-2020/37/K/ST10/04127 <br><br><strong>File descriptions</strong>: Excel file with all data, as well as core details (coordinates and water depth).</p> <p><strong>Comment</strong>: This dataset is related to the following article which has been accepted for publication:</p> <p>Pieńkowski, Anna J.; Szczuciński, Witold; Breszka, Agnieszka; Chyleński, Maciej; Juras, Anna; Romel, Paulina; Rozwalak, Piotr; Trzebny, Artur; Dabert, Mirosława; Belt, Simon; Jagodziński, Robert; Smik, Lukas; Włodarski, Wojciech. Sedimentary ancient DNA and HBI biomarkers as sea-ice indicators: a complementary approach in Antarctic fjord environments. Limnology Oceanography Letters. doi: 10.1002/lol2.10395</p>
Assembled file of spring annual averages of measures of total mesozooplankton organic biomass as carbon, in the California Current System, 1951 - 2008.
Reference for zooplankton organic carbon is Lavaniegos and Ohman (2007). More than 400 zooplankton types counted.
Global Prediction Of Total Organic Carbon In Marine Sediments Using Deep Neural Networks (nn-toc) v2
<p>This is the second version that was uploaded to make the code available for the paper submission <strong><span>NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks</span></strong> to the Geoscientific Model Development journal. Here we create a deep neural network based approach for the geospatial predicition of total organic carbon percentages in marine sediments.</p> <p><span>The data folder contains "raw" features and labels, "interim" data for preprocessed features and labels and "output"s produced from the model. While the preprocessed folder contain all the other files that can be produced by running the code. The features are in .nc or .grd file format. The other files are in .xyz or .csv file format.</span></p> <p> </p>
Trophic complexity in aqueous systems: bacterial species richness and protistan predation regulate dissolved organic carbon and dissolved total nitrogen removal
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.