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Figure 4 in Two species of the genus Nilotanypus (Diptera: Chironomidae) in Europe
Figure 4. Neighbour joining tree with boostrap values on branches (500 replicates) generated by MEGA 11 (Tamura et al. 2021). Distances used were uncorrected p-distances, all codon positions were included. First alphanumeric code is BOLD Process ID, last alphanumeric code is GenBank accession.
Figure 8 in From Eradication to Containment: Invasion of French Polynesia by Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) and Releases of Two Natural Enemies: A 17-Year Case Study
Figure 8. Dates of detection of B. dorsalis and introduction of F. arisanus on the various islands of French Polynesia.
Data for "Site-Specific Management Zones Delineation Based on Apparent Soil Electrical Conductivity in Two Contrasting Fields of Southern Brazil"
<p>This dataset contains soybean yield, soil property values, and apparent electrical conductivity (ECa) from two farms located in the South of Brazil. The data description and methods can be found in the paper:</p> <p><strong>Bottega, E. L., Safanelli, J. L., Zeraatpisheh, M., Amado, T. J. C., Queiroz, D. M. de, & Oliveira, Z. B. de. (2022). Site-Specific Management Zones Delineation Based on Apparent Soil Electrical Conductivity in Two Contrasting Fields of Southern Brazil. In Agronomy (Vol. 12, Issue 6, p. 1390). MDPI AG. https://doi.org/10.3390/agronomy12061390</strong></p> <p>Abstract:</p> <p>Management practices that aim to increase the profitability of agricultural production with minimal environmental impact must consider within-field soil variability, and this site-specific management can be addressed by precision agriculture (PA). Thus, this work aimed to investigate which key soil attributes are distinguishable management zones (MZ) delineated based on the soil apparent electrical conductivity (ECa), using fuzzy k-means, in two fields with contrasting soil textures in southern Brazil. For this, a grid scheme (50 × 50 m) was applied to measure ECa, conduct soil sampling for analysis, and determine soybean yield. The MZ were delineated based on the ECa spatial distribution, and statistical non-parametric tests (p < 0.05) were employed to compare the soil chemical and physical attributes among MZ. The management zones were able to distinguish the average values of Clay, Silt, pH, Ca<sup>2+</sup>, Mg<sup>2+</sup>, SB, Al<sup>3+</sup>, H<sup>+</sup> + Al<sup>3+</sup>, AS%, and BS%. In the field classified as sandy clay loam texture, management zones were able to differentiate the average values of soybean yield, Clay, Ca<sup>2+</sup>, Mg<sup>2+</sup>, SB, and CEC. Thus, this study supports the ECa as an efficient tool for delineating MZ of contrasting cropland soils in southern Brazil to understand the within-field soil variability and adjust the inputs accordingly.</p>
Table 1 in Two unusual new species of Caleremaeus (Acari: Oribatida) from eastern North America, with redescription of C. retractus and reevaluation of the genus
<p><b>Table 1</b> Ontogeny of leg setae and solenidia in <i>Caleremaeus</i> species 1, 2.</p><table><tbody><tr><th></th><th>Trochanter</th><th>Femur</th><th>Genu</th><th>Tibia</th><th>Tarsus</th></tr><tr><th>Leg I</th></tr></tbody><tbody><tr><th>Larva</th><td><i>-</i></td><td><i>d, bv"</i></td><td><i>(l), dσ</i></td><td><i>(l), v', dφ</i> 1</td><td><i>(ft), (tc), (p), (u), (a), s, (pv), (pl), e, ω</i> 1</td></tr><tr><th>Protonymph</th><td><i>-</i></td><td><i>-</i></td><td><i>-</i></td><td><i>-</i></td><td><i>ω</i> 2</td></tr><tr><th>Deutonymph</th><td><i>-</i></td><td><i>(l)</i></td><td><i>-</i></td><td><i>φ</i> 2</td><td><i>-</i></td></tr><tr><th>Tritonymph</th><td><i>v'</i></td><td><i>-</i></td><td><i>v'</i></td><td><i>v"</i></td><td><i>(it)</i></td></tr><tr><th>Adult</th><td><i>-</i></td><td><i>-</i></td><td>[<i>d</i> lost]</td><td>[<i>d</i> lost]</td><td><i>v', l"</i></td></tr><tr><th>Leg II</th><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Larva</th><td><i>-</i></td><td><i>d, bv"</i></td><td><i>(l), dσ</i></td><td><i>l', v', dφ</i></td><td><i>(ft), (tc), (p), (u), (a), s, (pv), ω</i></td></tr><tr><th>Protonymph</th><td><i>-</i></td><td><i>-</i></td><td><i>-</i></td><td><i>-</i></td><td><i>-</i></td></tr><tr><th>Deutonymph</th><td><i>-</i></td><td><i>(l)</i></td><td><i>-</i></td><td><i>l"</i></td><td><i>-</i></td></tr><tr><th>Tritonymph</th><td><i>v'</i></td><td><i>-</i></td><td><i>-</i></td><td><i>v"</i></td><td><i>-</i></td></tr><tr><th>Adult</th><td><i>-</i></td><td><i>-</i></td><td>[<i>d</i> lost]</td><td>[<i>d</i> lost]</td><td><i>l"</i>, (<i>it</i>)</td></tr><tr><th>Leg III</th><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Larva</th><td><i>-</i></td><td><i>d, ev'</i></td><td><i>l’, dσ</i></td><td><i>v', dφ</i></td><td><i>(ft), (tc), (p), (u), (a), s, (pv)</i></td></tr><tr><th>Protonymph</th><td><i>-</i></td><td><i>-</i></td><td><i>-</i></td><td><i>-</i></td><td><i>-</i></td></tr><tr><th>Deutonymph</th><td><i>v', l’</i></td><td><i>l'</i></td><td><i>-</i></td><td><i>l'</i> 3</td><td><i>-</i></td></tr><tr><th>Tritonymph</th><td><i>-</i></td><td><i>-</i></td><td><i>-</i></td><td><i>l'</i> 3, <i>v"</i></td><td><i>-</i></td></tr><tr><th>Adult</th><td><i>-</i></td><td><i>-</i></td><td>[<i>d</i> lost]</td><td>[<i>d</i> lost]</td><td><i>it"</i></td></tr><tr><th>Leg IV</th><td></td><td></td><td></td><td></td><td></td></tr><tr><th>Protonymph</th><td><i>-</i></td><td><i>-</i></td><td><i>-</i></td><td><i>-</i></td><td><i>ft", (p), (u), (pv)</i></td></tr><tr><th>Deutonymph</th><td><i>-</i></td><td><i>d, ev'</i></td><td><i>d, l'</i></td><td><i>v', dφ</i></td><td><i>(瑣), a",</i></td></tr><tr><th>Tritonymph</th><td><i>v'</i></td><td><i>-</i></td><td><i>-</i></td><td><i>l', v"</i></td><td><i>-</i></td></tr><tr><th>Adult</th><td><i>-</i></td><td><i>-</i></td><td><i>-</i></td><td>[<i>d</i> lost]</td><td>-</td></tr></tbody></table><p><sup>1</sup> Adult data are based on all studied species and populations, including <i>C. monilipes</i> from Sweden, Germany and Spain. Ontogenetic data are from <i>C. arboricolus</i> <b>n. sp.</b> (topotypical population), <i>C. retractus</i> (near-topotypical population), a species in the ‘retractus group’ (New York population; see text), and <i>C. monilipes</i> (Norwegian population; see Seniczak and Seniczak 2019 and corrections in R12); from each population all instars were studied, except no larva was available from the <i>C. retractus</i> near-topotypical population.</p><p><sup>2</sup> Setae (Roman letters) and solenidia (<i>σ, φ, ω</i>) are shown where they are first added and are assumed present through the rest of ontogeny, unless noted in brackets. Setae in parentheses represent pseudosymmetrical pairs; dash indicates no addition; underline indicates solenidion is coupled to seta <i>d</i>, in same alveolus.</p><p><sup>3</sup> Seta <i>l'</i> of tibia III is invariably deutonymphal in <i>C. arboricolus</i>, <i>C. retractus</i> and the ‘retractus group’ from New York. By contrast, in <i>C. monilipes l'</i> first forms along with <i>v"</i> in the tritonymph, according to Seniczak and Seniczak (2019); appropriately, <i>l'</i> is absent from both tibiae III of our single deutonymph from Sweden.</p>
Two-Streams Revisited: General Equations, Exact Coefficients, and Optimized Closures (data and code)
<p>Code and data necessary to reproduce figures presented in our paper "Two-Streams Revisited: General Equations, Exact Coefficients, and Optimized Closures" (Ho, and Pincus 2024). The figures and code to reproduce them are in the Jupyter Notebook `Two-stream_revisited_code.ipynb` which includes 3D interactive versions of Figures 6, 7, 10. We also provided code to further explore the dataset of coupling coefficients `twostreams_revisited_data.npz`.</p> <p>Identical GitHub repository: <a href="https://github.com/LDEO-CREW/Two-streams_revisited_code">https://github.com/LDEO-CREW/Two-streams_revisited_code</a>.</p> <p>This record is for the post-review (final) version of the paper, with a slight code change to accomodate `PythonicDISORT > 0.9.1`.</p> <p>You may contact me, Dion, through dh3065@columbia.edu if you have any questions.</p> <p>Requirements</p> <ul> <li>Python 3.8+</li> <li>`PythonicDISORT >= 0.8.0` (A minor code change will be required for `PythonicDISORT <= 0.9.1`)</li> <li>`numpy >= 1.8.0`</li> <li>`scipy >= 1.8.0`</li> <li>`matplotlib >= 3.6.0`</li> <li>`jupyter > 1.0.0`</li> <li>`notebook > 6.5.2`</li> <li>`autograd >= 1.5`</li> <li>`plotly >= 5.22.0`</li> </ul> <p> </p>
Surrogate Modeling Benchmark - Two-dimensional heat diffusion model
<p>This dataset is related to the Two-dimensional heat diffusion model benchmark case. A detailed description of the benchmark case can be found on the public online community website UQWorld: <a href="https://uqworld.org/t/benchmark-case-two-dimensional-heat-diffusion-model/" target="_blank" rel="noopener">https://uqworld.org/t/benchmark-case-two-dimensional-heat-diffusion-model/</a>.</p> <p>The experimental designs include datasets with 400, 800, 1200, 1600, and 2000 samples, each generated using optimized maximin distance Latin Hypercube Sampling (LHS) with 1000 iterations. Each dataset is replicated 20 times. The validation set contains 100,000 samples generated by Monte Carlo simulation. Each dataset contains input samples and the corresponding computational model responses.</p> <h2>Description of the dataset file</h2> <p>The dataset file includes two variables:</p> <ul> <li><em>ExpDesigns</em>, and</li> <li><em>ValidationSet</em>.</li> </ul> <p>Both variables are Matlab structures with fields <em>X</em>, <em>Y</em>, and <em>nSamples</em>. Variable <em>ExpDesigns</em> is a non-scalar structure sized according to the number of experimental design groups. Each field of <em>X</em> for the i-th element of the struct array contains replicated datasets, forming a matrix of size [number of samples] x [dimensionality] x [number of replications]. Similarly, each field of Y for the i-th element contains replicated computational model responses that correspond to the experimental design of the same replication, sized [number of samples] x [number of model outputs] x [number of replications]. The same structure logic applies to the <em>ValidationSet</em> variable, except it contains only one dataset per benchmark case.</p> <p>The structure can be summarized as follows:</p> <ul> <li>ExpDesigns(i).X(j,k,l) <ul> <li>i: dataset group,</li> <li>j: sample index,</li> <li>k: variable index, and</li> <li>l: replication index.</li> </ul> </li> </ul> <ul> <li>ExpDesigns(i).Y(j,m,l) <ul> <li>i, j, l: same as above,</li> <li>m: computational model output index.</li> </ul> </li> </ul> <ul> <li>ValidationSet.X(j,k) <ul> <li>j, k: same as above.</li> </ul> </li> </ul> <ul> <li>ValidationSet.Y(j,m) <ul> <li>j, m: same as above.</li> </ul> </li> </ul> <h2>Description of benchmarked metamodel competitors</h2> <p>The selection of competitors was based on our experience with meta-modeling and includes various metamodel types: Polynomial Chaos Expansions (PCE), Polynomial Chaos Kriging (PCK), and Kriging. Given that each metamodel has many hyperparameters, we chose the most general settings to address different benchmark case difficulties, including dimensionality, nonlinearity, and non-monotonicity.</p> <p>For <strong>Polynomial Chaos Expansions (PCE)</strong>, we used a polynomial degree and q-norm adaptivity approach. This approach adaptively increases the maximum polynomial degree and truncation q-norm until the estimated leave-one-out error starts increasing. Maximum polynomial interaction terms were limited to 2 due to the memory requirements for large model dimensionality and large experimental designs. We tested three different solvers to calculate the PCE coefficients: Least Angle Regression (LARS), Orthogonal Matching Pursuit (OMP), and Subspace Pursuit (SP).</p> <p><strong>Polynomial Chaos Kriging (PCK)</strong> employs a sequential combination strategy of PCE and Kriging. PCE uses degree adaptivity with a fixed q-norm. The maximum number of interactions is again set to 2 with the LARS solver. Ordinary Kriging is applied using the Matérn-5/2 correlation family, ellipsoidal, and anisotropic correlation function. We used a hybrid genetic algorithm to optimize the hyperparameters.</p> <p>We benchmarked both linear and ordinary <strong>Kriging</strong>, including Matérn-5/2 and Gaussian correlation families and separable and ellipsoidal correlation, resulting in eight different Kriging competitors. The hyperparameters were calculated using a hybrid covariance matrix adaptation-evolution strategy optimization.</p> <p>For further details on the settings, please refer to the competitors.m file and UQLab user manuals:</p> <ul> <li>S. Marelli, N. Luethen, B. Sudret, <a href="https://www.uqlab.com/pce-user-manual">UQLab User Manual – Polynomial Chaos Expansions</a>, Report UQLab-V2.1-104, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>C. Lataniotis, D. Wicaksono, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/kriging-user-manual">UQLab User Manual – Kriging (Gaussian Process Modeling)</a>, Report UQLab-V2.1-105, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>R. Schoebi, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/pck-user-manual">UQLab User Manual – Polynomial Chaos Kriging</a>, Report UQLab-V2.0-109, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2022.</li> </ul> <h2>Description of the results file</h2> <p>The results file contains one variable: <em>Metrics</em>. It is a Matlab structure with fields corresponding to each competitor (currently 12). Each competitor field contains data of type non-scalar struct array. The performance metrics included are RelMSE, RelRMSE, RelMAE, MAPE, Q2, and RelCVErr. Each field of Metrics.(CompetitorName) for the i-th element of the struct array contains metrics corresponding to the replicated dataset and the competitor, structured as follows:</p> <ul> <li>Metrics.(CompetitorName)(i).(MetricName)(l)<br> <ul> <li>i: dataset group,</li> <li>l: replication index.</li> </ul> </li> </ul> <p>The description of the performance measures (metrics) can be found here: <a href="https://uqworld.org/t/metamodel-performance-measures/" target="_blank" rel="noopener">https://uqworld.org/t/metamodel-performance-measures/</a>.</p> <h2>Additional files</h2> <p>We provide files in three languages (MATLAB, Python, and Julia) to showcase how to work with datasets, results, and their visualization. The files are called <em>working_with_datafiles.*</em> (the extension depends on the selected language).</p> <h2>Acknowledgment</h2> <p>This project was supported by the Open Research Data Program of the ETH Board under Grant number EPFL SCR0902285. The calculations were run on the Euler cluster of ETH Zürich using the MATLAB-based UQLab software developed at the Chair of Risk, Safety and Uncertainty Quantification of ETH Zürich.</p>
Figure 12 in Two new plioplatecarpine mosasaurs (Mosasauridae; Plioplatecarpinae) of the genus Ectenosaurus from the Upper Cretaceous of North America
Figure 12: Ectenosaurus shannoni sp. nov., ALMNH:Paleo:5452. A–D. Left quadrate fragment with suprastapedial-infrastapedial contact in posterior (A), anterior/interior (B), lateral (C), and medial (D) views. Arrow indicates interior flange of infrastapedial process. Scale bar=2 cm.
Figure 10 in Two new plioplatecarpine mosasaurs (Mosasauridae; Plioplatecarpinae) of the genus Ectenosaurus from the Upper Cretaceous of North America
Figure 10. Ectenosaurus tlemonectes sp. nov., YPM VP4673. Cervical vertebra in left lateral view. Arrow indicates ventral ridge connecting the hypapophyseal peduncle and cotyle. Scale bar=5 cm.
Figure 7 in Two new plioplatecarpine mosasaurs (Mosasauridae; Plioplatecarpinae) of the genus Ectenosaurus from the Upper Cretaceous of North America
Figure 7. Ectenosaurus tlemonectes sp. nov., YPM VP4673. A, B. Right coronoid in lateral (A) and medial (B) views. C, D. Left jugal in lateral (C) and medial (D) views. E, F. Right jugal in lateral (E) and medial (F) views. Scale bars for A, B=5 cm. Scale bar for C–F=2 cm.
Figure 3 in Two new plioplatecarpine mosasaurs (Mosasauridae; Plioplatecarpinae) of the genus Ectenosaurus from the Upper Cretaceous of North America
Figure 3. Ectenosaurus tlemonectes sp. nov., YPM VP4673. A–C. Left quadrate in medial (A), anterior (B), and posterior (C) views. D–F. Left squamosal in ventral (D), lateral (E), and medial (F) views. Arrow indicates suprastapedial/infrastapedial contact. Scale bars=5 cm.
Figure 14 in Two new plioplatecarpine mosasaurs (Mosasauridae; Plioplatecarpinae) of the genus Ectenosaurus from the Upper Cretaceous of North America
Figure 14: Phylogenetic trees. A. Strict consensus tree of 30 most parsimonious trees (MPTs; tree length=255, CI=0.6680; HI=0.3137; RI=0.7377; RC=0.5063). B. Tree produced using constraint option and assuming monophyly of Ectenosaurus, 30 MPTs (tree length=255, CI=06680; HI=0.3320; RI=0.7877; RC=0.5063).
Figure 6 in Two new plioplatecarpine mosasaurs (Mosasauridae; Plioplatecarpinae) of the genus Ectenosaurus from the Upper Cretaceous of North America
Figure 6. Ectenosaurus tlemonectes sp. nov., YPM VP 4673, A–D. Detail of pathologic and non-pathologic anterior terminus of left dentary in left lateral (nonpathologic) (A), right lateral (pathologic) (B), right medial (pathologic) (C), and right dorsal (pathologic) (D) views. Scale bars for A–C=5 cm. Scale bar for D=3 cm.
Figure 11 in Two new plioplatecarpine mosasaurs (Mosasauridae; Plioplatecarpinae) of the genus Ectenosaurus from the Upper Cretaceous of North America
Figure 11. Ectenosaurus shannoni sp. nov., ALMNH:Paleo:5452. A, B. Frontal in dorsal (A) and ventral (B) views. C, D. Parietal in dorsal (C) and ventral (D) views. Abbreviations: POF, excavation for postorbitofrontal; PRF, excavation for prefrontal; VSR, ventral separation ridge. Scale bars=5 cm.
Figure 2 in Two new plioplatecarpine mosasaurs (Mosasauridae; Plioplatecarpinae) of the genus Ectenosaurus from the Upper Cretaceous of North America
Figure 2. Ectenosaurus tlemonectes sp. nov., YPM VP4673. A-C. Frontal in dorsal (A) and ventral (B) views. C. Detail of olfactory canal. Abbreviations: POF, excavation for postorbitofrontal; PRF, excavation for prefrontal; VSR, ventral separation ridge. Scale bars=5 cm.
Figure 9 in Two new plioplatecarpine mosasaurs (Mosasauridae; Plioplatecarpinae) of the genus Ectenosaurus from the Upper Cretaceous of North America
Figure 9. Ectenosaurus tlemonectes sp. nov., YPM VP4673. A–E. Left angular in lateral (A), medial (B), anterior (C), dorsal (D), and ventral (E) views. F–H. Right angular in lateral (F), anterior (G), and medial (H) views. I–K. Right splenial in lateral (I), anterior (J), and dorsal (K) view. L. Articulated right splenial and angular. Arrows point to pathologic ventral rugosity on left angular. Abbreviations: SAS, surangular articulation surface. Scale bars for A–E, I–K, L=5 cm. Scale bars for F–H=3 cm.
Figure 13 in Two new plioplatecarpine mosasaurs (Mosasauridae; Plioplatecarpinae) of the genus Ectenosaurus from the Upper Cretaceous of North America
Figure 13: Ectenosaurus shannoni sp. nov., ALMNH:Paleo:5452. A–C. Right splenial in lateral (A), medial (B), and posterior (C) views. D, E. Right coronoid in lateral (D) and medial (E) views. F. Laser scan of cast of right coronoid of ALMNH:Paleo:5452 in posteromedial view. G. Cervical vertebra in ventral view. H. Caudal vertebra in ventral view. Scale bars for A–F=5 cm. Scale bar for G=4 cm.
Figure 8 in Two new plioplatecarpine mosasaurs (Mosasauridae; Plioplatecarpinae) of the genus Ectenosaurus from the Upper Cretaceous of North America
Figure 8. Ectenosaurus tlemonectes sp. nov., YPM VP4673. A, B. Left surangular-articular in lateral (A) and medial (B) views. C. Close-up of glenoid fossa in lateral view. D. Close-up of glenoid fossa in medial view. E. Close-up of glenoid fossa in dorsal view. Arrow indicates postglenoid notch. Scale bars=5 cm.
Figure 1. Geography and Stratigraphy. A in Two new plioplatecarpine mosasaurs (Mosasauridae; Plioplatecarpinae) of the genus Ectenosaurus from the Upper Cretaceous of North America
Figure 1. Geography and Stratigraphy. A. Generalized map of the United States during the Santonian Stage of the Cretaceous (~85 mya) with localities for YPM VP4673 (1) and ALMNH:Paleo:4673 (2). B. Generalized correlation chart of Coniacian to Campanian surface stratigraphy for Kansas and Alabama, USA. Base map data modified from Blakey (2014). Stratigraphic chart modified from Szabo et al. (1988) and Everhart (2014).
Figure 5 in Two new plioplatecarpine mosasaurs (Mosasauridae; Plioplatecarpinae) of the genus Ectenosaurus from the Upper Cretaceous of North America
Figure 5. Ectenosaurus tlemonectes sp. nov., YPM VP4673. A–E. Dentaries in left lateral (A), left medial (B), left dorsal (C), right lateral (D), and right medial (E) views. Scale bars=10 cm.
Figure 4 in Two new plioplatecarpine mosasaurs (Mosasauridae; Plioplatecarpinae) of the genus Ectenosaurus from the Upper Cretaceous of North America
Figure 4. Ectenosaurus tlemonectes sp. nov., YPM VP4673, A–C. Premaxilla in dorsal (A) and ventral (B) views. C. Detail of dorsal ridge. D, E. Right maxilla in lateral (D) and medial (E) views. Scale bars=10 cm.
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.