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1,291 results for “gap”
Figure 2 in Taxonomy of the shieldtail snake genus Teretrurus Beddome, 1886 (Serpentes: Uropeltidae) with a revised key and remarks on the geographic gaps in the Western Ghats, Peninsular India
Figure 2. Preserved specimens of Teretrurus species: (a) T. travancoricus ZSI/WGRC/2135-6, (b) T. hewstoni ZSI/WGRC/3124, (c) T. sanguineus CSPT/S-10, (d) T. rhodogaster CSPT/S-14. Photos: S. R. Ganesh.
Brightness cues affect gap negotiation behaviour in zebra finches
<p>Flying animals have had to evolve robust and effective guidance strategies for dealing with habitat clutter. Birds and insects use optic flow cues to sense and avoid obstacles, but orchid bees have also been shown to use brightness cues during gap negotiation. Brightness cues might therefore be of general importance in structuring visually guided flight. To test the hypothesis that brightness cues affect gap negotiation behaviours in birds, we presented captive zebra finches <em>Taeniopygia guttata</em> with a symmetric or asymmetric background brightness distribution on the other side of a tunnel. The background brightness conditions influenced both the birds' decision to enter the tunnel aperture, and their flight direction upon exit. Zebra finches were more likely to initiate flight through the tunnel if they could see a bright background through it; they were also more likely to fly to the bright side upon exiting. We found no evidence of the centring response that would be expected if optic flow cues were balanced bilaterally during gap negotiation. Instead, the birds entered the tunnel by targeting a clearance of approximately one wing length from its near edge. Brightness cues therefore affect how zebra finches structure their flight when negotiating gaps in enclosed environments.</p>
Fig. 2 in Filling the Silurian gap of solutan echinoderms with the description of new species of Dehmicystis from Spain
Fig. 2. Solutan echinoderm Dehmicystis globulus (Dehm, 1934) from the the Lower Devonian Hunsrück Slate, Germany. A. Holotype (SMF-HS 432), almost complete specimen showing the theca and posterior appendage. Note anus in on right side. B. SMF-HS 969, slab containing three individuals. Note that individual 1 has the anus on "left" position. Repository number assigned to a replica of the newly prepared slab. Photographs courtesy of Ulrich Jansen.
Fig. 4 in Filling the Silurian gap of solutan echinoderms with the description of new species of Dehmicystis from Spain
Fig. 4. Camera lucida drawings of solutan echinoderm Dehmicystis ariasi sp. nov. from the Llagarinos Formation, Ludlow, Silurian, northern Central Iberian Zone, Spain; with interpretation of main anatomical parts.
Fig. 3 in Filling the Silurian gap of solutan echinoderms with the description of new species of Dehmicystis from Spain
Fig. 3. Solutan echinoderm Dehmicystis ariasi sp. nov. from the Llagarinos Formation, Ludlow, Silurian, northern Central Iberian Zone, Spain. A. MGM-208S. A1, complete specimen in dorsal aspect preserving proxistele and feeding appendage above the body. A2, counterpart of same specimen. A3, detail of feeding appendage consisting on biserial floor plates and cover plates. Gonopore on single plate, and a possible hydropore close to the feeding appendage attachment. A4, detail of the periproct on right position. Note that the periproct is visible from the inside and was located on dorsal right part of the theca. A5, detail of the appendage preserving the complete proxistele and proximal dististele. Photographs are from latex casts whitened with ammonium chloride sublimated.
Forest gap dynamics with repeat lidar in Berchtesgaden National Park - Data and analysis scripts
<p>This repository holds data and code for the paper: Krüger, K., Senf, C., Jucker, T., Pflugmacher, D., Seidl, R. (2024). Gap expansion is the dominant driver of canopy openings in a temperate mountain forest landscape. Journal of Ecology. <span><a href="http://doi.org/10.1111/1365-2745.14320">http://doi.org/10.1111/1365-2745.14320</a> </span></p> <p><strong>NOTE:</strong> This is a static repository, but the project might evolve. See the connected GitHub repository for latest updates!</p> <p>All data to reproduce the results are available, all other layers can be generated with the code provided in this repository. The Canopy Height Models underlying the analysis and respective processing scripts for the lidar data, are available from the corresponding author upon reasonable request. Gap layers derived from the Canopy Height Models are available in this repository.</p> <p>Empty folders are set up to follow the directory structure of the scripts. </p>
Haplotype-Resolved and Gap-Free Genome of a Floating Aquatic Plant from the Oryzeae Tribe, Hygroryza aristata
<p><em><span>Hygroryza aristata</span></em><span> (Retz.) Nees ex Wight & Arn.</span><span> </span><span>is a floating aquatic plant. <span>Genomic DNA and RNA samples of </span><em><span>H. aristata</span></em><span> were extracted from plants clonally propagated from a single individual. Long-read sequencing of PacBio HiFi and ultra-long (UL) ONT (read lengths > 100 kb), and short-read sequencing of Hi-C, WGS, and RNA-seq, were performed. </span></span></p> <p><span>For genome assembly, 31.91 Gb of PacBio HiFi and 22.36 Gb of UL-ONT sequencing data sets were utilized. Assembly was conducted using HiFiAsm (v0.20.0-r639) under HiFi + UL-ONT mode with the following parameters: -l 3 -r 5 -a 6 -n 10 --ctg-n 10 -w 63 -k 63. Chromosome IDs and strand directions were determined by aligning the assemblies to the rice (</span><em><span>O. sativa</span></em><span>) genome. The resulting assemblies of unphased two haplotypes, designated as hap1 and hap2, were obtained. </span></p> <p><span><span>Both hap1 and hap2 are complete genomes,<span> </span>each comprising 12 chromosomes with genome sizes of 349.74 Mb and 347.98 Mb, respectively. Notably, both haplotypes are gap-free. Telomere detection using Seqtk telo (v1.4-r122) revealed that each haplotype contains 23 telomeres. In conclusion, this study presents a haplotype-resolved and gap-free genome assembly. </span></span></p>
Рис. 8. СреЗы череЗ гонады моллюска: А – поперечный среЗ череЗ гонаду самки, Б–Д – фолликулы в гонадах самок (Б, В – Зрелые ооциты круглой формы, готовые к вымету; Г – ооциты в период активного гаметогенеЗа на стадии раннего трофоплаЗматического роста, Д – ооциты каплевидной формы в период преднерестовой стадии при ЗаверШении трофоплаЗматического роста), Е, Ж – поперечные среЗы череЗ гонаду самца, З, И – ацинусы в гонадах самцов (З – преднерестоваЯ стадиЯ, просветы в ацинусах практически отсутствуют, стенки ацинусов не раЗличимы, И – нерестоваЯ стадиЯ, имеютсЯ просветы в ацинусах). МасШтабные линейки 300 мкм (А), 200 мкм (Е), 100 мкм (Ж), 50 мкм (Б–Д, З, И). вя – вакуолиЗированное Ядро, сф – стенка фолликула, вм – вителлиноваЯ мембрана, РО – раЗвиваюЩиесЯ иЗ пелликулы ооциты, пг – ресничный проток гонады, с – сперматоциты, па – просветы в ацинусах. Fig. 8. Sections through the gonads of the mollusk: А – transverse section through the female gonad, Б–Д – ovarian acini, follicles (Б, В – mature round-shaped oocytes ready to be swept out; Г – oocytes in the period of active gametogenesis at the stage of early trophoplasmatic growth, Д – tear-shaped oocytes during the pre-spawning stage at the end of trophoplasmatic growth), Е, Ж – transverse sections through the male gonads, З, И – testicular acini (З – pre-spawning stage, with practically absent gaps in the acini and invisible the acini walls, И – spawning stage, with gaps in the acini). Scale bars 300 µm (A), 200 µm (E), 100 µm (Ж), 50 µm (Б–Д, З, И). вя – vacuolated nucleus, сф – follicle wall, вм – vitelline membrane, РО – developing oocytes arising from a pellicle, пг – ciliated gonadal duct, с – spermatocytes, па – gaps in acini. in Nodularia vladivostokensis (Bivalvia: Unionidae) from Razdolnaya River (Primorye, Russia)
Рис. 8. СреЗы череЗ гонады моллюска: А – поперечный среЗ череЗ гонаду самки, Б–Д – фолликулы в гонадах самок (Б, В – Зрелые ооциты круглой формы, готовые к вымету; Г – ооциты в период активного гаметогенеЗа на стадии раннего трофоплаЗматического роста, Д – ооциты каплевидной формы в период преднерестовой стадии при ЗаверШении трофоплаЗматического роста), Е, Ж – поперечные среЗы череЗ гонаду самца, З, И – ацинусы в гонадах самцов (З – преднерестоваЯ стадиЯ, просветы в ацинусах практически отсутствуют, стенки ацинусов не раЗличимы, И – нерестоваЯ стадиЯ, имеютсЯ просветы в ацинусах). МасШтабные линейки 300 мкм (А), 200 мкм (Е), 100 мкм (Ж), 50 мкм (Б–Д, З, И). вя – вакуолиЗированное Ядро, сф – стенка фолликула, вм – вителлиноваЯ мембрана, РО – раЗвиваюЩиесЯ иЗ пелликулы ооциты, пг – ресничный проток гонады, с – сперматоциты, па – просветы в ацинусах. Fig. 8. Sections through the gonads of the mollusk: А – transverse section through the female gonad, Б–Д – ovarian acini, follicles (Б, В – mature round-shaped oocytes ready to be swept out; Г – oocytes in the period of active gametogenesis at the stage of early trophoplasmatic growth, Д – tear-shaped oocytes during the pre-spawning stage at the end of trophoplasmatic growth), Е, Ж – transverse sections through the male gonads, З, И – testicular acini (З – pre-spawning stage, with practically absent gaps in the acini and invisible the acini walls, И – spawning stage, with gaps in the acini). Scale bars 300 µm (A), 200 µm (E), 100 µm (Ж), 50 µm (Б–Д, З, И). вя – vacuolated nucleus, сф – follicle wall, вм – vitelline membrane, РО – developing oocytes arising from a pellicle, пг – ciliated gonadal duct, с – spermatocytes, па – gaps in acini.
GNN Models and results for the paper "Band-gap regression with architecture-optimized message-passing neural networks"
<p>Contains files with model parameters for random search and reference models, as well as the converted AFLOW dataset, in graphs form. Corresponds to results in <a href="https://arxiv.org/pdf/2309.06348.pdf">https://arxiv.org/pdf/2309.06348.pdf</a>.</p> <p>Model predictions along with AUID identifiers are located in result_combined.zip, band gap (egap) and formation energy (ef) predictions are from the PaiNN ensemble, band gap classification is done by MPEU model.</p> <p>New results include PaiNN NAS models.</p> <p>Compatible source code can be found at <a href="https://github.com/tisabe/jraph_MPEU/tree/v1.0.0">jraph_MPEU GitHub repository</a>.</p>
Filling the data gap between GRACE and GRACE-FO based on a two-step reconstruction method
<p><span>GRACE, </span><span>GRACE-FO</span><span>, Data gap, long short-term memory Bayesian convolutional neural network (LSTM-BCNN)</span></p>
PrimeKGQA, the dataset from paper: Bridging the Gap: Generating a Comprehensive Biomedical Knowledge Graph Question Answering Dataset
<p>Despite the plethora of resources such as large-scale corpora and manually curated Knowledge Graphs (KGs), the ability to perform reasoning with natural language inputs over biomedical graphs remains challenging due to insufficient training data. We propose a novel method for automatically constructing a Biomedical Knowledge Graph Question Answering (BioKGQA) dataset sourced from PrimeKG, the largest precision medicine-oriented KG. In total,<br>we create 83999 question-answer pairs along with their respective SPARQL queries. Our approach generates a diverse array of contextually relevant questions covering a wide spectrum of biomedical concepts and levels of complexity. We evaluate our method based on automatic metrics alongside manual annotations. We establish novel standards tailored for KGQA systems to highlight the linguistic correctness and semantical faithfulness of the generated questions based on extracted KG facts. The compiled dataset – PrimeKGQA – serves as a valuable benchmarking resource for advancing knowledge-driven biomedical research and evaluating KGQA system.</p>
Supplementary dataset for "Global water gaps under future warming levels"
<p>The folder contains water gap data relative to the paper:<br>Rosa, L., Sangiorgio, M. Global water gaps under future warming levels. Nat Commun 16, 1192 (2025). https://doi.org/10.1038/s41467-025-56517-2</p> <p>All water gaps data are in km3/yr.</p> <p><br>Gridded data(NetCDF at 0.5°)<br> - baseline<br> water_gap_baseline.nc: Baseline water gap in the period 2001-2010<br> - 1.5°C warming (5 models + average)<br> water_gap_15C_average.nc: Water gap under 1.5°C warming (multi-model average)<br> water_gap_15C_h08_ipsl-cm6a-lr.nc: Water gap under 1.5°C warming (h08 + ipsl-cm6a-lr)<br> water_gap_15C_h08_mri-esm2-0.nc: Water gap under 1.5°C warming (h08 + mri-esm2-0)<br> water_gap_15C_h08_ukesm1-0-ll.nc: Water gap under 1.5°C warming (h08 + ukesm1-0-ll)<br> water_gap_15C_h08_mpi-esm1-2-hr.nc: Water gap under 1.5°C warming (h08 + mpi-esm1-2-hr)<br> water_gap_15C_h08_gfdl-esm4.nc: Water gap under 1.5°C warming (h08 + gfdl-esm4)<br> - 3°C warming (5 models + average)<br> water_gap_3C_average.nc: Water gap under 3°C warming (multi-model average)<br> water_gap_3C_h08_ipsl-cm6a-lr.nc: Water gap under 3°C warming (h08 + ipsl-cm6a-lr)<br> water_gap_3C_h08_mri-esm2-0.nc: Water gap under 3°C warming (h08 + mri-esm2-0)<br> water_gap_3C_h08_ukesm1-0-ll.nc: Water gap under 3°C warming (h08 + ukesm1-0-ll)<br> water_gap_3C_h08_mpi-esm1-2-hr.nc: Water gap under 3°C warming (h08 + mpi-esm1-2-hr)<br> water_gap_3C_h08_gfdl-esm4.nc: Water gap under 3°C warming (h08 + gfdl-esm4)</p> <p><br>Aggregated data (.xlsx)<br> - source_data.xlsx: Water gap aggregated by country and basin for all the considered scenarios (including multi-model average and agreement analysis)</p> <p>Note: the global water gap obtained by summing all the countries/basins is not completely equivalent to the sum of all the pixels because some pixels' center is outside the polygon of the corresponding country/basin (differences in the order of 1km/yr3, <0.3%). See sheet "Figure 3" A249:N251 (countries) and "Figure 5" A235:N237 (basins) for further details.</p>
Bridging the Gap between Autonomous and Predetermined Paradigms: The Role of Sampling in Evaluative Learning
<p>While most evaluative learning paradigms remove participants’ autonomy over the information they receive, other research traditions have demonstrated that information sampling has an important role in learning. We investigate the impact of information sampling on a central evaluative learning paradigm: evaluative conditioning. We compare a traditional evaluative conditioning paradigm with a paradigm in which participants have autonomy over the stimulus pairings they receive. Participants in the high-autonomy condition show a strong preference for positively paired CSs. Nevertheless, the strength of evaluative conditioning effects was independent of autonomy. Moreover, high-autonomy participants, but not their low-autonomy counterparts, demonstrate a relationship between sampling frequency and evaluations, in line with the interpretation that sampled stimuli become more positive, whereas ignored stimuli become more negative over the course of the learning phase. The present research provides a cornerstone for integrating several research traditions within and beyond the evaluative learning literature, providing a foundation for new insights and more comprehensive theories of evaluative learning.</p>
Data from: A burning issue: Savanna fire management can generate enough carbon revenue to help restore Africa's rangelands and fill Protected Area funding gaps
<p>Many savanna-dependent species in Africa including large herbivores and apex predators are at increasing risk of extinction. Achieving effective management of protected areas (PAs) in Africa where lions live will cost an estimated USD >$1-2 B/year in new funding. We explored the potential for fire management-based carbon-financing programs to fill this funding gap and benefit degrading savanna ecosystems. We demonstrated how introducing early dry season fire management programs could produce potential carbon revenues (PCR) from either a single carbon-financing method (avoided emissions) or from multiple sequestration methods ranging from USD $59.6-$655.9 M/year (at USD $5/ton) or USD $155.0 M–$1.7 B/year (at USD $13/ton). We highlighted variable but significant PCR for savanna PAs from USD $1.5–$44.4 M/year per PA. We suggest investing in fire management programs to jump-start the United Nations Decade of Ecological Restoration to help restore degraded African savannas and conserve imperiled keystone herbivores and apex predators. <br> <br> Open Access article: <a href="https://doi.org/10.1016/j.oneear.2021.11.013">https://doi.org/10.1016/j.oneear.2021.11.013</a></p>
Replication Package for: "Overcoming the gap between structured dependencies and change coupling"
<p>This datasets contains example projects that resulted from the empirical analysis of the history of object-oriented cyber-physical<br> systems: Glucosio/glucosio-android<sup>[1]</sup> , isl-org/OpenBot<sup>[2]</sup>, eclipse/concierge<sup>[3]</sup>, and WPIRoboticsProjects/GRIP<sup>[4]</sup>. It was created using our prototype tool <em>callgraphCA</em> to discover metrics of software changes, based on<br> call graph analysis and evolution and serve to display change coupling of artifacts and call graph evolution.</p> <p>Contained in the dataset are the databases generated by <em>callgraphCA </em>as well as example Notebooks that show the project results and <em>callgraphCA </em>libraries that support the analysis. For detailed information about <em>callgraphCA </em>visit <a href="https://github.com/GLopezMUZH/callgraphCA">GitHub: GLopezMUZH/callgraphCA</a></p> <p>[1] https://github.com/Glucosio/glucosio-android</p> <p>[2] https://github.com/isl-org/OpenBot/</p> <p>[3] https://github.com/eclipse/concierge</p> <p>[4] https://github.com/WPIRoboticsProjects/GRIP</p>
Large surface-rupture gaps and low surface fault slip of the 2021 Mw 7.4 Maduo earthquake along a low-activity strike-slip fault, Tibetan Plateau
<p>In this data set, Text S1 describes methods of (i) field investigation, UAV image collection and interpretation and (ii) horizontal and vertical displacement measurements. Figure S3 shows pre-event topographic expressions of the Maduo earthquake fault. Tables S1 and S2 provide measurement results of horizontal and vertical displacements, respectively. Datasets 1-4 provide the UAV flight swath, interpreted surface ruptures and secondary cracks, horizontal displacements and vertical displacements.</p>
State of biodiversity documentation in the Philippines: Metadata gaps, taxonomic biases, and spatial biases in the DNA barcode data of animal and plant taxa in the context of species occurrence data
<p>These files can be categorized into three groups: (1) raw datasets obtained from public databases (i.e., GBIF, BOLD, and GenBank), (2) manually edited files needed for parsing and analysis, and (3) supplementary files for spatial analysis. All are used in the examination of gaps and biases present in Philippine biodiversity data, which can direct research on the taxa and spatial regions that need more sampling.</p>
GAP interatomic potential for gold
<p><strong>Gaussian approximation potential</strong> (GAP) for <strong>gold</strong> [1]. It has been fitted with <strong>QUIP/GAP </strong>[1,2] by generating a new database of atomic structures containing:</p> <ol> <li>dimers;</li> <li>fcc, bcc, hcp and simple-cubic supercells, including strained, distorted and high-temperature configurations;</li> <li>surface slabs;</li> <li>clusters.</li> </ol> <p>The calculations were carried out at the <strong>PBE</strong> level of theory [3] using the VASP code [4,5]. This potential uses <strong>2-body</strong> (distance_2b) and <strong>SOAP-type descriptors</strong> (soap_turbo) [6,7], as implemented in the <strong>TurboGAP</strong> code [8]. The files can be used both with QUIP/GAP (compiled with the soap_turbo libraries) and TurboGAP. When using this potential, please read and cite:</p> <blockquote> <p><strong>J. Kloppenburg, A. Pedersen, K. Laasonen, M. A. Caro, and H. Jónsson</strong></p> <p>"Reassignment of magic numbers for icosahedral Au clusters: 310, 564, 928 and 1426"</p> <p><a href="https://doi.org/10.1039/D2NR01763F">Nanoscale 14, 9053 (2022)</a></p> </blockquote> <p><strong>References</strong></p> <ol> <li>A.P. Bartók, M.C. Payne, R. Kondor, and G. Csányi. Phys. Rev. Lett. 104, 136403 (2010).</li> <li>LibAtoms: <a href="https://libatoms.github.io/">https://libatoms.github.io</a></li> <li>J.P. Perdew, K. Burke and M. Ernzerhof. Phys. Rev. Lett. 77, 3865 (1996).</li> <li>VASP: <a href="http://vasp.at/">http://vasp.at</a></li> <li>G. Kresse and J. Furthmüller. Phys. Rev. B 54, 11169 (1996).</li> <li>A.P. Bartók, R. Kondor, and G. Csányi. Phys. Rev. B 87, 184115 (2013).</li> <li>M.A. Caro. Phys. Rev. B 100, 024112 (2019).</li> <li>TurboGAP: <a href="http://turbogap.fi/">http://turbogap.fi</a></li> </ol>
Data for a CML blackout gap detection example
<p><em>blackout_example_cmls.nc</em> consists of three years of data from two commercial microwave links. It includes transmitted and recieved signal levels with a temporal resolution of one minute.</p> <p><em>blackout_example_radar_reference.nc</em> consists of three years of rainfall data from RADKLIM-YW, a radar product from the German Weather Service, path-averaged along the two example CMLs.</p>
Sapling regeneration within canopy gaps in a temperate montane riparian forest.
<p>This is a dataset of sapling regeneration within canopy gaps in a temperate montane riparian forest.</p> <p>The followings are details of each file.</p> <p><strong>GapSeedlings_v1.0.0.csv</strong></p> <ul> <li><code>Plot</code> Integer. The ID of plots, some plots include more than one gap.</li> <li><code>Gap</code> Factor. The ID of gaps.</li> <li><code>Quadrat</code> Integer. The ID of quadrats within a gap.</li> <li><code>stemID</code> Character. The ID of stems.</li> <li><code>Sp.</code> Factor. The species names.</li> <li><code>Family</code> Factor. The family name of the species.</li> <li><code>Substrate</code> Factor. Established substrates. NA means that it was not recorded.</li> <li><code>Heightyyyy</code> Numeric. Vertical heights of trees (cm) in yyyy. The individuals with <code>CensusIn2020</code> = 0, their <code>Height2020</code> is NA because they had not been censused in 2020.</li> <li><code>Lengthyyyy</code> Numeric. Length of trees (cm) in yyyy. The individuals with <code>CensusIn2020</code> = 0, their <code>Length2020</code> is NA because they had not been censused in 2020.</li> <li><code>DBH1_yyyy</code>, <code>DBH2_yyyy</code> Numeric. Diameter at breast height (mm) in yyyy. DBH1 and DBH2 were measured to cross at right angles. The individuals with <code>CensusIn2020</code> = 0, their <code>DBH2020_1</code> and <code>DBH2020_2</code> are NA because they had not been censused in 2020.</li> <li><code>Cmtyyyy</code> Character. Comments in yyyy.</li> <li><code>CensusIn2020</code> Factor. 1 means that the plot was censused in 2020, 0 does not.<br> </li> </ul> <p><strong>Map_Gaps.pdf</strong><br> <code>p. 1</code>: The overall picture of the positional relations between each gap.<br> <code>pp. 2-19</code>: The details of gaps.</p> <p> </p> <p><strong>Metadata_GapSeedlings.txt</strong><br> Metadata of "<strong>GapSeedlings_v0.1.0.csv</strong>".<br> It is the same as this description.</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.