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28 results for “biogeochemical cycles”
Model codes and simulation data for "Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)"
<p>ModelE-BiomeE v1.0 model codes and data This folder contains the simulation data and model codes that were used in the paper ‘Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS’s Earth system model (ModelE-BiomeE v.1.0)’ (https://doi.org/10.5194/gmd-2022-72). We included the data simulated by ModelE-BiomeE v.1.0 with settings of full demography (folder FullDemography) and single cohort (folder SingleCohort), and initial settings of land grids and vegetation data (folder GlobalVegetation). The codes include the full ModelE 2.1, module BiomeE files in ModelE, and the standalone BiomeE. In the folder FullDemography, we have 4 netcdf files for global output and 25 files for single grids output. The files ‘FullDM_2588_JAN.nc’ and ‘FullDM_2588_JUL.nc’ are the original model output of January and July in the year 2588. The file ‘FullDM_2588_Annual.nc’ is the yearly summary of model simulations. The file ‘FullDM_Selected.nc’ is an annual summary of 588 years of model simulation only with selected variables. The csv files are for single grids output at the time steps of daily and yearly. The last digit 1~8 represents the sites of 'BNC','MNT','HF','OKR','KZ','SV','WGK','TPJ', respectively (Table 1). Table 1 Site ID and file number ['BNC', 'MNT', 'HF', 'OKR', 'KZ', 'SV', 'WGK', 'TPJ'] ['8991', '8992', '8993', '8994', '8995', '8996', '8997', '8998'] ['8971', '8972', '8973', '8974', '8975', '8976', '8977', '8978'] ['8961', '8962', '8963', '8974', '8965', '8966', '8977', '8968'] Please refer to Table 2 in the paper for the detail of these 8 sites. ‘DailyLAIGPP.csv’ is a summary of all ‘DailyEcosystem’ files with LAI and GPP data. We included the Python scripts that can be used to generate the figures in out paper (Plotting-BiomeE-MsTMIP.py, Plotting-Scatter-Comparison.py, PlottingBiomeEMaps.py, and PlottingGridOutput.py). For the convenience of readers (in reproducing our figures), we included the summary of reanalysis of the data from observations and MsTMIP in folder ‘Sum-Obs-Simu’. Please refer to the original sources listed in our paper for the detail of these data.</p>
Data accompanying the manuscript "Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake", published in Limnology and Oceanography (doi: 10.1002/lno.12687)
<p>CTD and geochemical data accompanying the publication: Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake in Limnology & Oceanography (doi: 10.1002/lno.12687).</p>
Supplemental tables for a study of the seasonal Impacts of the Physical Environment on Biogeochemical Cycles in Arctic Lakes of the Mackenzie River Delta
<p>submitted abstract</p> <p>We conducted two- and six-year-long deployments of continuous water samplers (OsmoSamplers) and sensors (Temperature, pressure, light level, dissolved oxygen (DO) and conductivity) in nine lakes within the mid- to outer-delta region of the Mackenzie River and documented biogeochemical fluctuations (Mn, Fe, sulfate, and DO), defined physical processes that that drive such fluctuations, and constrained the impact of lake solutes on annual riverine fluxes. Five lakes were in the mid-delta region near Inuvik, NT, two lakes were in the outer delta, and two lakes were on the Arctic coastal plain and were not impacted by the Mackenzie River. In general, temperature minima occurred in September/October, indicative of ice formation, and distinct hydrostatic pressure (water level) anomalies occurred in May/June associated with ice breakup, lasting for days to months and impacting lake levels up to 4.2 m higher than “normal”. Such anomalies coincide with a dramatic change in solute concentrations. Systematic changes in solute concentrations indicate redox-driven biogeochemical reactions, salt exclusion during ice formation, and continuous to sporadic exchange of river water. Redox reactions were regulated by DO inputs stemming from atmospheric, photosynthetic, and riverine sources. During ice-covered periods dissolved sulfate may be conservative but was generally removed. Manganese and iron concentrations showed phases of production and removal during ice-covered periods, but both were produced overall. Calculated solute fluxes from lake waters alone to the Arctic Ocean may only impact yearly riverine fluxes for solutes that exceed ten times the river concentration prior to ice breakup (e.g., Mn and Fe).</p>
Code and data for "Contrasting upper and deep ocean oxygen response to protracted global warming," by Frölicher et al., Global Biogeochemical Cycles, 34, e2020GB006601: https://doi.org/10.1029/2020GB006601
<p>This file contains the data and python/NCL scripts that have been used for the analysis in this paper. </p>
Dataset for "The Role of Microbial Communities in Biogeochemical Cycles and Greenhouse Gas Emissions within Tropical Soda Lakes"
<p>Here, we make available 27 raw metagenomic files in fastq.gz associated to the article: "The Role of Microbial Communities in Biogeochemical Cycles and Greenhouse Gas Emissions within Tropical Soda Lakes". This files is not paired, with forward as _1.fastq.gz and reverse as _2.fastq.gz. The abstract of manuscript is described below:<br><br></p> <p>Abstract</p> <p>Although anthropogenic activities are the primary drivers of increased greenhouse gas (GHG) emissions, it is crucial to acknowledge that wetlands are a significant source of these gases. Brazil's Pantanal, the largest tropical inland wetland, includes numerous lacustrine systems with freshwater and soda lakes. This study focuses on soda lakes to explore potential biogeochemical cycling and the contribution of biogenic GHG emissions from the water column, particularly methane. Both seasonal variations and the eutrophic status of each examined lake significantly influenced GHG emissions. Eutrophic turbid lakes (ET) showed remarkable methane emissions, likely due to cyanobacterial blooms. The decomposition of cyanobacterial cells, along with the influx of organic carbon through photosynthesis, accelerated the degradation of high organic matter content in the water column by the heterotrophic community. This process released byproducts that were subsequently metabolized in the sediment leading to methane production, more pronounced during periods of increased drought. In contrast, oligotrophic turbid lakes (OT) avoided methane emissions due to high sulfate levels in the water, though they did emit CO2 and N2O. Clear vegetated oligotrophic turbid lakes (CVO) also emitted methane, possibly from organic matter input during plant detritus decomposition, albeit at lower levels than ET. Over the years, a concerning trend has emerged in the Nhecolândia subregion of Brazil's Pantanal, where the prevalence of lakes with cyanobacterial blooms is increasing. This indicates the potential for these areas to become significant GHG emitters in the future. The study highlights the critical role of microbial communities in regulating GHG emissions in soda lakes, emphasizing their broader implications for global GHG inventories. Thus, it advocates for sustained research efforts and conservation initiatives in this environmentally critical habitat.</p> <p><strong> </strong></p>
CESM2.2-8P4Z data supporting Yu et al. (2024): Simulating ecosystem dynamics and marine biogeochemical cycles with multiple plankton functional types
<p><span>This dataset contains the model output from CESM2.2-8P4Z, used in Yu et al. (2024) and</span><span> </span><span>submitted to</span><span> the Journal of Advances in Modeling Earth Systems (JAMES). These are the last 20-year averaged output files from 310 years of the model simulations, which are analyzed in Yu et al., (2024). CESM2.2-8P4Z contains twelve plankton groups, including eight types of phytoplankton:</span><span> </span><span>1</span><span>) picophytoplankton groups: <em>Prochlorococcus</em>, <em>Synechococcus</em>, picoeukaryotes and diazotrophs; 2) nanophytoplankton groups:</span><span> </span><em><span><em>P</em></span></em><em><span><em>haeocystis</em></span></em><span>, <em>coccolithophores</em></span><span> </span><span>and a generic other nanophytoplankton; 3) micro-sized phytoplankton: diatoms</span><span>; and four types of zooplankton:</span><span> </span><span>small microzooplankton (5-20 u</span><span>m, such as ciliates, nanoflagellates), large microzooplankton (20-200 u</span><span>m, such as copepod nauplii, small dinoflagellates etc.), mesozooplankton (200-2000 u</span><span>m, such as smaller copepod, large dinoflagellates) and macrozooplankton (>2000 u</span><span>m, such as larger copepod, krill).</span><span> </span><span>The MARBL-8P4Z model improves seasonal simulation of the spring bloom compared with more simplified MARBL configurations, benefiting from dampened diatom blooms at higher latitudes due to a combination of bottom-up and top-down drivers.</span></p>
The role of nitrogen and iron biogeochemical cycles in the production and export of dissolved organic matter in agricultural headwater catchments
<p>Data on soil solutions collected in the riparian area at 15 cm depth in an agricultural catchment in Brittany (France) during one hydrological cycle. Zero-tension lysimeters were collected at a fortnightly frequency from October 2022 to June 2023. Measurements inlcude dissolved organic matter concentration and composition (3D flurorescence), nitrates, iron, and phosphorus.</p> <p>Data are published in Lambert et al., 2014, The role of nitrogen and iron biogeochemical cycles in the production and export of dissolved organic matter in agricultural headwater catchments, doi.org/10.5194/egusphere-2024-1212 (preprint).</p>
A boreal forest model benchmarking dataset for North America: a case study with the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC)
<p>A boreal forest model benchmarking dataset for North America by harmonizing eddy covariance and supporting measurements from black spruce (Picea mariana)-dominated mature forest stands.</p> <p>Dataset glossary and users’ instructions are documented in ‘README.md’. </p>
Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle"
<p>Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle". </p> <p>Clerc, C., Bopp, L., Benedetti, F., Vogt, M., and Aumont, O.: Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1282, 2022.</p> <p>Three directories can be downloaded:</p> <p><strong>DataOBS</strong> : AtlantECO [WP2] – Traditional microscopy dataset – Thaliacea (Salpida+Doliolida+Pyromosomatida) abundance and biomass concentration data, presented in Clerc et al. (2022). </p> <p><strong>FigPaper </strong>: Source code and .nc files for the figures presented in Clerc et al. (2022) (https://doi.org/10.5194/egusphere-2022-1282). </p> <p><strong>MY_SRC_PISCES_NEMO_3.6 :</strong> Additional fortran routines for the compilation of PISCES-FFGM, the model developed for Clerc et al. (2022), from NEMO-3.6 (https://www.nemo-ocean.eu)</p>
Biogeochemical cycles in holm oak dehesas
<p><span>In anthropic savannah ecosystems from the Iberian Peninsula (i.e., dehesa), complex interactions between climate change, pathogen outbreaks and human land use are presumed to be behind the observed increase in holm oak decline. These environmental disturbances alter the plant-soil microbial continuum, which can destabilize the ecological balance that sustains tree health. Yet, little is known about the underlying mechanisms, particularly the directions and nature of the causal-effect relations between plants and soil microbial communities. </span></p> <p><span>In this study, we aimed to determine the role of plant-soil feedbacks in climate-induced holm oak decline in the Iberian dehesa. Using a gradient of holm oak health, we reconstructed key soil biogeochemical cycles mediated by soil microbial communities. We used quantitative microbial element cycling (QMEC), a functional gene-array-based high-throughput technique to assess microbial functional potential in carbon (C), nitrogen (N), phosphorous (P), and sulfur (S) cycling. </span></p> <p><span>The onset of holm oak decline was positively related with the increase in relative abundance of soil microbial functional genes associated with denitrification and phosphorous mineralization (i.e., <em>nirS3</em>, <em>ppx</em> and <em>pqqC</em>; parameter value: 0.21, 0.23 and 0.4; p<0.05). The structural equation model (ꭓ<sup>2</sup> = 32.26, p-value = 0.73), moreover, showed a negative association between these functional genes and soil nutrient availability (i.e., mainly mineral nitrogen and phosphate). Particularly, the holm oak crown health was mainly determined by the abundance of phosphate (parameter value=0.27; p-value<0.05) and organic phosphorus (parameter value=-0.37; p-value<0.5). </span></p> <div> <p><span>Hence, we propose a potential tree-soil feedback loop, in which the decline of holm oak promotes changes in the soil environment that trigger changes in key microbial-mediated metabolic pathways related to the net loss of soil N and P mineral forms. The shortage of essential nutrients, in turn, affects the ability of the trees to withstand the environmental stressors to which they are exposed. </span></p> </div>
Unveiling the link between Phytoplankton Molecular Physiology and Biogeochemical Cycling via Genome-Scale Modeling
<p>Data used for the manuscript "Unveiling the link between Phytoplankton Molecular Physiology and Biogeochemical Cycling via Genome-Scale Modeling"</p>
Significance of the terrestrial sink in the biogeochemical sulfur cycle
<p>An imbalance in pyrite weathering and burial is regarded as one of the primary mechanisms responsible for the oxygenation of the atmosphere and oceans, but key processes governing the terrestrial sulfur cycle remain nebulous. Here, we investigate components of the terrestrial sulfur cycle in a highly productive, glacier-fed catchment, and use a global mass balance model to put constraints on the riverine sulfur fluxes. Chemistry of stream water and plant debris in the Jostedal watershed, Norway suggests sulfur isotope discrimination is occurring in the porewater. Global models also corroborate additional, previously overlooked pyrite burial with a modest isotope fractionation (<20‰), similar to values reported from freshwater ecosystems. Collectively, our results support the notion that a significant amount of sulfate produced by weathering remains trapped in terrestrial environments. This terrestrial sulfur sink might have waxed and waned over geologic time in response to major biogeochemical events such as terrestrial afforestation.</p>
A combined microbial and biogeochemical dataset from high-latitude ecosystems with respect to methane cycle
<p><span>High latitudes are experiencing intense ecosystem changes with climate warming. The underlying methane (CH4) cycling dynamics remain unresolved, despite its crucial climatic feedback. Atmospheric CH4 emissions are heterogeneous, resulting from local geochemical drivers, global climatic factors, and microbial production/consumption balance. Holistic studies are mandatory to capture CH4 cycling complexity. Here, we report a large set of integrated microbial and biogeochemical data from 396 samples, using a concerted sampling strategy and experimental protocols. The study followed international standards to ensure inter-comparisons of data amongst three high-latitude regions: Alaska, Siberia and Patagonia. The dataset encompasses different representative environmental features (e.g. lake, wetland, tundra, forest soil) of these high-latitude sites and their respective heterogeneity (e.g. characteristic microtopographic patterns). The data included physicochemical parameters, greenhouse gas concentrations and emissions, organic matter characterization, trace elements and nutrients, isotopes, microbial quantification and composition. This dataset addresses the need for a robust physicochemical framework to conduct and contextualize future research on the interactions between climate change, biogeochemical cycles and microbial communities at high-latitudes.</span></p>
The role of external inputs and internal cycling in shaping the global ocean cobalt distribution: insights from the first cobalt biogeochemical model
<p>Model output for cobalt biogeochemistry model on ORCA2 grid.</p>
Biogeochemical cycles in holm oak dehesas
Open the record for dataset details and reuse information.
Significance of the terrestrial sink in the biogeochemical sulfur cycle
Open the record for dataset details and reuse information.
A combined microbial and biogeochemical dataset from high-latitude ecosystems with respect to methane cycle
Open the record for dataset details and reuse information.
Metagenome-assembled genomes for "Impacts of beaver ponds on biogeochemical cycling of organic nitrogen within a fire-impacted watershed"
<p>This dataset includes all of the metagenome-assembled genomes (MAGs) used in Roth et al.: "Impacts of beaver ponds on biogeochemical cycling of organic nitrogen within a fire-impacted watershed" (in prep.). The metagenomic sequencing was completed on a suite of sediment samples collected from the sediment-water interface of beaver ponds within wildfire burn scars.</p>
Biogeochemical data from diel cycles in a turbid-water pond and a clear-water pond in Brussels
<p><span>The dataset comprises one file containing geo-referenced information with corresponding timestamps. The names of the two ponds are written in French according to the official name defined by Brussels Environment (BE) (i.e. Leybeek and Silex).</span></p> <p><strong><span>Field sampling</span></strong></p> <p><span>Sampling was done every hour from a pontoon by collecting directly surface waters with 60ml polypropylene syringes for gases (CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O). Contents of the syringes were transferred with a silicone tube in 60 ml borosilicate serum bottles (Weathon) for CH<sub>4</sub> and N<sub>2</sub>O, poisoned with 200 µl of a saturated solution of HgCl<sub>2</sub>, and sealed with a butyl stopper and crimped with aluminium cap, without a headspace for further analysis at home laboratory. CO<sub>2</sub> measurements were carried out directly on the field with a Li-Cor Li-840 CO<sub>2</sub>/H<sub>2</sub>O gas analyser using the headspace technique by equilibrating four syringes with 30 mL of sample water and 30 mL of atmospheric air by vigorous shaking during 5 min (Borges et al., 2019). The Li-Cor Li-840 was calibrated before and after each cruise with ultrapure N<sub>2</sub> and a suite of gas standards (Air Liquide Belgium) with CO<sub>2</sub> mixing ratios of 388, 813, 3788 and 8300 ppm. The overall precision of pCO<sub>2</sub> measurements was ±2.0%. Water temperature, specific conductivity, and %O<sub>2</sub> were also measured every hour in-situ with VWR MU 6100H probe. 2L polyethylene containers were filled with water three to four times a day and processed at home laboratory for nutrients (soluble reactive phosphorus (SRP), ammonium (NH<sub>4</sub><sup>+</sup>), nitrate (NO<sub>3</sub><sup>-</sup>), and nitrite (NO<sub>2</sub><sup>-</sup>)), chlorophyll-<em>a</em> (Chl-<em>a</em>) and total suspended matter (TSM). </span></p> <p><strong><span>Meteorological data</span></strong></p> <p><span>Meteorological data including hourly air temperature, rainfall, wind speed and atmospheric pressure were retrieved online from </span><span><a href="https://wow.meteo.be/en"><span>https://wow.meteo.be/en</span></a></span><span> from the closest meteorological station of the two ponds (Institute of St-Lambert in Brussels, at 50.8408 °N, 4.4234 °E) located from 2.5km of Pêcheries pond and 5km from Silex pond.</span></p> <p><strong><span>CH<sub>4</sub> and N<sub>2</sub>O measurements by gas chromatography and </span></strong><strong><span>δ</span></strong><strong><sup><span>13</span></sup></strong><strong><span>C-CH<sub>4</sub> by <span>cavity ring-down spectrometry</span></span></strong></p> <p><span>Measurements of N<sub>2</sub>O and CH<sub>4</sub> concentrations dissolved in water and in the gas were made with the headspace technique (20ml of ultra-pure N<sub>2</sub>, Air Liquid Belgium, Weiss, 1981) and a gas chromatograph (GC) (SRI 8610C) with a flame ionisation detector for CH<sub>4</sub> and an electron capture detector for N<sub>2</sub>O calibrated with CO<sub>2</sub>:CH<sub>4</sub>:N<sub>2</sub>O:N<sub>2</sub> gas mixtures (Air Liquide Belgium) with mixing ratios of 1, 10 and 30 ppm for CH<sub>4</sub>, 404, 1018, 3961 ppm for CO<sub>2</sub>, and 0.2, 2.0 and 6.0 ppm for N<sub>2</sub>O. The precision of measurement based on duplicate samples was ±3.9% for CH<sub>4</sub> and ±3.2% for N<sub>2</sub>O.</span></p> <p><span>The </span><span>δ</span><sup><span>13</span></sup><span>C-CH<sub>4</sub> was measured in the headspace gas (20 ml of synthetic air, Air Liquid Belgium) equilibrated with the water sample (total volume 60 ml). The gas samples were diluted to achieve a final CH4 partial pressure below 10 ppm, aligning with the instrument's recommended operational concentration range. This prepared gas was then injected into a cavity ring-down spectrometer (G2201-I, Isotopic Analyzer, Picarro) equipped with a Small Sample Introduction Module 2 (SSIM, Picarro). The data were corrected using calibration curves of </span><span>δ</span><sup><span>13</span></sup><span>C-CH<sub>4</sub> as a function of concentration, based on two gas standards from Airgas Specialty Gases with certified </span><span>δ</span><sup><span>13</span></sup><span>C-CH<sub>4</sub> values of -23.9±0.3 ‰ and -69.0±0.3 ‰.</span></p> <p><strong><span>Chlorophyll-<em>a</em>, total suspended matter, and dissolved inorganic nutrients</span></strong></p> <p><span>Water was filtered through Whatman GF/F glass microfiber filters (porosity 0.7 µm) with a diameter of 47 mm for TSM and Chl-<em>a</em> determination. Chl-<em>a</em> was extracted from filters that were kept frozen before analysis (-20°C) with 90% acetone and concentrations was determined by fluorimetry (Kontron model SFM 25) (Yentsch and Menzel, 1963). Filters used for determination of TSM were pre-weighed before filtration and weighed after filtration of a known volume of water (after oven drying at 50°C). Filtered water was used for the determination of dissolved nutrients. NH<sub>4</sub><sup>+</sup> was measured by the nitroprusside-hypochlorite-phenol staining method (Grasshoff and Johannsen, 1972), NO<sub>2</sub><sup>-</sup> and NO<sub>3</sub><sup>-</sup> were measured before and after reduction of NO<sub>3</sub><sup>-</sup> to NO<sub>2</sub><sup>-</sup> by a cadmium-copper column, using the Griess acid reagent staining method (Grasshoff and Kremling, 2009), SRP was measured by the ammonium molybdate, ascorbic acid and potassium antimony tartrate staining method (Koroleff, 1983).</span></p> <p><strong><span>References</span></strong></p> <p><span>Borges AV, F Darchambeau, T Lambert, C Morana, G H Allen, E Tambwe, A Toengaho Sembaito, T Mambo, J Nlandu Wabakhangazi, J-P Descy, CR Teodoru, S Bouillon (2019) Variations in dissolved greenhouse gases (CO2, CH4, N2O) in the Congo River network overwhelmingly driven by fluvial-wetland connectivity, Biogeosciences, 16, 3801-3834. </span><span><a href="https://doi.org/10.5194/bg-16-3801-2019"><span>https://doi.org/10.5194/bg-16-3801-2019</span></a></span><span> </span></p> <p><span>Grasshoff, K., and Johannsen, H (1972). A new sensitive and direct method for the automatic determination of ammonia in sea water. ICES J. Mar. Sci. 34 (3), 516–521. </span><span><a href="https://doi.org/10.1093/icesjms/34.3.516"><span>https://doi.org/10.1093/icesjms/34.3.516</span></a></span><span>.</span></p> <p><span>Grasshoff, K., Kremling, K., and Ehrhardt, M. (2009). Methods of Seawater Analysis: Determination of Nitrite. </span><span>John Wiley & Sons.</span></p> <p><span>Koroleff, J. (1983). Determination of total phosphorus by alkaline persulphate oxidation. </span><span>Methods of Seawater Analysis. Verlag Chemie, Wienheim, pp. 136–138.</span></p> <p><span>Weiss, R. F. (1981). Determinations of carbon dioxide and methane by dual catalyst flame ionization chromatography and nitrous oxide by electron capture chromatography. <em>Journal of Chromatographic Science</em>, <em>19</em>(12), 611-616. </span><span><a href="https://doi.org/10.1093/chromsci/19.12.611"><span>doi.org/10.1093/chromsci/19.12.611</span></a></span><span> </span></p> <p><span>Yentsch, C. S., & Menzel, D. W. (1963). </span><span>A method for the determination of phytoplankton chlorophyll and phaeophytin by fluorescence. In <em>Deep Sea Research and Oceanographic Abstracts</em> (Vol. 10, No. 3, pp. 221-231). </span><span>Elsevier. </span><span><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/0011-7471(63)90358-9" target="_blank" rel="noopener"><span><span>https://doi.org/10.1016/0011-7471(63)90358-9</span></span></a><span> </span></span></p>
Variable Stoichiometry Effects on Glacial/Interglacial Ocean Model Biogeochemical Cycles and Carbon Storage - MODEL OUTPUT
<p>This is the repository for the model output and controls pertaining to the simulation experiments performed in "Variable Stoichiometry Effects on Glacial/Interglacial Ocean Model Biogeochemical Cycles and Carbon Storage" by Nathaniel Fillman, Andreas Schmittner, and Karin Kvale. Citation and DOI for parent publication will be updated here when available.<br> See https://github.com/fillmann/variable-stoichiometry for model code.</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.