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1,342 results for “aerosol”
Data for "Improved constraints on hematite refractive index for estimating climatic effects of dust aerosols"
<p>This repository contains calculated/simulated data on the imaginary part of the complex refractive index, single scattering albedo, and/or optical depth for dust aerosols in the visible band or at the wavelength of 550 nm.</p> <p>For detailed information on (1) the acquisition and utilization of this data, (2) comprehensive configurations for model simulations, (3) the principal findings, and (4) the methodology employed to achieve these findings, please refer to the article authored by Li, Mahowald et al. (2024; Commun. Earth Environ).</p> <p>Other datasets, including the code and laboratory observations presented in the paper, can be found elsewhere (refer to the Data and Code Availability sections of the paper).</p> <p>For any clarification regarding the data and code, inquiries related to the publication, or potential collaboration, please contact Longlei Li (<a href="mailto:ll859@cornell.edu">ll859@cornell.edu</a>) or Natalie M. Mahowald (<a href="mailto:mahowald@cornell.edu">mahowald@cornell.edu</a>).</p>
Balloon-borne Aerosol-Cloud Interaction Studies (BACIS): Field campaigns to understand and quantify aerosol effects on clouds
<p>The dataset uploaded on Zenodo consists of in-situ measurements from specialised balloon borne sondes used in the field campaign named BACIS (Balloon borne Aerosol Cloud Interaction Studies) conducted from Gadanki, a location in Southern Peninsular India. Note that raw data from the observations is only uploaded here. There is lot more post processing of data carried out during our analysis for brining out meaningful data. Hence the users are cautioned in using the raw data for analysis and kindly advised to approach us for the post processed, quality controlled data. </p>
LOAC in situ profiles of aerosol concentrations in la Reunion Island in 2015
<p>Concentration profiles gave been obtained from balloon-borne observations. They are provided for 19 aerosol size classes (between 0.2 and ~50 µm) from the surface to the stratosphere. 2 flights are provided here: 19/05/2015 and 19/08/2015.</p>
MSR Simulation With cGEMS: Fission Product Release And Aerosol Formation
<p>The release of fission products and fuel materials from a molten salt fast reactor fuel in hypothetical accident conditions was investigated. The molten salt fast reactor in this investigation features a fast neutron spectrum, operating in the thorium cycle, and it uses LiF-ThF4-UF4 as a fuel salt. A coupling between the severe accident code MELCOR and thermodynamical equilibrium solver GEMS, the so-called cGEMS, with the updated HERACLES database was used in the modeling work. The work was carried out in the frame of the EU SAMOSAFER project. At the beginning of the simulation, the fuel salt is assumed to be drained from the reactor to the bottom of a confinement building. The containment atmosphere is nitrogen. The fission products and salt materials are heated by the decay heat, and due to heating, they are evaporated from the surface of a molten salt pool. The chemical system in this investigation included the following elements: Li, F, Th, U, Zr, Np, Pu, Sr, Ba, La, Ce, and Nd. In addition to the release of radioactive materials from the fuel salt, the formation of aerosols and the vapor phase species in the modeled confinement were determined.</p>
Chemical composition and sources of organic aerosol on the Adriatic coast in Croatia
<p>Air pollution studies are still scarce in some areas in Europe like around the Adriatic Sea. Source apportionment of the fine particulate (PM<sub>2.5</sub>) organic aerosol (OA) was conducted near Rogoznica, a small touristic settlement on the Eastern Adriatic coast of Croatia, near Lake Rogoznica (43.53° N, 15.95° E). Filter-based offline analyses of PM by a high resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) were used to apportion OA to its sources. We quantified the contributions of fresh biomass burning OA (BBOA) and three oxygenated OA (OOA), denoted as background OOA (bkgOOA), summer OOA (SOOA), and sulfur-containing OOA (SC-OOA). The bkgOOA component correlates with anthropogenically influenced secondary inorganic aerosol constituents (e.g. sulfate) and dominates OA both during the warm and cold seasons (44%). It exhibits characteristics of regional SOA and there are indications that during the warm season SOA from wildfires could be substantial contributors. EESI-TOF measurements of the levoglucosan related ion showed a high correlation with the BBOA factor. Secondary OA has a similar molecular composition during the cold and warm seasons – in line with the large contribution of bkgOOA throughout the investigated seasons. SOOA comprises 19% of total OA and increases exponentially with the local temperature, consistent with SOA production by oxidation of biogenic emissions. In addition to biogenic precursors, other precursors (alkanes and aromatic) plausibly also contribute to the SOOA enhancement during the warm season. SC-OOA is a minor contributor to OA (6%) and is most likely linked to emissions from a close-by marine lake.</p>
GEOS CCM free-running simulation data of the Pacific-Northwest pyrocumulonimbus Event-like aerosol injection, SWIRL selection
<p>Data from GEOS CCM free running simulation of the Pacific-Northwest pyrocumulonimbus Event. </p> <p>The simulations have been designed and performed by Sampa Das and Peter R. Colarco at the NASA Center for Climate Simulations; the computing resources supporting the simulations shown in this work were provided by the NASA High-End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at the Goddard Space Flight Center.</p> <p>The datasets stored here have been generated by Giorgio Doglioni starting from the whole results of the simulations.</p> <p>The files are in hdf5 format and their structure and content is described in the README file. </p> <p> </p>
Data supporting the study "The impact of molecular self-organisation on the atmospheric fate of a cooking aerosol proxy" by Milsom et al.
<p>Model and experimental data from the study "The impact of molecular self-organisation on the atmospheric fate of a cooking aerosol proxy" to be published in Atmospheric Chemistry and Physics. </p>
Dataset for Influence of Aerosol Chemical Composition on Condensation Sink Efficiency and New Particle Formation in Beijing
<p>This dataset includes one year long measurements of particle number size distributions, chemical composition of PM2.5, gaseous precursors, and meteorological parameters in urban Beijing, China, from March 1, 2018, to March 1, 2019. It is the supplementary data for "Influence of Aerosol Chemical Composition on Condensation Sink Efficiency and New Particle Formation in Beijing", which is published by Environmental Science & Technology Letter. Please cite: Wei Du, Jing Cai, Feixue Zheng, Chao Yan, Ying Zhou, Yishuo Guo, Biwu Chu, Lei Yao, Liine M. Heikkinen, Xiaolong Fan, Yonghong Wang, Runlong Cai, Simo Hakala, Tommy Chan, Jenni Kontkanen, Santeri Tuovinen, Tuukka Petäjä, Juha Kangasluoma, Federico Bianchi, Pauli Paasonen, Yele Sun, Veli-Matti Kerminen, Yongchun Liu, Kaspar R. Daellenbach, Lubna Dada, and Markku Kulmala Environmental Science & Technology Letters Article ASAP DOI: 10.1021/acs.estlett.2c00159</p>
A historic global ground-based monthly seasonal aerosol climatology based in AERONET data: a database 1993-2013
<table class="ds-includeSet-table detailtable table table-striped table-hover"> <tbody> <tr class="ds-table-row odd "> <td class="metadata-key label-cell" title="dc.description.abstract"> </td> <td class="metadata-field word-break">We present an aerosol classification based upon AERONET level 2.0 almucantar retrieval products from the period 1993 to 2012. In the initial phase of this research we opto-physically identified five major types of Bulk Columnar Aerosol (BCA) - based solely upon intensive optical properties of spectral Single Scattering Albedo (SSA), spectral Indices of Refraction (real – RRI and imaginary - IRI), and two Angstrom Exponents (extinction – EAE and absorption - AAE). These BCA we classified as Maritime Aerosol, Dust Aerosol, Urban Industrial Aerosol, Biomass Burning Aerosol, and Mixed Aerosol. The classification of a particular observation as one of these aerosol types is determined by its five-dimensional Mahalanobis distance (MD) to the centroid of each reference cluster (itself a 5-D hyperellipsoid). To retain a greater number of AERONET sites in the study (200+), we kept the variable space to 5-D. To generate reference clusters, we only retained data points that lie within 2 MD from the data centroid. Our typology is based on AERONET retrieved quantities, which do not include low optical depth values (AOD=440nm < 0.4 as per AERONET criteria for almucantar scan inversion). The classifications obtained will be useful in interpreting aerosol retrievals from satellite borne instruments and as input for regional climate models. The result is a dataset describing the types of aerosol particles that are distinct from one another in optical properties, and a geographic distribution of those aerosol types. We used the typology scheme upon the qualifying AERONET data archive, and produced seasonal aerosol climatologies by aerosol type for each of the AERONET sites included in the study, regional aerosol climatology maps, and a time-integrated global aerosol climatology map based entirely upon ground-based photometric data. An internally hyperlinked compendium of the individual AERONET site aerosol climatologies was produced to contain the results of the first phase of this work [available at https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf]. Each of these five aerosol types can be further discriminated into specific sub-types by this same scheme. For example, optical discrimination into specific sub-types of Biomass Burning aerosol may provide insight into sources exhibiting spectrally distinct smoke properties. We then use the mathematical strategies to sort the global AERONET data retrievals into the aerosol type classified against the reference standards. We believe these strategies regarding aerosol differentiation using polarization data will be useful for analysis of the newer AERONET version 3 data retrievals, and data collected from the deployment of newer CIMEL sun-photometers (with enhanced polarization measurement capabilities) to the network. The resulting AERONET-based aerosol typology is useful for applications in aerosol optics, including forward modeling or radiative transfer for remote sensing algorithms, or evaluating radiative forcing calculations in atmospheric models.</td> </tr> </tbody> </table> <p>Necessary Reference Material:</p> <p><span><span><span><span><span><span><span><span><span><span>[1] Giordano, M. E.,<em> </em><em>On Interactions of Matter and Energy: Light and Particles in a Terrestrial Atmosphere Progress on Opto-Physical Recognition and Classification of Aerosols: </em>A PhD dissertation, University of Nevada, copyright M.E. Giordano, 294 pages, December 2019. URI: <a href="http://hdl.handle.net/11714/6686" title="http://hdl.handle.net/11714/6686">http://hdl.handle.net/11714/6686</a></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span> <a href="https://scholarworks.unr.edu/handle/11714/6686?show=full" title="https://scholarworks.unr.edu/handle/11714/6686?show=full">https://scholarworks.unr.edu/handle/11714/6686?show=full</a></span></span></span></span></span></span></span></span></span></span></p> <p>[2]<span><span><span><span><span><span><span><span><span><span> Giordano, M.E., Ward, C.S., and Hamill, P.: <em>A Compendium of Aerosol Types Based on Mahalanobis Distances and AERONET data. </em>[An internally hyperlinked compendium of seasonal aerosol and local aerosol compositions] Atmospheric Environment, 140, 213-233,2016. </span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><a href="https://doi.org/10.1016/j.atmosenv.2016.06.002" title="https://doi.org/10.1016/j.atmosenv.2016.06.002">https://doi.org/10.1016/j.atmosenv.2016.06.002</a></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span> <a href="https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf" title="https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf">https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf</a></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span>[3] </span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span>Hamill, P. J., Giordano, M. E., Ward, C.S., Giles, D., Holben, B.: <em>An AERONET - based aerosol</em></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><em> classification using the Mahalanobis distance,</em> Atmospheric Environment, Volume 140, September, pgs 213 -233, 2016. <a href="http://dx.doi.org/10.1016/j.atmosenv.2016.06.002">http://dx.doi.org/10.1016/j.atmosenv.2016.06.002</a>and also at</span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span> <a href="https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf">https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf</a></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span>[4] Hamill, Patrick, Piedra, Patricio G., Giordano, Marco, E., 2020: <em>Simulated Polarization as a Signature of Aerosol Type</em>. Atmospheric Environment, Volume 224, 117348 article ATMENVD- 19-01763, 2020. </span></span></span></span></span></span></span></span></span></span><a href="https://doi.org/10.1016/j.atmosenv.2020.117348" title="Persistent link using digital object identifier">https://doi.org/10.1016/j.atmosenv.2020.117348</a></p> <p><span><span><span><span><span><span><span><span><span><span> </span></span></span></span></span></span></span></span></span></span></p>
Dataset for "Experimental determination of the relationship between organic aerosol viscosity and ice nucleation at upper free tropospheric conditions"
<p><strong>Data and scripts used to create the figures in the manuscript titled: "Experimental determination of the relationship between organic aerosol viscosity and ice nucleation at upper free tropospheric conditions"</strong></p>
Investigation of New Particle Formation mechanisms and aerosol processes at the Marambio Station, Antarctic Peninsula
<p>This dataset belongs to : <strong>Investigation of New Particle Formation mechanisms and aerosol processes at the Marambio Station, Antarctic Peninsula</strong></p> <p><em>Quéléver, L. L. J., Dada, L., Asmi, E., Lampilahti, J., Chan, T., Ferrara, J. E., Copes, G. E., Pérez-Fogwill, G., Barreira, L., Aurela, M., Worsnop, D. R., Jokinen, T., and Sipila, M.: Investigation of New Particle Formation mechanisms and aerosol processes at the Marambio Station, Antarctic Peninsula, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-1063, in review, 2022</em></p> <p> </p> <p>The Mass spectrometry and particle data were the result of a measurement campaign performed at the Marambio Station, Antarctica during the austral summer 2018 </p> <p>The data set includes:</p> <p>- Time series for sulfuric acid, methane sulfonic acid and iodic acid concentration </p> <p>- Mass defect data for a new particle formation case study on 16-02-2018</p> <p>- Time series for nucleation rates </p> <p>- Time series for formation rates </p> <p>Please refer to the individual *README.txt files and the corresponding publication.</p> <p>For further information please contact the corresponding author: Lauriane L.J. Quéléver: Lauriane.quelever@helsinki.fi</p>
Aerosol Microphysics Emulation Dataset
<p>This dataset contains input/output data of one time step of the M7 aerosol microphysics model. It is created to enable the use of machine learning to emulate the model part. It contains input and output pairs for training, validation and testing from separate days of the year. The code can be found <a href="https://github.com/paulaharder/aerosol-microphysics-emulation">here</a>. </p>
The realization of autonomous, aircraft-based, real-time aerosol mass spectrometry in the upper troposphere and lower stratosphere (dataset)
<p>Dataset accompanying the journal article titled "The realization of autonomous, aircraft-based, real-time aerosol mass spectrometry in the upper troposphere and lower stratosphere". Preprint: doi.org/10.5194/egusphere-2022-33</p>
Data archive for the peer-reviewed journal article "Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data"
<p>Data archive accompanying the peer-reviewed journal article "Information content and aerosol property retrieval potential for different types of in situ polar nephelometer data". This article was accepted for publication in the journal <em>Atmospheric Measurement Techniques</em> in 2022. The original contributions presented in the study are included in the article and its supplementary information. The GRASP-OPEN model was used to perform forward calculations: this model is publicly available on the official GRASP website (https://www.grasp-open.com/; last access: 14 September, 2022). The specific GRASP-OPEN model outputs that were used for the study are contained in this data archive. </p>
Development and evaluation of E3SM-MOSAIC: Spatial distributions and radiative effects of nitrate aerosol
<p>FC20TR-MOZ_NUG_PD_V2beta4_ANN_200501_201412_climo.nc 10-yr mean for MZT_PD</p> <p>FC20TR-MOZ_MOSAIC_AIKDST_NUG_PD_V2beta4_ANN_200501_201412_climo.nc 10-yr mean for MTC_SLOW_PD</p> <p>FC20TR-MOZ_MOSAIC_AIKDST_MTC_NUG_PD_V2beta4_ANN_200501_201412_climo.nc 10-yr mean for MTC_WGT_PD</p> <p>FC20TR-MOZ_MOSAIC_AIKDST_MTC-SPLC_NUG_PD_V2beta4_ANN_200501_201412_climo.nc 10-yr mean for MTC_SPLC_PD</p> <p> </p> <p>NO3_TM_2005-2014.nc 10-yr mean nitrate burden</p> <p>NO3_AQCH_GAEX_2005-2014.nc 10-yr mean for nitrate chemistry production</p> <p>NO3_DRF_2005-2014.nc 10-yr mean nitrate direct forcing between PD and PI</p> <p>NO3_INDRF_2005-2014.nc 10-yr mean nitrate indirect forcing between PD and PI</p> <p> </p> <p>NH4_TM_2005-2014.nc 10-yr mean for ammonium burden</p> <p>NH4_DRF_2005-2014.nc 10-yr mean ammonium direct forcing between PD and PI</p> <p> </p> <p>SO4_TM_2005-2014.nc 10-yr mean for sulfate burden</p> <p>SO4_DRF_2005-2014.nc 10-yr mean sulfate direct forcing between PD and PI</p> <p> </p> <p>CCN3_1850.nc Cloud condensation nuclei number concentrations at 0.1% super saturation at PI</p> <p>CCN3_2005-2014.nc CCN3 at PD</p> <p>CCN3_NONO3_1850.nc CCN3 at PI without nitrate formation</p> <p>CCN3_NONO3_2005-2014.nc CCN3 at PD without nitrate formation</p> <p> </p> <p>CDNC_*.nc cloud droplet number concentrations</p> <p>CLDFRC_*.nc cloud fraction</p> <p>CWP_*.nc cloud liquid water path</p> <p> </p> <p> </p>
WORCC-PMOD/WRC quality assured aerosol optical depth and Ångström exponent for Ny-Ålesund GAW station (2002- present)
<p>WORCC-PMOD/WRC quality assured aerosol optical depth and Ångström exponent for Ny-Ålesund GAW station (2002- present)</p> <p>Aerosol optical depth (AOD) measurements have been performed within the frame of Global Atmospheric Watch Precision Filter Radiometer (GAW-PFR) network in Ny-Ålesund (79N,11E) since 2002. The measurements are performed from March to October, with PFR (PrecisionFilterRadiometer) instruments provided by Physikalisch-Meteorologisches Observatorium Davos, World Radiation Center (PMOD/WRC). The solar tracker and infrastructure are provided by provided by NILU(Norsk institutt for luftforskning) in collaboration with the Norwegian Polar Institute.</p> <p>PFR manufactured by PMOD/WRC is a temperature stabilized instrument at 20<sup>o</sup> C equipped with four narrow band interference filters with nominal centroid wavelengths 368 nm, 412 nm, 500 nm and 862 nm and bandpass (fullwidth half maximum) 4 nm for 368 nm channel and 5 nm for the rest. The instrument is calibrated in yearly bases against the WMO-AOD reference at PMOD/WRC during the polar winter. The operation is done remotely by PMOD/WRC with the installation and onsite maintenance (cleaning, alignment adjustments) done by personnel of Norwegian Polar Institute and NILU in collaboration with PMOD/WRC.</p> <p>The processing and quality assurance of the data is done following the protocols of World Optical depth Research and Calibration Center (WORCC, PMOD/WRC) (2018), and the data are submitted to the WDCA database (EBAS-NILU) as hourly mean AOD and AE values. This dataset contains the high-resolution data (1 min) cloud screened and quality assured since 2002. The provided Ångström exponent is retrieved from the 4 wavelengths.</p> <p> </p> <p> </p> <p>SUN_PFR_AOD_AE_V1.0.dat : AOD and AE for the period 2002-2021</p> <p>Lunar_PFR_AOD_AE_V1.0.dat : AOD and AE for the period 2018-2021</p> <p> </p> <p> </p> <p>KN: Calibration, operation, and processing since 2014, WORCC quality assurance protocols, quality assurance of the presented dataset</p> <p>KS: , WORCC quality assurance protocols, consulting on quality assurance of the presented dataset</p> <p>WC: calibration, operation, and processing 2002- 2014</p> <p>NS: operation and processing 2005- 2012</p> <p>HGH: principal investigator of hosting institute NILU</p> <p>SK: principal investigator of hosting institute NILU</p> <p>Acknowledgement: Special thanks the personnel of the Norwegian Polar Institute at Ny-Ålesund for all valuable the technical support for the solar and lunar measurements.</p> <p> </p> <p> </p> <p>1. Kazadzis, S., Kouremeti, N., Nyeki, S.<em>, et al.</em> (2018) The World Optical Depth Research and Calibration Center (WORCC) quality assurance and quality control of GAW-PFR AOD measurements 10.5194/gi-7-39-2018 <a href="https://gi.copernicus.org/articles/7/39/2018/">https://gi.copernicus.org/articles/7/39/2018/</a></p> <p> </p>
Atmospheric aerosol chemical characterization and organic aerosol source apportionment by HR-TOF-AMS in the Po Valley during RHAPS (2021)
<p><span>Time series of non-refractory submicrometric aerosol (PM1) chemical components (sulfate, nitrate, ammonium, chloride, and organic aerosol, OA) from RHAPS campaigns (winter and summer 2021) at Bologna (BO) and San Pietro Capofiume (SPC), Po Valley, Italy.<br></span></p> <p><span>Time series and profiles of OA source factors derived from PMF.</span></p>
MAJA look-up tables for Sentinel-2 A&B sensors, for a continental aerosol model
<p>These are the Look-up tables used by MAJA atmospheric correction software, used to process Sentinel-2 A&B sensors.</p> <p>These look-up tables correspond to a continental model.</p>
Data for "The role of H2SO4-NH3 anion clusters in ion-induced aerosol nucleation mechanisms in the boreal forest"
<p>This is the dataset that has been analyzed for "The role of H2SO4-NH3 anion clusters in ion-induced aerosol nucleation mechanisms in the boreal forest". Please contact the author (chao.yan@helsinki.fi) for more details. </p>
MAJA look-up tables for Sentinel-2 A&B sensors, for Copernicus Atmosphere Monitoring Service aerosol types
<p>The archive contains the Look-up tables used by MAJA atmospheric correction software, used to process Sentinel-2 A&B sensors. These look-up tables correspond to the aerosol types used by Copernicus Atmosphere Monitoring Service (CAMS). However, the default continental model is also provided.</p> <p>Version 1.1 has new LUT for water vapour estimates, which corrects for a bias observed for large water vapour contents (above 2.5 g/cm2)</p> <p>Version 1.2 just changed the Folder name for a better integration with Start_maja.</p> <p>Version 1.3 added the Header files</p>
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.