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465 results for “energetics”
Energetic Valorisation of Saltworks Bitterns via Reverse Electrodialysis: A Laboratory Experimental Campaign
<p>Concentrated bitterns discharged from saltworks have extremely high salinity, often up to 300 g/L, thus their direct disposal not only has a harmful effect on the environment, but also generates a depletion of a potential resource of renewable energy. Here, reverse electrodialysis (RED), an emerging electrochemical membrane process, is proposed to capture and convert the salinity gradient power (SGP) intrinsically conveyed by these bitterns also aiming at the reduction of concentrated salty water disposal. A laboratory-scale RED unit has been adopted to study the SGP potential of such brines, testing ion exchange membranes from different suppliers and un-der different operating conditions. Membranes supplied by Fujifilm, Fumatech, and Suez were tested, and the results were compared. The unit was fed with synthetic hypersaline solution mimicking the concentration of natural bitterns (5 mol/L of NaCl) on one side, and with variable concentration of NaCl dilute solutions (0.01–0.1 mol/L) on the other. The influence of several op-erating parameters has also been assessed, including solutions flowrate and temperature. In-creasing feed solutions’ temperature and velocity has been found to lower the stack resistance, which enhances the output performance of the RED stack. The maximum obtained power density (corrected to account for the effect of electrodic compartments, which can be very relevant in five cell pairs laboratory stacks) reached around 10.5 W/m2cellpair, with FUJIFILM Type 10 membranes, temperature of 40 °C, and a fluid velocity of 3 cm s−1 (as empty channel, considering 270 m thickness). Notably, the present study results confirm the large potential for SGP generation from hypersaline brines, thus providing useful guidance for the harvesting of SGP in seawater saltworks all around the world.</p>
A Novel Backtracing Model to Study the Emission of Energetic Neutral Atoms at Titan
<p>Data for the manuscript "A Novel Backtracing Model to Study the Emission of Energetic Neutral Atoms at Titan" by Tippens et al., (2023). See README.txt for a description of the data files included here.</p>
Why bears hibernate? Redefining the scaling energetics of hibernation
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Energetic trade-offs in migration decision-making, reproductive effort, and subsequent parental care in a long-distance migratory bird
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Data from: Individual energetics scale up to community coexistence: Movement, metabolism and biodiversity dynamics in fragmented landscapes
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Data for: The energetics of rapid mechanotransduction
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Energetic mismatch induced by warming decreases leaf litter decomposition by aquatic detritivores
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Data from: Biologging in a free-ranging mammal reveals apparent energetic trade-offs among physiological and behavioral components of the acute phase response
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Economies of scale shape energetics of solitary and group living spiders and their webs
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Size-associated energetic constraints on the seasonal onset of reproduction in a species with indeterminate growth
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Energetic limits: Defining the bounds and trade-offs of successful energy management in a capital breeder
<p>1. Judicious management of energy can be invaluable for animal survival and reproductive success. Capital breeding mammals typically transfer energy to their young at extremely high rates while undergoing prolonged fasting, making lactation a tremendously energy demanding period. Effective management of the competing demands of the mother's energy needs and those of her offspring is presumably fundamental to maximising lifetime reproductive success.</p> <p>2. How does the mother maximise her chances of successfully rearing her pup, by ensuring that both her pup and herself have sufficient energy during this 'energetic fast'? While energy management models were first discussed in the 1990s, application of this analytical technique is still very much in its infancy. Recent work suggests that a broad range of species exhibit 'energy compensation'; during periods when they expend more energy on activity, their bodies partially compensate by reducing background (basal) metabolic rate as an adaptation to limit overall energy expenditure. However, the value of energy management models in understanding animal ecology is presently unclear.</p> <p>3. We investigate whether energy management models provide insights into the breeding strategy of phocid seals. Not only do we expect lactating seals to display energy compensation because of their breeding strategy of high energy transfer while fasting, but we anticipate that mothers exhibiting a lack of energy compensation are less likely to rear offspring successfully.</p> <p>4. On the Isle of May in Scotland, we collected heart rate data as a proxy for energy expenditure in 52 known individual grey seal (Halichoerus grypus) mothers, repeatedly across three years of breeding. We provide evidence that grey seal mothers typically exhibit energy compensation during lactation by down-regulating their background metabolic rate to limit daily energy expenditure during periods when other energy costs are relatively high. However, individuals that fail to energy compensate during the lactation period are more likely to end lactation earlier than expected.</p> <p>5. Our study is the first to demonstrate the importance of energy compensation to an animal's reproductive expenditure. Moreover, our multi-seasonal data indicate that environmental stressors may reduce the capacity of some individuals to follow the energy compensation strategy. </p>
Limb work and joint work minimisation reveal an energetic benefit to the elbows-back, knees-forward limb design in parasagittal quadrupeds. Supplementary material including data, simulation code and simulation results
<p>Quadrupedal animal locomotion is energetically costly. We explore two forms of mechanical work that may be relevant in imposing these physiological demands. Limb work, due to the forces and velocities between the stance foot and the centre of mass, could theoretically be zero given vertical limb forces and horizontal centre of mass path. To prevent pitching, skewed vertical force profiles would then be required, with forelimb forces high in late stance and hindlimb forces high in early stance. By contrast, joint work – the positive mechanical work performed by the limb joints – would be reduced with forces directed through the hip or shoulder joints. Measured quadruped kinetics show features consistent with compromised reduction of both forms of work, suggesting some degree of, but not perfect, inter-joint energy transfer. The elbows-back, knees-forward design reduces the joint work demand of a low limb-work, skewed, vertical force profile. This geometry allows periods of high force to be supported when the distal segment is near vertical, imposing low moments about the elbow or knee, while the shoulder or hip avoids high joint power despite high moments because the proximal segment barely rotates – translation over this period is due to rotation of the distal segment.</p>
Snapshots, frequency contact maps analysis, Poisson Boltzmann calculations, and data scripts for characterization of structural and energetic differences between conformations of the SARS-CoV-2 spike protein
<p><strong>Molecular dynamics simulation</strong> trajectories, which have been performed using the Amber ff14SB force field running with the Amber18 package at the NSF-funded (OAC-1826915, OAC-1828163) ELSA high performance computing cluster at The College of New Jersey. Simulation methodology and further details are described in [1] and [2]. For further details on the trajectories, please contact Joseph Baker (bakerj@tcnj.edu).</p> <p>The <strong>Poisson Boltzmann </strong>energy calculations have been achieved by using the input_files.tar.xz found here and solving the Poisson Boltzmann equation with pygbe. A more detailed example and tutorial can be found at [4]. For further details contact Horacio V Guzman.</p> <p><strong>The dataset contains </strong></p> <ul> <li><strong>A total of 30 snapshots of the three trajectories (10 snapshots each system = two per replica x 5 replicas/system):</strong></li> </ul> <ol> <li>SARS-CoV-2002 spike protein with three RBD in the down positions: "COV2-DDD/PDB/" .</li> <li>SARS-CoV-2002 spike protein with one RBD in the up and two RBD in the down positions: "COV2-UDD/PDB/".</li> <li>SARS-CoV-2002 spike protein with two RBD in the up and one RBD in the down positions: "COV2-DUU/PDB/".</li> </ol> <ul> <li><strong>Input files for Poisson-Boltzmann analysis</strong>:</li> </ul> <ol> <li>PoissonBoltzmann/input_files.tar.xz</li> </ol> <ul> <li><strong>Data for the frequency contact map and processing scripts</strong>:</li> </ul> <ol> <li>cov2-ddd.pdb, cov2-udd.pdb, cov2-duu.pdb reference PDB files.</li> <li>Contact maps [3] at "COV2-DDD/CONTACT_MAP/", "COV2-UDD/CONTACT_MAP/", "COV2-DUU/CONTACT_MAP/".</li> <li>frequency.lua: get frequency of contacts from a set of contacts map files.</li> <li>diff_frequency.lua: get differential frequency of contacts from a set of frequency files.</li> <li>Frequency of contacts listed in frequency.data files at "COV2-DDD/", "COV2-UDD/" and "COV2-DUU/" directories.</li> </ol> <p>Read the "INFO" files for further informations.</p> <p>This dataset and the code is part of a collaboration between:</p> <ul> <li>The Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland (supported by the National Science Centre, Poland, under grant No. 2017/26/D/NZ1/0046)</li> <li>Department of Chemistry, The College of New Jersey, New Jersey, United States (supported by National Science Foundation under grant numbers OAC-1826915 and OAC-1828163).</li> <li>Jozef Stefan Institute, Ljubljana, Slovenia (supported by the Slovenian Research Agency (Funding No. P1-0055)).</li> <li>School of engineering in bioinformatics, University of Talca, Talca, Chile.</li> </ul> <p>[1] Rodrigo A. Moreira, Mateusz Chwastyk, Joseph L. Baker, Horacio V Guzman, & Adolfo B. Poma. (2020). All-atom simulations snapshots and contact maps analysis scripts for SARS-CoV-2002 and SARS-CoV-2 spike proteins with and without ACE2 enzyme (Version 0.1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3817447</p> <p>[2] Chad W. Hopkins, Scott Le Grand, Ross C. Walker, and Adrian E. Roitberg. Long-Time-Step Molecular Dynamics through Hydrogen Mass Repartitioning. Journal of Chemical Theory and Computation 2015 11 (4), 1864-1874. http://doi.org/10.1021/ct5010406</p> <p>[3] Rodrigo A. Moreira, Mateusz Chwastyk, Joseph L. Baker, Horacio V Guzman, & Adolfo B. Poma. Quantitative determination of mechanical stability in the novel coronavirus spike protein. Nanoscale, 2020,12, 16409-16413. <a href="https://doi.org/10.1039/D0NR03969A">https://doi.org/10.1039/D0NR03969A</a></p> <p>[4] https://github.com/pyF4all</p>
Does the preferred walk-run transition speed on steep inclines minimize energetic cost, heart rate or neither?
Humans prefer to walk at slow speeds and to run at fast speeds. In between, there is a speed at which people choose to transition between gaits, the Preferred Transition Speed (PTS). At slow speeds, it is energetically cheaper to walk and at faster speeds, it is cheaper to run. Thus, there is an intermediate speed, the Energetically Optimal Transition Speed (EOTS). Our goals were to determine: 1) how PTS and EOTS compare across a wide range of inclines and 2) if the EOTS can be predicted by the heart rate optimal transition speed (HROTS). Ten healthy, high-caliber, male trail/mountain runners participated. On day 1, subjects completed 0&[deg] and 15&[deg] trials and on day 2, 5&[deg] and 10&[deg]. We calculated PTS as the average of the walk-to-run transition speed (WRTS) and the run-to-walk transition speed (RWTS) determined with an incremental protocol. We calculated EOTS and HROTS from energetic cost and heart rate data for walking and running near the expected EOTS for each incline. The intersection of the walking and running linear regression equations defined EOTS and HROTS. We found that PTS, EOTS, and HROTS all were slower on steeper inclines. PTS was slower than EOTS at 0&[deg], 5&[deg], and 10&[deg], but the two converged at 15&[deg]. Across all inclines, PTS and EOTS were only moderately correlated. Although EOTS correlated with HROTS, EOTS was not predicted accurately by heart rate on an individual basis.
Data for "Does the Preferred Walk-Run Transition Speed on Steep Inclines Minimize Energetic Cost, Heart Rate or Neither?"
<p>Abstract</p> <p>Humans prefer to walk at slow speeds and to run at fast speeds. In between, there is a speed at which people choose to transition between gaits, the Preferred Transition Speed (PTS). At slow speeds, it is energetically cheaper to walk and at faster speeds, it is cheaper to run. Thus, there is an intermediate speed, the Energetically Optimal Transition Speed (EOTS). Our goals were to determine: 1) how PTS and EOTS compare across a wide range of inclines and 2) if the EOTS can be predicted by the heart rate optimal transition speed (HROTS). Ten healthy, high-caliber, male trail/mountain runners participated. On day 1, subjects completed 0&[deg] and 15&[deg] trials and on day 2, 5&[deg] and 10&[deg]. We calculated PTS as the average of the walk-to-run transition speed (WRTS) and the run-to-walk transition speed (RWTS) determined with an incremental protocol. We calculated EOTS and HROTS from energetic cost and heart rate data for walking and running near the expected EOTS for each incline. The intersection of the walking and running linear regression equations defined EOTS and HROTS. We found that PTS, EOTS, and HROTS all were slower on steeper inclines. PTS was slower than EOTS at 0&[deg], 5&[deg], and 10&[deg], but the two converged at 15&[deg]. Across all inclines, PTS and EOTS were only moderately correlated. Although EOTS correlated with HROTS, EOTS was not predicted accurately by heart rate on an individual basis.</p> <p>Methods</p> <p>Subjects walked and ran on a classic Quinton 18-60 motorized treadmill with a rigid steel deck (Quinton Instrument Company, Bothell, WA).</p> <p><strong>Determination of PTS: </strong>The average of the walk-to-run transition speed (WRTS) and run-to-walk transition speed (RWTS) defined the PTS as per Hreljac et. al. (2007). We first determined the WRTS in the walk-first group and then their RWTS and <em>vice versa</em> for the run-first group. Based on pilot experiments, we selected starting speeds such that there was no doubt which gait would be preferred at the initial speed. Once the speed of the treadmill was correctly set, subjects mounted the treadmill and chose their gait <em>ad libitum</em>. After we determined the preferred gait at the particular speed, the subject straddled the treadmill belt while we changed the speed by 0.1 m/s (increased during WRTS trials, decreased during RWTS trials). The process repeated until a gait transition occurred and was sustained for 30 seconds.</p> <p><strong>Determination of EOTS and HROTS: </strong>For the energetics and heart rate trials, we set the initial speed based on pilot experiments that indicated it would be near the EOTS. Subjects in the walk-first group walked at the incline-specific initial speed for 5 min, rested for ∼5 min and then ran at that speed for 5 min. Subjects in the run-first group did the opposite. During the rest periods, we re-weighed the subject and they drank just enough water to compensate for the weight loss due mostly to sweating. Thus, each subject maintained a nearly constant weight throughout all the trials.</p> <p>To measure metabolic rate during walking and running, we used an open-circuit, expired gas analysis system (TrueOne 2400; ParvoMedics, Sandy, UT). Subjects wore a mouthpiece with a one-way breathing valve and a nose clip allowing us to collect their expired air. The ParvoMedics software calculated the STPD rates of oxygen consumption (V□O<sub>2</sub>) and carbon dioxide production (V□CO<sub>2</sub>) and we averaged the last 2 minutes of each 5-minute trial. We then calculated metabolic power using the equation of Péronnet and Massicotte (1991) equation, as clarified by Kipp et al. (2018). We only included trials with respiratory exchange ratios (RER) <1.0 to ensure that metabolic energy was predominantly being provided from oxidative pathways. We used an R7 Polar iWL (Polar Electro Oy, Kempele, Finland) to measure heart rate in beats per minute (bpm) and averaged the values for the last 2 min of each trial.</p> <p>Immediately after both gait trials were completed for the initial speed, we calculated and compared the metabolic power required for walking and running. If walking was the more economical gait, we increased the treadmill speed by 0.1 m/s, and the process repeated. If running was the more economical gait, we decreased the treadmill speed by 0.1 m/s, and the process repeated. Each subject performed three speeds, both walking and running at each incline. However, some subjects needed to complete walking and running trials at a fourth speed so that we could obtain energetics data for one speed faster and one speed slower than their EOTS.</p> <p>For the three speeds at which the differences between metabolic rates between walking and running were least, we calculated linear regression equations for both metabolic power and heart rate as functions of speed for both walking and running for each subject and incline. The speeds at which the two equations intersected defined the EOTS and HROTS for each subject.</p> <p>Overall, we analyzed ten subjects at four different inclines, i.e. 40 determinations of EOTS and HROTS. Of those 80 linear regression analyses, the walking vs. running regressions intersected at a speed < 3 m/sec for all but two subjects (one subject for EOTS at 15° and a different subject for HROTS at 10°). Essentially, those individuals’ regression lines were nearly parallel. We chose to exclude those two conditions from further statistical analysis and aggregate data compilation.</p> <p>Usage Notes</p> <p>There are two missing values, as noted in the methods: HROTS for subject 5 at 10 degrees and EOTS for subject 4 at 15 degrees.</p>
Energetic Proton Propagation and Acceleration Simulated for the Bastille Day Event of July 14, 2000
<p>This includes data from the EPREM+CORHEL simulation run presented in "Energetic Proton Propagation and Acceleration Simulated for the Bastille Day Event of July 14, 2000" (Astrophysical Journal). The eight files ending in '.nc' contain the EPREM stream-observer data used to create Figures 5 & 7. The data was saved in the self-describing <a href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF4</a> format. The HTML files contain the following interactive figures, which you can open in your internet browser:</p> <ul> <li><strong>cos_theta-e10.0-t44.html</strong> cosine of the flow angle (Figure 7)</li> <li><strong>divV-e10.0-t44.html</strong> velocity divergence (Figure 7)</li> <li><strong>flux-e10.0-t44-log.html</strong> differential flux of 10-MeV protons (Figure 7)</li> <li><strong>peak_flux-e10.0-t44.html</strong> relative peak flux of 10-MeV protons (Figure 5)</li> <li><strong>peak_flux-e100.0-t44.html</strong> relative peak flux of 100-MeV protons (not shown in paper)</li> <li><strong>tau_p-e10.0-t44-log.html</strong> theoretical acceleration rate (Figure 7)</li> </ul>
Data for the paper: An energetic view on the geographical dependence of the fast aerosol radiative effects on precipitation
<p>This is the data presented in "An energetic view on the geographical dependence of the fast aerosol radiative effects on precipitation", Dagan et al 2021 JGR.</p> <p>names of files:</p> <p>*aqua* is for aqua-planet simulations, while *AMIP* is for AMIP simulations. *id* is for the simulations with the idealised aerosol perturbations in AMIP type of simulations. sur* are for surface variables, while d_*zonal* and d_mar* present the devotion zonal and meridional cross-sections as in the paper </p> <p>The names of the variables are as in the paper. </p>
Heterothermy as a mechanism to offset energetic costs of environmental and homeostatic perturbations
<p>Environmental and biotic pressures impose homeostatic costs on all organisms. The energetic costs of maintaining high body temperatures (<i>T</i><sub>b</sub>) render endotherms sensitive to pressures that increase foraging costs. In response, some mammals become more heterothermic to conserve energy. We measured <i>T</i><sub>b</sub> in banner-tailed kangaroo rats (<i>Dipodomys spectabilis</i>) to test and disentangle the effects of air temperature and moonlight (a proxy for predation risk) on thermoregulatory homeostasis. We further perturbed homeostasis in some animals with chronic corticosterone (CORT) via silastic implants. Heterothermy increased across summer, consistent with the predicted effect of lunar illumination (and predation), and in the direction opposite to the predicted effect of environmental temperatures. The effect of lunar illumination was also evident within nights as animals maintained low <i>T</i><sub>b</sub> when the moon was above the horizon. The pattern was accentuated in CORT-treated animals, suggesting they adopted an even further heightened risk-avoidance strategy that might impose reduced foraging and energy intake. Still, CORT-treatment did not affect body condition over the entire study, indicating kangaroo rats offset decreases in energy intake through energy savings associated with heterothermy. Environmental conditions receive the most attention in studies of thermoregulatory homeostasis, but we demonstrated here that biotic factors can be more important and should be considered in future studies.</p>
Solar Energetic Proton Access to the Inner Magnetosphere during the 7-8 September 2017 event
<p>Dataset used in the manuscript.</p> <p> </p> <p>2fluxLFM.txt: The first two columns are time from 09/07 00UT in hours and L along RBSP-B trajectory where the cutoff energy in vertical direction calculated using LFM model is 21 MeV. The third and fourth columns are time from 09/07 00UT in hours and L along RBSP-A trajectory where the cutoff energy in vertical direction calculated using LFM model is 21 MeV. Columns 5 and 6 are time from 09/07 00UT in hours and L where the 21 MeV proton flux measured by RBSP-B is 50% of the interplanetary flux. Columns 7 and 8 are time from 09/07 00UT in hours and L where the 21 MeV proton flux measured by RBSP-A is 50% of the interplanetary flux. </p> <p>2fluxTS.txt: Columns 1 and 2 are time from 09/07 00UT in hours and L where the 21 MeV proton flux measured by RBSP-B is 50% of the interplanetary flux. Columns 3 and 4 are time from 09/07 00UT in hours and L where the 21 MeV proton flux measured by RBSP-A is 50% of the interplanetary flux. Columns 5 and 6 are time from 09/07 00UT in hours and L along RBSP-A trajectory where the cutoff energy in vertical direction calculated using LFM model is 21 MeV. Columns 7 and 8 are time from 09/07 00UT in hours and L along RBSP-B trajectory where the cutoff energy in vertical direction calculated using LFM model is 21 MeV. </p> <p>acut_0907.txt and acutoff_0908.txt are the cutoff energy along RBSP-A orbit calculated using TS07. The first column is time in seconds, the next three columns are the satellite location (radial distance in Re, latitude and longitude), and the last three columns are the cutoff energy in MeV in west, vertical and east direction. </p> <p>bcut_0907.txt and bcutoff_0908.txt are the cutoff energy along RBSP-B orbit calculated using TS07. The first column is time in seconds, the next three columns are the satellite location (radial distance in Re, latitude and longitude), and the last three columns are the cutoff energy in MeV in west, vertical and east direction. </p> <p>cutoffa_t700_fixed.dat and cutoffb_t700.dat are the cutoff energy along RBSP-A and RBSP-B orbits calculated using LFM. The first column is time in seconds, the next three columns are the satellite location (radial distance in Re, latitude and longitude), and the last three columns are the cutoff energy in MeV in west, vertical and east direction. </p> <p>tmax_test.txt is the cutoff energy using different tmax parameters at two time points. The first column is time in seconds from the start of the simulation. The next three column are the satellite location (radial distance in Re, latitude and longitude). The last three columns are the cutoff energy in MeV in west vertical and east directions. Lines corresponds to different tmax parameters. </p> <p>vap_081.txt and vap_082.txt are external Bz in nT measured by RBSP-B along an outbound orbit on 09/08 0719-1110UT and an inbound orbit on 09/08 1145-1545UT.</p> <p>ts05_081.txt and ts05_082.txt are external Bz in nT calculated using TS05 magnetic field model at RBSP-B location along an outbound orbit on 09/08 0719-1110UT and an inbound orbit on 09/08 1145-1545UT.</p> <p>ts07_081.txt and ts07_082.txt are external Bz in nT calculated using TS07 magnetic field model at RBSP-B location along an outbound orbit on 09/08 0719-1110UT and an inbound orbit on 09/08 1145-1545UT</p> <p>lfm_081.txt and lfm_082.txt are external Bz in nT calculated using LFM global MHD model at RBSP-B location along an outbound orbit on 09/08 0719-1110UT and an inbound orbit on 09/08 1145-1545UT</p> <p>b1430TS05 and EXTERNALTS07.txt are the magnetic field in nT calculated by TS05 and TS07 on 09/08 1430UT. </p>
Data from: Costs and benefits of group living in primates: an energetic perspective
Group size is a fundamental component of sociality, and has important consequences for an individual's fitness as well as the collective and cooperative behaviours of the group as a whole. This review focuses on how the costs and benefits of group living vary in female primates as a function of group size, with a particular emphasis on how competition within and between groups affects an individual's energetic balance. Because the repercussions of chronic energetic stress can lower an animal's fitness, identifying the predictors of energetic stress has important implications for understanding variation in survivorship and reproductive success within and between populations. Notably, we extend previous literature on this topic by discussing three physiological measures of energetic balance—glucocorticoids, c-peptides and thyroid hormones. Because these hormones can provide clear signals of metabolic states and processes, they present an important complement to field studies of spatial and temporal changes in food availability. We anticipate that their further application will play a crucial role in elucidating the adaptive significance of group size in different social and ecological contexts.
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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)
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.
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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.