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626 results for “Methanation”
PIE LTER time series of methane, CO2 and N2O ebullition measurements at four headwater streams in Massachusetts and New Hampshire.
Methane ebullition was monitored at four headwater streams during 2018 and 2019. Stationary bubble traps were deployed from approximately May through October. CC and SB were monitored in 2018 and 2019, while DB and CB were only monitored in 2019. 12 traps were deployed at CC, SB, and DB, and 9 traps were deployed at CB. The concentration measured in the emitted gas was multiplied by the volume measured in a trap to calculated the total methane flux via ebullition. The traps were visited at least once weekly. The mean, median, minimum, and maximum rate of ebullition across all traps at a site over a two week period are listed here. Relevant publications: Robison, A.L. (2021) Carbon emissions from streams and river: Integrating methane emission pathways and storm carbon dioxide emissions into stream and river carbon balances. Doctoral Dissertation. University of New Hampshire. Robison, A.L., W.M. Wollheim, B. Turek, C. Bova, C Snay, & R.K. Varner (in review). Spatial and temporal heterogeneity of methane ebullition in lowland headwater streams. Limnology and Oceanography.
PIE LTER dissolved methane and water temperature from four headwater streams in Massachusetts and New Hampshire.
Dissolved methane was measured in the surface water of four headwater streams during 2019. Relevant publications: A.L. Robison (2021) Carbon emissions from streams and river: Integrating methane emission pathways and storm carbon dioxide emissions into stream and river carbon balances. Doctoral Dissertation. University of New Hampshire. A.L. Robison, W.M. Wollheim, C.R. Perryman, A. Cotter, J.E. Mackay, R.K. Varner, P. Clarizia, and J.G. Ernakovich (in review). Dominance of diffusive methane emissions from lowland headwater streams promotes oxidation and isotopic enrichment. Frontiers in Environmental Science.
PIE LTER Methane isotopes (13C and D) for methane in sediments and dissolved in surface water from four headwater streams in Massachusetts and New Hampshire.
Gas samples for methane isotopes were collected from four headwater streams. Benthic gas samples were collected by physcially distrubing the sediment and collecting ebullated gas. Dissolved gas samples were extracted from surface water. 13C and deuterium isotopes were analyzed. Relevant publications: A.L. Robison (2021) Carbon emissions from streams and river: Integrating methane emission pathways and storm carbon dioxide emissions into stream and river carbon balances. Doctoral Dissertation. University of New Hampshire. A.L. Robison, W.M. Wollheim, C.R. Perryman, A. Cotter, J.E. Mackay, R.K. Varner, P. Clarizia, and J.G. Ernakovich (in review). Dominance of diffusive methane emissions from lowland headwater streams promotes oxidation and isotopic enrichment. Frontiers in Environmental Science.
Dataset for "Topography-based statistical modelling reveals high spatial variability and seasonal emission patches in forest floor methane flux"
<p>This dataset provides measured and upscaled forest floor methane (CH4) fluxes and soil moisture.</p> <p>This dataset is related to the following manuscript:</p> <p>Vainio et al., Topography-based statistical modelling reveals high spatial variability and seasonal emission patches in forest floor methane flux, Biogeosciences, in review. (The discussion preprint is available at https://doi.org/10.5194/bg-2020-263.)</p>
TCOM-CH4: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric methane profile dataset [1991-2021] constructed using machine-learning
<p>Methodology: </p> <p><span>he </span><strong><span>TOMCAT simulation</span></strong><span> was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized </span><strong><span>ERA-5 reanalysis data</span></strong><span>.</span></p> <h3><span>CH4 Profile Processing and Bias Correction</span></h3> <p><strong><span>Collocated CH4 profiles</span></strong><span> are organized into five distinct latitude bins:</span></p> <ul> <li> <p><strong><span>NH polar</span></strong><span>: </span><span><span><span><span><span>9</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N - </span><span><span><span><span><span>5</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>NH mid-lat</span></strong><span>: </span><span><span><span><span><span>2</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N - </span><span><span><span><span><span>7</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>Tropics</span></strong><span>: </span><span><span><span><span><span>4</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>4</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>N</span></p> </li> <li> <p><strong><span>SH mid-lat</span></strong><span>: </span><span><span><span><span><span>7</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>2</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S</span></p> </li> <li> <p><strong><span>SH polar</span></strong><span>: </span><span><span><span><span><span>9</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S - </span><span><span><span><span><span>5</span><span>0<span><span><span><span><span><span><span>∘</span></span></span></span></span></span></span></span></span></span></span></span><span>S</span></p> </li> </ul> <p><span>Initially, </span><strong><span>differences between TOMCAT and satellite measurements</span></strong><span> (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from </span><span><span><span><span><span>10</span><span>,</span><span><span>km</span></span></span></span></span></span><span> to </span><span><span><span><span><span>60</span><span>,</span><span><span>km</span></span></span></span></span></span><span>). It is important to note that unlike previous versions that might have used both HALOE and ACE measurements, this version exclusively utilizes </span><strong><span>ACE-FTS data</span></strong><span>, which is why the dataset starts from 2000.</span></p> <p><strong><span>Separate XGBoost regression models</span></strong><span> are then trained for these CH4 differences at each height level within a given latitude bin. These trained models are subsequently used to estimate </span><strong><span>CH4 bias corrections</span></strong><span> for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles.</span></p> <p><strong><span>Height-resolved CH4 profile data</span></strong><span> are then interpolated onto 28 standard pressure levels (from </span><span><span><span><span><span>300</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span> to </span><span><span><span><span><span>0.1</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span>), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions.</span></p> <h3><span>Data Files</span></h3> <p><span>The dataset includes two files containing daily mean zonal mean CH4 profiles:</span></p> <ul> <li> <p><code><span>zmch4_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</span></code><span>: Contains </span><strong><span>height level data</span></strong><span> (</span><span><span><span><span><span>10</span><span>,</span><span><span>km</span></span></span></span></span></span><span> to </span><span><span><span><span><span>60</span><span>,</span><span><span>km</span></span></span></span></span></span><span>).</span></p> </li> <li> <p><code><span>zmch4_TCOM_plev_T2Dz_2000-2024_V1.1.nc</span></code><span>: Contains </span><strong><span>pressure level data</span></strong><span> (</span><span><span><span><span><span>300</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span> to </span><span><span><span><span><span>0.1</span><span>,</span><span><span>hPa</span></span></span></span></span></span><span>).</span></p> </li> </ul> <h3><span>Reference Publication</span></h3> <p><span>This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication:</span></p> <p><span>Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, </span><a title="null" href="https://doi.org/10.5194/essd-15-5105-2023"><span>https://doi.org/10.5194/essd-15-5105-2023</span></a><span>, 2023.</span></p>
Daily European biospheric methane emissions estimated with the ecosystem model JSBACH-HIMMELI.
<p>Daily estimates of European biospheric methane emissions from JSBACH-HIMMELI model from year 1990 to year 2023. JSBACH-HIMMELI is an ecosystem process model based on JSBACH land surface model, YASSO soil carbon model and HIMMELI methane emission model. The gridded fluxes are available with a resolution of 0.1x0.1 degrees and in units of mol m-2 s-1 (m-2 refers to grid cell area). The gridded flux file contains a variable for methane fluxes, including a sum of methane fluxes from peatlands, inundated lands and mineral soils. More information of the model set-up is documented in Petrescu, A. M. R., et al., The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2019, Earth Syst. Sci. Data, 15, 1197–1268, https://doi.org/10.5194/essd-15-1197-2023, 2023, Tyystjärvi, V., 2024. Future methane fluxes of peatlands are controlled by management practices and fluctuations in hydrological conditions due to climatic variability. EGUsphere 1–37. https://doi.org/10.5194/egusphere-2023-3037 and Raivonen, M. et al., 2017. HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands. Geoscientific Model Development 10, 4665–4691. <a href="https://doi.org/10.5194/gmd-10-4665-2017">https://doi.org/10.5194/gmd-10-4665-2017</a></p>
Non-methane volatile organic compound emissions over China estimated using TROPOMI HCHO retrievals
<p>We used the Regional multi-Air Pollutant Assimilation System (RAPAS) with the EnKF algorithm to optimize daily NMVOC emissions in China by assimilating TROPOMI HCHO retrievals. </p><p>airqual.qc.csv includes assimilated and verified surface NO2 observations.</p><p>HCHO.tar.gz includes assimilated TROPOMI HCHO retrievals.</p><p>posterior_emission_27km.nc and posterior_emission_mg_27km.nc includes inferred daily posterior anthropogenic and biogenic NMVOC emissions respectively for August 2022.</p>
The SMaRP database: Sulfate, Methane and Related Parameters in marine sediments
<p>Global dataset on sulfate, methane and related parameters in marine sediments</p>
Net Methane Production Predicted by Patch Characteristics in a Freshwater Wetland
<p>Dataset supporting submitted research paper. "flux_patchdata<i>archive.csv" includes all plot-level data and is organized with sample locations and time in rows and data collected as headers in columns. "microbial</i>all.csv" includes only microbial taxonomic data collected from soils in each treatment. Other files include time series data from water level and temperature sensors deployed at each treatment (n=5). Refer to 'metadata.csv' for units and descriptions of data in each file. Data collected 2021-2022. Approved by all authors. </p>
Investigation of the post-2007 methane renewed growth with high-resolution 3-D variational inverse modelling and isotopic constraints - Input data
<p>This dataset contains all the input data utilized to perform the inversions in Thanwerdas et al. (2023).</p> <p>First, we store here some data used in the paper but originally generated for other studies. Because these original datasets did not have any DOI, the authors have graciously agreed to store their dataset here. Note that the paper associated to each dataset must be properly referenced if utilized.</p> <ul> <li><strong>Cl Concentrations - Wang et al. (2021).zip:</strong> Original Cl concentrations field from Wang et al. (2021). </li> <li><strong>CH4 Fluxes - Saunois et al. (2020).zip: </strong>Original CH4 fluxes used as prior data for the inversions performed as part of the Global Methane Budget 2000-2017 (Saunois et al., 2020).</li> </ul> <p>Second, we store the processed input data generated for the purpose of our study.</p> <ul> <li><strong>CH4 Fluxes - LMDz9696.zip:</strong> Aggregated CH4 fluxes remapped on LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>d13C Signatures - LMDz9696.zip:</strong> δ(13C, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>dD Signatures - LMDz9696.zip:</strong> δ(D, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.</li> <li><strong>OH O1D Concentrations - LMDz9696-INCA.zip:</strong> OH and O1D monthly concentrations simulated with LMDz-INCA.</li> <li><strong>Masks regions.zip</strong>: Masks for the regions used for the input data and the analysis.</li> </ul> <p> </p>
Data from: Soil incubation methods lead to large differences in inferred methane production temperature sensitivity
<p>Quantifying the temperature sensitivity of methane (CH4) production is crucial for predicting how wetland ecosystems will respond to climate warming. Typically, the temperature sensitivity (often quantified as a Q10 value) is derived from laboratory incubation studies and then used in biogeochemical models. However, studies report wide variation in incubation-inferred Q10 values, with a large portion of this variation remaining unexplained. Here we applied observations in Stordalen Mire, a thawing permafrost peatland, and a well-tested process-rich model, ecosys, to interpret incubation observations and investigate controls on inferred CH4 production temperature sensitivity. We developed a Field-Storage-Incubation (FSI) modeling approach to mimic the full incubation sequence, including field sampling at a particular time in the growing season,refrigerated storage, and the laboratory incubation process, followed by model evaluation. We found that CH4 production rates during incubation are regulated by seasonally-dependent substrate availability and active microbial biomass of key microbial functional groups. Applying a model sensitivity analysis, we found that storage duration, storage temperature, and field sampling time significantly affect CH4 production during incubation. Shorter storage duration and lower storage temperature led to larger CH4 production during incubation. Our findings revealed a wide range of inferred Q10 values (1.2 to 3.5), which we attribute to incubation temperatures, incubation duration, storage duration, and sampling time. Q10 of CH4 production is controlled by many interacting biological, biochemical, and physical processes, which cause the aggregated Q10 values to differ from those of the component processes. Terrestrial ecosystem models that use a constant Q10 value to represent temperature responses may therefore predict biased soil carbon cycling under future climate scenarios.</p> <p>This dataset includes all the data used to plot figures in the manuscript, including Fig.2-6 and Fig.S2-S11. Each sheet in the aggregated spreadsheet corresponds to one figure in the manuscript. The simulation experiment setup and analyses are thoroughly described in the manuscript. Here we provide a brief summary. The data includes field greenhouse gas observations and laboratory incubation measurements of CH4 production in Stordalen Mire. These datasets were already published and references were provided in the manuscript and spreadsheet. The data also includes simulation data, including modeled cumulative CH4 production, CH4 production rates, substrate concentrations, and active microbial biomass under different incubation temperature, sampling time and storage conditions. This data also includes inferred temperature sensitivity of CH4 production as Q10 values under different scenarios. Please refer to the manuscript for more detailed information.</p> <p>Please see "Related works" at the bottom of this page and the "References" tab in the spreadsheet for a full list of source datasets and associated publications.</p> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.</p> <p>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council’s grant 4.3-2021-00164. This research used resources of the National Energy Research Scientific Computing Center (NERSC) which is a U.S. Department of Energy Office of Science user facility. This research used the Lawrencium computational cluster resource provided by the IT Division at the Lawrence Berkeley National Laboratory (Supported by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231). Incubation and field observation data were collected under the IsoGenie Project, which was funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440.</p>
Land use change converts temperate dryland landscape into a net methane source
<p>Drylands cover approximately 40% of the global land surface and are thought to contribute significantly to the soil methane sink. However, large-scale methane budgets have not fully considered the influence of agricultural land use change in drylands, which often includes irrigation to create land cover types that support hay or grains for livestock production. These land cover types may represent a small proportion of the landscape but could disproportionately contribute to greenhouse gas exchange and are currently omitted in estimates of dryland methane fluxes. We measured greenhouse gas fluxes among big sagebrush, introduced wetlands, and hay meadows in a semi-arid temperate dryland in Wyoming, USA to investigate how these small-scale irrigated land cover types contributed to landscape-scale methane dynamics. Big sagebrush ecosystems dominated the landscape while the introduced wetlands and hay meadows represented 1% and 12% respectively. Methane uptake was consistent in the big sagebrush ecosystems, emissions and uptake were variable in the hay meadows, and emissions were consistent in the introduced wetlands. Despite making up 1% of the total land area, methane production in the introduced wetlands overwhelmed consumption occurring throughout the rest of the landscape, making this region a net methane source. Our work suggests that introduced wetlands and other irrigated land cover types created for livestock production may represent a significant, previously overlooked source of anthropogenic methane in this region and perhaps in drylands globally.</p>
Datasets for "Machine-Learning-Enhanced Symbolic Regression for Methane Storage Prediction in Covalent Organic Frameworks"
<p>This collection contains the datasets and associated files used in the research presented in the manuscript titled "Machine Learning-Enhanced Symbolic Regression for Methane Storage Prediction in Covalent Organic Frameworks". The datasets are critical for the development and validation of machine learning and symbolic regression models aiming to predict methane storage capacities in covalent organic frameworks (COFs).</p> <p><strong>Included Datasets:</strong></p> <ol> <li><code>COF_Data_for_ML.csv</code>: This dataset was utilized for the development of machine learning models.</li> <li><code>COF_Data_for_SISSO.csv</code>: This dataset was employed for the development of SISSO-based symbolic regression models.</li> <li><code>ML_vs_GCMC.xlsx</code>: This comparative dataset features GCMC-calculated results alongside machine learning predictions.</li> <li><code>Feature_Combination.xlsx</code>: This file contains data detailing all the feature combinations explored in the study.</li> <li><code>ML_SISSO_GCMC.xlsx</code>: This comparative dataset includes GCMC calculations, SISSO-based symbolic regression model predictions, and ML predictions.</li> <li><code>Crystallographic_Properties_of_535k_COFs.xlsx</code>: This consolidated dataset presents the crystallographic properties of 535,293 COFs.</li> </ol> <p><strong>Software Used:</strong></p> <ul> <li>Machine Learning Computations: Scikit-Learn (<a href="https://scikit-learn.org/stable/" target="_new">https://scikit-learn.org/stable/</a>)</li> <li>GCMC Simulations: RASPA2 (<a href="https://github.com/iRASPA/RASPA2" target="_new">https://github.com/iRASPA/RASPA2</a>)</li> <li>SISSO Calculations: SISSO toolkit (<a href="https://github.com/rouyang2017/SISSO" target="_new">https://github.com/rouyang2017/SISSO</a>)</li> <li>Crystallographic property calculations: Zeo++ (<a href="https://www.zeoplusplus.org/" target="_new">https://www.zeoplusplus.org/</a>)</li> </ul> <p>The datasets are provided to enable replication of the study's findings, encourage further research in the field, and facilitate the development of advanced predictive models by the scientific community. Researchers who use these datasets are requested to cite this Zenodo entry as well as the associated paper upon its publication.</p>
Figure 14 in A methane seep from the deep-marine, late Eocene Keasey Formation, Rock Creek, Columbia County, Oregon
Figure 14. Detail of chimney at the Second Seep Site, where the location of a solemyid bivalve in figure 12A is situated at periphery of seep limestone .
Figure 4 in A methane seep from the deep-marine, late Eocene Keasey Formation, Rock Creek, Columbia County, Oregon
Figure 4. Sketch of the Main Seep Site in east-west cross-sectional view. The sketch shows some representative positions of primary seep-related bivalves. Note relative positions of massive limestone in relation to areas dominated by nodules and carbonate blebs. Dashed symbol represents Keasey mudstone. A meter or more of black mudstone immediately subjacent to the limestone yield seep-related mollusks.
Figure 13 in A methane seep from the deep-marine, late Eocene Keasey Formation, Rock Creek, Columbia County, Oregon
Figure 13. Early diagenetic infilling of botryoidal aragonite in shell interior of the epitonid gastropod Boreoscala condoni (Dall, 1908). Note different size bubbles in successive layers. Scale bar = 1 cm.
Figure 3 in A methane seep from the deep-marine, late Eocene Keasey Formation, Rock Creek, Columbia County, Oregon
Figure 3. The Main Seep Site, east side, looking west. The numbers denote the top (1) and base (2) of the seep limestone body, respectively. The limestone is covered at top by approx. 1.8 m of Keasey mudstone, typical of middle member lithology..
Figure 12 in A methane seep from the deep-marine, late Eocene Keasey Formation, Rock Creek, Columbia County, Oregon
Figure 12. Solemyid bivalve preservation in the seep mudstone facies. A. Chalky remnant shell material on an articulated internal mold collected in life orientation. B. Fragments of chalky articulated internal molds and extremely thin shell fragments that exfoliated during collection. Scale bars = 1 cm.
Figure 8 in A methane seep from the deep-marine, late Eocene Keasey Formation, Rock Creek, Columbia County, Oregon
Figure 8. Select carbonate rock hand samples from the Main Seep Site, with some showing taphonomy of macrofossil occurrences. A. Articulated specimens of Conchocele taylori Hickman, 2015 on weathered (orange-brown stained) outer surface of medium gray colored microcrystalline carbonate; sample RC9(2). B. Articulated specimen of C. taylori with partial recrystallized shell, found in stratigraphic up position, and infilled with medium gray microcrystalline carbonate sample RC10B. C. Dark gray, homogeneous to slightly mottled microcrystalline carbonate with scattered, thin-shelled bivalve fragments; sample RC9. D. Irregular, coalesced nodular, medium to dark gray microcrystalline carbonate with scattered, straight bivalve shell fragments; sample RC10A. E. Irregularly nodular and mottled microcrystalline carbonate to cemented breccia of medium to dark gray microcrystalline carbonate with scattered, relatively thick, single curved valves of C. taylori shells; sample RC11. F. microcrystalline carbonate cemented, friable calcareous sandstone with nacreous molluscan shell fragments; sample RC21.
Figure 6 in A methane seep from the deep-marine, late Eocene Keasey Formation, Rock Creek, Columbia County, Oregon
Figure 6. Photographs of exposures. A. Upper turbidite bed with meter-stick for scale. Note sharp base of bed. B. Closeup of the base of the upper turbidite bed showing scour channel filled with medium sand. C. Rockfall in predominantly medium- to thick-bedded mudstones 1-3 meters below lower turbidite bed. Blocks in background are pieces of turbidite bed fallen from the bank. D. Lower turbidite bed at a point where it descends to near river level. E. Medium-bedded mudstones at base of stratigraphic section. Upper part of bank exposure is composed of Pleistocene fluvial gravels.
ScienceDex guides
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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.