Large-scale Properties Research Articles

An analysis of the most distant cataloged open clusters

Context. Several studies have been presented in the last few years applying some kind of automatic processing of data to estimate the fundamental parameters of open clusters. These parameters are then employed in larger scale analyses, for example the structure of the Galaxy’s spiral arms. The distance is one of the most straightforward parameters to estimate, yet enormous differences can still be found among published data. This is particularly true for open clusters located more than a few kiloparsecs away. Aims. We cross-matched several published catalogs and selected the 25 most distant open clusters (> 9000 pc). We then performed a detailed analysis of their fundamental parameters, with emphasis on their distances, to determine the agreement between the catalogs and our estimates. Methods. Photometric and astrometric data from the Gaia EDR3 survey was employed. The data were processed with our own membership analysis code, pyUPMASK, and our package for the automatic estimation of fundamental cluster parameters, ASteCA. Results. We find differences in the estimated distances of up to several kiloparsecs between our results and those cataloged, even for the catalogs that show the best matches with ASteCA values. Large differences are also found for the age estimates. As a by-product of the analysis we find that vd Bergh-Hagen 176 could be the open cluster with the largest heliocentric distance cataloged to date. Conclusions. Caution is thus strongly recommended when using cataloged parameters of open clusters to infer large-scale properties of the Galaxy, particularly for those located more than a few kiloparsecs away.

Surveying the Giant H ii Regions of the Milky Way with SOFIA. IV. Sgr D, W42, and a Reassessment of the Giant H ii Region Census

This is the fourth paper exploring the infrared properties of giant H ii regions with the FORCAST instrument on the Stratospheric Observatory For Infrared Astronomy (SOFIA). Our survey utilizes the census of 56 Milky Way giant H ii regions identified by Conti & Crowther, and in this paper we present the 20 and 37 μm imaging data we obtained from SOFIA for sources Sgr D and W42. Based upon the SOFIA data and other multiwavelength data, we derive and discuss the detailed physical properties of the individual compact sources and subregions as well as the large-scale properties of Sgr D and W42. However, improved measurements have revealed much closer distances to both regions than previously believed, and consequently, both sources are not powerful enough to be considered giant H ii regions any longer. Motivated by this, we revisit the census of giant H ii regions, performing a search of the last two decades of literature to update each source with the most recent and/or most accurate distance measurements. Based on these new distance estimates, we determine that 14 sources in total (25%) are at sufficiently reliable and closer distances that they are not powerful enough to be considered giant H ii regions. We briefly discuss the observational and physical characteristics specific to Sgr D and W42 and show that they have properties distinct from the giant H ii regions previously studied as a part of this survey.

Fast free-energy-based neutral set size estimates for the RNA genotype-phenotype map.

The genotype–phenotype (GP) map of RNA secondary structure links each RNA sequence to its corresponding secondary structure. Previous research has shown that the large-scale structural properties of GP maps, such as the size of neutral sets in genotype space, can influence evolutionary outcomes. In order to use neutral set sizes, efficient and accurate computational methods are needed to compute them. Here, we propose a new method, which is based on free energy estimates and is much faster than existing sample-based methods. Moreover, this approach can give insight into the reasons behind neutral set size variations, for example, why structures with fewer stacks tend to have larger neutral set sizes. In addition, we generalize neutral set size calculations from the previously studied many-to-one framework, where each sequence folds into a single energetically preferred structure, to a fuller many-to-many framework, where several low-energy structures are included. We find that structures with high neutral sets in one framework also tend to have large neutral sets in the other framework for a range of parameters and thus the choice of GP map does not fundamentally affect which structures have the largest neutral set sizes.

Network resilience of non-hub nodes failure under memory and non-memory based attacks with limited information.

Previous studies on network robustness mainly concentrated on hub node failures with fully known network structure information. However, hub nodes are often well protected and not accessible to damage or malfunction in a real-world networked system. In addition, one can only gain insight into limited network connectivity knowledge due to large-scale properties and dynamic changes of the network itself. In particular, two different aggression patterns are present in a network attack: memory based attack, in which failed nodes are not attacked again, or non-memory based attack; that is, nodes can be repeatedly attacked. Inspired by these motivations, we propose an attack pattern with and without memory based on randomly choosing n non-hub nodes with known connectivity information. We use a network system with the Poisson and power-law degree distribution to study the network robustness after applying two failure strategies of non-hub nodes. Additionally, the critical threshold 1 - p and the size of the giant component S are determined for a network configuration model with an arbitrary degree distribution. The results indicate that the system undergoes a continuous second-order phase transition subject to the above attack strategies. We find that 1 - p gradually tends to be stable after increasing rapidly with n. Moreover, the failure of non-hub nodes with a higher degree is more destructive to the network system and makes it more vulnerable. Furthermore, from comparing the attack strategies with and without memory, the results highlight that the system shows better robustness under a non-memory based attack relative to memory based attacks for n > 1. Attacks with memory can block the system's connectivity more efficiently, which has potential applications in real-world systems. Our model sheds light on network resilience under memory and non-memory based attacks with limited information attacks and provides valuable insights into designing robust real-world systems.

Influence of surface roughness on methane flow in shale kerogen nano-slits

Understanding the mechanism of methane transport in real kerogen nano-pores is critical to evaluate gas productivity in shale formations accurately. This work presents a novel numerical method to generate nano-slits with different surface roughness for Type II-D kerogen in molecular dynamics (MD) simulations. The methane adsorption and transport phenomenon in the kerogen nano-slits are then studied. A modified velocity model is proposed by considering the effect of surface roughness on the slip velocity and the effect of gas rarefaction on the methane viscosity. The ratio of the mass flow rate in the adsorption region to the total flow rate is between 57% and 78% at a channel width of 2.5 nm depending on the surface roughness and pressure, but it drops to between 6% and 16% at a channel width of 10 nm. Hence, two mathematical equations are derived to estimate the mass flow rate in the adoption region from either (1) the adsorption per apparent surface area, or (2) the product of the bulk density and the width of the adsorption region, respectively. Finally, a new flow enhancement model is derived by combining the modified velocity model and the equation for mass flow rate in the adsorption region. The flow enhancement increases with the Knudsen number but decreases with the increase of surface roughness. The proposed flow enhancement model can match the MD simulation well. The proposed model for methane flow enhancement covers the slip flow and transition flow regimes and can potentially be implemented to study the large-scale flow properties, such as permeability, of the shale matrix at various conditions to better evaluate the shale gas production performance.

Complex fitness landscape shapes variation in a hyperpolymorphic species.

It is natural to assume that patterns of genetic variation in hyperpolymorphic species can reveal large-scale properties of the fitness landscape that are hard to detect by studying species with ordinary levels of genetic variation. Here, we study such patterns in a fungus Schizophyllum commune, the most polymorphic species known. Throughout the genome, short-range linkage disequilibrium (LD) caused by attraction of minor alleles is higher between pairs of nonsynonymous than of synonymous variants. This effect is especially pronounced for pairs of sites that are located within the same gene, especially if a large fraction of the gene is covered by haploblocks, genome segments where the gene pool consists of two highly divergent haplotypes, which is a signature of balancing selection. Haploblocks are usually shorter than 1000 nucleotides, and collectively cover about 10% of the S. commune genome. LD tends to be substantially higher for pairs of nonsynonymous variants encoding amino acids that interact within the protein. There is a substantial correlation between LDs at the same pairs of nonsynonymous mutations in the USA and the Russian populations. These patterns indicate that selection in S. commune involves positive epistasis due to compensatory interactions between nonsynonymous alleles. When less polymorphic species are studied, analogous patterns can be detected only through interspecific comparisons.

Build up of yield stress fluids via chaotic emulsification

Stabilised dense emulsions display a rich phenomenology connecting microstructure and rheology. In this work, we study how an emulsion with a finite yield stress can be built via large-scale stirring. By gradually increasing the volume fraction of the dispersed minority phase, under the constant action of a stirring force, we are able to achieve a volume fraction close to . Despite the fact that our system is highly concentrated and not yet turbulent we observe a droplet size distribution consistent with the scaling, often associated with inertial range droplets breakup. We report that the polydispersity of droplet sizes correlates with the dynamics of the emulsion formation process. Additionally, we quantify the visco-elastic properties of the dense emulsion finally obtained and we demonstrate the presence of a finite yield stress. The approach reported can pave the way to a quantitative understanding of the complex interplay between the dynamics of mesoscale constituents and the large-scale flow properties of yield stress fluids.

Hierarchical N/O co-doped hard carbon derived from waste saccharomyces cerevisiae for lithium storage

Biomass derived hard carbon has been considered as an alternative anode for lithium ion battery due to its low cost, natural abundance and large-scale production properties. Herein, N/O co-doped hard carbon was fabricated by calcining and pickling waste saccharomyces cerevisiae, and the graphitization degree and surface functional groups in hard carbon was elaborately controlled by calcination temperature. The unique hierarchical structure with hollow nanoparticles on the surface of microparticles provided a buffer space for the insertion/extraction of Li+, the large interlayer distance favored the fast electrochemical kinetics. N/O heteroatom doping improved the intrinsic conductivity and afforded additional active sites for ions storage through pseudo-capacitive behavior. Consequently, the saccharomyces cerevisiae derived hard carbon with optimized graphitization degree and N/O doping delivered a high specific capacity of 307.4 mAh g−1 after 500 cycles at 1 A g−1. This work not only provided an optional anode for LIBs, but also enriched the high-value reutilization of waste saccharomyces cerevisiae.

Cross-scale dynamic interactions in compacting porous media as a trigger to pattern formation

SUMMARYPatterns in nature are often interpreted as a product of reaction-diffusion processes which result in dissipative structures. Thermodynamic constraints allow prediction of the final state but the dynamic evolution of the microprocesses is hidden. We introduce a new microphysics-based approach that couples the microscale cross-constituent interactions to the large-scale dynamic behaviour, which leads to the discovery of a family of soliton-like excitation waves. These waves can appear in hydromechanically coupled porous media as a reaction to external stimuli. They arise, for instance, when mechanical forcing of the porous skeleton releases internal energy through a phase change, leading to tight coupling of the pressure in the solid matrix with the dissipation of the pore fluid pressure. In order to describe these complex multiscale interactions in a thermodynamic consistent framework, we consider a dual-continuum system, where the large-scale continuum properties of the matrix–fluid interaction are described by a reaction-self diffusion formulation, and the small-scale dissipation of internal energy by a reaction-cross diffusion formulation that spells out the macroscale reaction and relaxes the adiabatic constraint on the local reaction term in the conventional reaction-diffusion formalism. Using this approach, we recover the familiar Turing bifurcations (e.g. rhythmic metamorphic banding), Hopf bifurcations (e.g. Episodic Tremor and Slip) and present the new excitation wave phenomenon. The parametric space is investigated numerically and compared to serpentinite deformation in subduction zones.

Spanning trees of recursive scale-free graphs.

We present a link-by-link rule-based method for constructing all members of the ensemble of spanning trees for any recursively generated, finitely articulated graph, such as the Dorogovtsev-Goltsev-Mendes (DGM) net. The recursions allow for many large-scale properties of the ensemble of spanning trees to be analytically solved exactly. We show how a judicious application of the prescribed growth rules selects for certain subsets of the spanning trees with particular desired properties (small world, extended diameter, degree distribution, etc.), and thus approximates and/or provides solutions to several optimization problems on undirected and unweighted networks. The analysis of spanning trees enhances the usefulness of recursive graphs as sophisticated models for everyday life complex networks.

Nanopatterning of rotating highly oriented pyrolytic graphite (0001) surfaces by ion beam irradiation: Experiments and modeling

We produce ordered surface nanostructures on azimuthally rotating HOPG(0001) by irradiation with low-energy ion beams. Thus, small dots at low fluences evolve into cellular structures at large fluences. This transition is a consequence of the fast growth of dots and their subsequent concatenation to form walls along the grain boundaries, leading to cells. The walls compete and ripen with continued ion beam irradiation, resulting in both coarsening and roughening of the cell pattern. We reproduce the same morphological and scaling behavior using different ions, Kr or Ar, so the mechanisms involved in pattern formation and coarsening are robust. We compare the experimental findings with simulations of a nonlinear continuum model based on ion-driven viscous flow, which reproduces many qualitative properties seen in the experiments. Such a comparison underscores the need for a suitable consideration of both external noise and the grain structure of the target to fully account for the large-scale properties of the process.

Growth kinetics of a single-walled carbon nanotube: Exact and simulation results

The single walled (SW) carbon nanotube (CNT), discovered in 1991, is a fascinating system for studying key processes relevant to nanomaterials synthesis. It has crucial technological applications, in particular in biomedecine, sensors and telecom networks. In this work, we study numerically the growth kinetics and surface roughness of a hexagonal SWCNT with zero chiral angle using Glauber dynamics. The SWCNT growth kinetics are described in terms of Markov processes whose states are given by its upper edge profile that we mapped onto a 5-vertex model. The model parameters considered are the supersaturation of the fluid phase or driving force and the temperature. The kinetic equation of the system is solved exactly by means of the transition matrix method for small samples and the steady state growth velocity is calculated. Kinetic Monte Carlo (KMC) simulations based on the Bortz–Kalos–Lebowitz (BKL) update algorithm are used to extract some large scale properties of the model.

Lumped geohydrological modelling for long-term predictions of groundwater storage and depletion

Excessive groundwater pumping exacerbates aquifer depletion and poses a major threat in regions all around the world that already suffer from overuse or climate change. In this situation, accurate and reliable predictions of long-term aquifer water balances are key prerequisites to manage groundwater sustainably. Compared to spatially explicit numerical models, lumped hydrological models are computationally fast, lean on data requirement and more accessable for quantifying uncertainty. However, lumped hydrological models are mainly designed to simulate river discharge only, not aquifer storage. Consequently, calibration only includes stream flow data. In this study, we hypothesize that we can extend a lumped hydrological models (here HBV) towards a lumped geohydrological model (LGhM) by additional, designated terms for water budget and groundwater storage. The model building is inspired by the geometry and hydrogeological large-scale properties of the catchment’s aquifers. Underground flow routing resembles major groundwater flow paths. The model is calibrated and evaluated on both groundwater storage data and surface discharge data. We apply our LGhM to a MODFLOW-based virtual reality describing the unconfined Wairau Plain Aquifer, New Zealand. We consider and discuss specifically river-groundwater exchange processes, long-term forecast of aquifer storage dynamics, and groundwater depletion in a hypothetical, persistent drought. Our model evaluation shows very plausible predictive capabilities in 40-year forecasts with synthetic weather time series and several years of groundwater depletion in the extreme drought case.

Introducing the thesan project: radiation-magnetohydrodynamic simulations of the epoch of reionization

ABSTRACT We introduce the thesan project, a suite of large volume ($L_\mathrm{box} = 95.5 \, \mathrm{cMpc}$) radiation-magnetohydrodynamic simulations that simultaneously model the large-scale statistical properties of the intergalactic medium during reionization and the resolved characteristics of the galaxies responsible for it. The flagship simulation has dark matter and baryonic mass resolutions of $3.1 \times 10^6\, {\rm M_\odot }$ and $5.8 \times 10^5\, {\rm M_\odot }$, respectively. The gravitational forces are softened on scales of 2.2 ckpc with the smallest cell sizes reaching 10 pc at z = 5.5, enabling predictions down to the atomic cooling limit. The simulations use an efficient radiation hydrodynamics solver (arepo-rt) that precisely captures the interaction between ionizing photons and gas, coupled to well-tested galaxy formation (IllustrisTNG) and dust models to accurately predict the properties of galaxies. Through a complementary set of medium resolution simulations we investigate the changes to reionization introduced by different assumptions for ionizing escape fractions, varying dark matter models, and numerical convergence. The fiducial simulation and model variations are calibrated to produce realistic reionization histories that match the observed evolution of the global neutral hydrogen fraction and electron scattering optical depth to reionization. They also match a wealth of high-redshift observationally inferred data, including the stellar-to-halo-mass relation, galaxy stellar mass function, star formation rate density, and the mass–metallicity relation, despite the galaxy formation model being mainly calibrated at z = 0. We demonstrate that different reionization models give rise to varied bubble size distributions that imprint unique signatures on the 21 cm emission, especially on the slope of the power spectrum at large spatial scales, enabling current and upcoming 21 cm experiments to accurately characterize the sources that dominate the ionizing photon budget.

Non-local dispersion and the reassessment of Richardson's t3-scaling law

The Richardson-scaling law states that the mean square separation of a fluid particle pair grows according to t3 within the inertial range and at intermediate times. The theories predicting this scaling regime assume that the pair separation is within the inertial range and that the dispersion is local, which means that only eddies at the scale of the separation contribute. These assumptions ignore the structural organization of the turbulent flow into large-scale shear layers, where the intense small-scale motions are bounded by the large-scale energetic motions. Therefore, the large scales contribute to the velocity difference across the small-scale structures. It is shown that, indeed, the pair dispersion inside these layers is highly non-local and approaches Taylor dispersion in a way that is fundamentally different from the Richardson-scaling law. Also, the layer's contribution to the overall mean square separation remains significant as the Reynolds number increases. This calls into question the validity of the theoretical assumptions. Moreover, a literature survey reveals that, so far, t3 scaling is not observed for initial separations within the inertial range. We propose that the intermediate pair dispersion regime is a transition region that connects the initial Batchelor- with the final Taylor-dispersion regime. Such a simple interpretation is shown to be consistent with observations and is able to explain why t3 scaling is found only for one specific initial separation outside the inertial range. Moreover, the model incorporates the observed non-local contribution to the dispersion, because it requires only small-time-scale properties and large-scale properties.

The angular-momentum flux in the solar wind observed during Solar Orbiter’s first orbit

Aims. We present the first measurements of the angular-momentum flux in the solar wind recorded by the Solar Orbiter spacecraft. Our aim is to validate these measurements to support future studies of the Sun’s angular-momentum loss. Methods. We combined 60-min averages of the proton bulk moments and the magnetic field measured by the Solar Wind Analyser and the magnetometer onboard Solar Orbiter. We calculated the angular-momentum flux per solid-angle element using data from the first orbit of the mission’s cruise phase in 2020. We separated the contributions from protons and from magnetic stresses to the total angular-momentum flux. Results. The angular-momentum flux varies significantly over time. The particle contribution typically dominates over the magnetic-field contribution during our measurement interval. The total angular-momentum flux shows the largest variation and is typically anti-correlated with the radial solar-wind speed. We identify a compression region, potentially associated with a co-rotating interaction region or a coronal mass ejection, which leads to a significant localised increase in the angular-momentum flux, albeit without a significant increase in the angular momentum per unit mass. We repeated our analysis using the density estimate from the Radio and Plasma Waves instrument. Using this independent method, we find a decrease in the peaks of positive angular-momentum flux, but otherwise, our results remain consistent. Conclusions. Our results largely agree with previous measurements of the solar wind’s angular-momentum flux in terms of amplitude, variability, and dependence on radial solar-wind bulk speed. Our analysis highlights the potential for more detailed future studies of the solar wind’s angular momentum and its other large-scale properties with data from Solar Orbiter. We emphasise the need for studying the radial evolution and latitudinal dependence of the angular-momentum flux in combination with data from Parker Solar Probe and other assets at heliocentric distances of 1 au and beyond.

Subhalo abundance matching through the lens of a hydrodynamical simulation

ABSTRACT We use the IllustrisTNG100 hydrodynamical simulation to study the dependence of the galaxy two-point correlation function on a broad range of secondary subhalo and galactic properties. We construct galaxy mock catalogues adopting a standard subhalo abundance matching scheme coupled with a secondary assignment between galaxy colour or specific star formation rate and the following subhalo properties: starvation redshift zstarve, concentration at infall, overdensity $\delta _R^{\rm env}$, tidal anisotropy αR, and tidal overdensity δR. The last two quantities allow us to fully characterize the tidal field of our subhaloes, acting as mediators between their internal and large-scale properties. The resulting mock catalogues overall return good agreement with the IllustrisTNG100 measurements. The accuracy of each model strongly depends on the correlation between the secondary galaxy and subhalo properties employed. Among all the subhalo proxies tested, we find that zstarve and cinfall are the ones that best trace the large-scale structure, producing robust clustering predictions for different samples of red/blue and quenched/star-forming galaxies.

Design Principles for Long-Range Energy Transfer at Room Temperature

Under physiological conditions, ballistic long-range transfer of electronic excitations in molecular aggregates is generally expected to be suppressed by noise and dissipative processes. Hence, quantum phenomena are not considered to be relevant for the design of efficient and controllable energy transfer over significant length scales and timescales. Contrary to this conventional wisdom, here we show that the robust quantum properties of small configurations of repeating clusters of molecules can be used to tune energy-transfer mechanisms that take place on much larger scales. With the support of an exactly solvable model, we demonstrate that coherent exciton delocalization and dark states within unit cells can be used to harness dissipative phenomena of varying nature (thermalization, fluorescence, nonradiative decay, and weak intersite correlations) to support classical propagation over macroscopic distances. In particular, we argue that coherent delocalization of electronic excitations over just a few pigments can drastically alter the relevant dissipation pathways that influence the energy-transfer mechanism and thus serve as a molecular control tool for large-scale properties of molecular materials. Building on these principles, we use extensive numerical simulations to demonstrate that they can explain currently not-understood measurements of micron-scale exciton diffusion in nanofabricated arrays of bacterial photosynthetic complexes. Based on these results, we provide quantum design guidelines at the molecular scale to optimize both energy-transfer speed and range over macroscopic distances in artificial light-harvesting architectures.2 MoreReceived 4 June 2019Revised 15 July 2021Accepted 19 July 2021DOI:https://doi.org/10.1103/PhysRevX.11.041003Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasDiffusionEnergy materialsExcitonsQuantum state engineeringPhysical SystemsNonequilibrium systemsPhotosystemsTechniquesMaster equationCondensed Matter, Materials & Applied PhysicsInterdisciplinary Physics

Identifying and correcting spatial bias in opportunistic citizen science data for wild ungulates in Norway.

Many publications make use of opportunistic data, such as citizen science observation data, to infer large‐scale properties of species’ distributions. However, the few publications that use opportunistic citizen science data to study animal ecology at a habitat level do so without accounting for spatial biases in opportunistic records or using methods that are difficult to generalize. In this study, we explore the biases that exist in opportunistic observations and suggest an approach to correct for them. We first examined the extent of the biases in opportunistic citizen science observations of three wild ungulate species in Norway by comparing them to data from GPS telemetry. We then quantified the extent of the biases by specifying a model of the biases. From the bias model, we sampled available locations within the species’ home range. Along with opportunistic observations, we used the corrected availability locations to estimate a resource selection function (RSF). We tested this method with simulations and empirical datasets for the three species. We compared the results of our correction method to RSFs obtained using opportunistic observations without correction and to RSFs using GPS‐telemetry data. Finally, we compared habitat suitability maps obtained using each of these models. Opportunistic observations are more affected by human access and visibility than locations derived from GPS telemetry. This has consequences for drawing inferences about species’ ecology. Models naïvely using opportunistic observations in habitat‐use studies can result in spurious inferences. However, sampling availability locations based on the spatial biases in opportunistic data improves the estimation of the species’ RSFs and predicted habitat suitability maps in some cases. This study highlights the challenges and opportunities of using opportunistic observations in habitat‐use studies. While our method is not foolproof it is a first step toward unlocking the potential of opportunistic citizen science data for habitat‐use studies.

Cross-dataset reproducibility of human retinotopic maps

Population receptive field (pRF) models fit to fMRI data are used to non-invasively measure retinotopic maps in human visual cortex, and these maps are a fundamental component of visual neuroscience experiments. Here, we examined the reproducibility of retinotopic maps across two datasets: a newly acquired retinotopy dataset from New York University (NYU) (n = 44) and a public dataset from the Human Connectome Project (HCP) (n = 181). Our goal was to assess the degree to which pRF properties are similar across datasets, despite substantial differences in their experimental protocols. The two datasets simultaneously differ in their stimulus apertures, participant pool, fMRI protocol, MRI field strength, and preprocessing pipeline. We assessed the cross-dataset reproducibility of the two datasets in terms of the similarity of vertex-wise pRF estimates and in terms of large-scale polar angle asymmetries in cortical magnification. Within V1, V2, V3, and hV4, the group-median NYU and HCP vertex-wise polar angle estimates were nearly identical. Both eccentricity and pRF size estimates were also strongly correlated between the two datasets, but with a slope different from 1; the eccentricity and pRF size estimates were systematically greater in the NYU data. Next, to compare large-scale map properties, we quantified two polar angle asymmetries in V1 cortical magnification previously identified in the HCP data. The NYU dataset confirms earlier reports that more cortical surface area represents horizontal than vertical visual field meridian, and lower than upper vertical visual field meridian. Together, our findings show that the retinotopic properties of V1, V2, V3, and hV4 can be reliably measured across two datasets, despite numerous differences in their experimental design. fMRI-derived retinotopic maps are reproducible because they rely on an explicit computational model of the fMRI response. In the case of pRF mapping, the model is grounded in physiological evidence of how visual receptive fields are organized, allowing one to quantitatively characterize the BOLD signal in terms of stimulus properties (i.e., location and size). The new NYU Retinotopy Dataset will serve as a useful benchmark for testing hypotheses about the organization of visual areas and for comparison to the HCP 7T Retinotopy Dataset.