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2,139 results for “recognition”
How Samsung's AI Is Improving Audio and Voice Recognition
<p>https://www.vatican.va/various/cappelle/sistina_vr/index.html?n=q&id=Red_Boost_d45f2&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?xml=https://topcbc.net&id=Cellucare_1f21e<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?id=flowforce_max_8dfe6&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?w=t&id=GlucoTrust_gfdzb&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?id=Alipean_4ehg4&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?xml=https://topcbc.net&id=Hyperbolic_Stretching_b4zbf<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?xml=https://topcbc.net&id=Acv_keto_gummies_32z48<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?s=q&id=ProvaDent_dzhf1&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?r=f&id=Sumatra_Slim_4fg8c&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?i=w&id=Cellucare_1f26f&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?id=IlluDerma_edg16&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?id=Water_Freedom_System_6afch&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?id=Puravive_g8e5a&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?xml=https://topcbc.net&id=ZenCortex_85ef3<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?xml=https://topcbc.net&id=iGenics_z3d8e<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?id=Puravive_88fz6&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?u=z&id=Emperor_9125e&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?c=q&id=CollaGenius_1gcc1&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?t=i&id=BIOFIT_9zc18&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?xml=https://topcbc.net&id=flowforce_max_1z9aa<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?f=f&id=NEUROZOOM_bdb5c&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?o=f&id=Water_Freedom_System_1e7eb&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?id=PowerBite_3bag5&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?id=MenoRescue_d9a9h&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?xml=https://topcbc.net&id=ProDentim_4692a<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?xml=https://topcbc.net&id=Alpha_BRAIN_e48d3<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?v=t&id=Kerassentials_6186e&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?g=o&id=pineal_xt_gh7g8&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?h=r&id=GlucoTrust_5h9f3&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?u=s&id=Pelvic_Floor_Strong_8e791&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?id=Soulmate_Sketch_hc62c&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?xml=https://topcbc.net&id=iGenics_9becc<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?id=SonoFit_995d5&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?xml=https://topcbc.net&id=All_Day_Slimming_Tea_1756h<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?f=y&id=ProDentim_98h68&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?xml=https://topcbc.net&id=Keto_acv_gummies_shark_tank_323a3<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?xml=https://topcbc.net&id=Shark_tank_keto_gummies_b44ha<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?r=i&id=Nitric_boost_79c8b&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?xml=https://topcbc.net&id=Kerassentials_5b445<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?w=a&id=GlucoBerry_zh5z1&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?id=IlluDerma_ddhc8&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?i=y&id=Ikaria_Juice_dz33e&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?n=o&id=The_Money_Wave_4f2e7&xml=https://topcbc.net<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?xml=https://topcbc.net&id=SonoFit_gb6gh<br>https://www.vatican.va/various/cappelle/sistina_vr/index.html?id=Pianoforall_ed171&xml=https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Dependence of the azure-winged magpie on nest spatial cues in offspring recognition decreases with nestling growth
<p>Various cues have been identified as the basis for animals' discrimination between kin and non-kin. In altricial birds, these cues show temporal and spatial variations, implying that kin recognition might be condition-dependent. In this study, we hypothesize that parents might be sensitive to the changes in nest spatial position, and that their dependence on the nest spatial cue in offspring recognition decreases with offspring growth. We tested this hypothesis in a Tibetan population of the azure-winged magpie, <i>Cyanopica cyanus</i>, by designing a nest translocation experiment. We found that adults could return to their nest and continue caring for their offspring even if the nest had been moved to a new position. Their sensitivity to the spatial change in nest position not only varied with distance but also relied on the offspring life-history stage. Breeders were more likely to recognize their nest and offspring during the nestling period than during the incubation period, indicating that the dependence of parents on the spatial cue of the nest is higher when offspring are in the form of eggs than in the form of nestlings. Our findings suggest that azure-winged magpies mainly depend on the spatial position of the nest to recognize their nest and offspring. After nestlings establish communication with their parents, their phenotypic characteristics may replace the nests' spatial cue to play a greater role in the offspring recognition of parents.</p>
Wavelet filters for automated recognition of birdsong in long-time field recordings
<p>1. Ecoacoustics has the potential to provide a large amount of information about the abundance of many animal species at a relatively low cost. Acoustic recording units are widely used in field data collection, but the facilities to reliably process the data recorded -- recognising calls that are relatively infrequent, and often significantly degraded by noise and distance to the microphone -- are not well developed yet. 2. We propose a call detection method for continuous field recordings that can be trained quickly and easily on new species, and degrades gracefully with increased noise or distance from the microphone. The method is based on the reconstruction of the sound from a subset of the wavelet nodes (elements in the wavelet packet decomposition tree). It is intended as a preprocessing filter, therefore we aim to minimise false negatives: false positives can be removed in subsequent processing, but missed calls will not be looked at again. 3. We compare our method to standard call detection methods, and also to machine learning methods (using as input features either wavelet energies or Mel-Frequency Cepstral Coefficients (MFCC)) on real-world noisy field recordings of six bird species. The results show that our method has higher recall (proportion detected) than the alternative methods: 87% with 85% specificity on >53 hrs of test data, resulting in an 80% reduction in the amount of data that needed further verification. It detected >60% of calls that were extremely faint (far away), even with high background noise. 4. This preprocessing method is available in our AviaNZ bioacoustic analysis program and enables the user to significantly reduce the amount of subsequent processing required (whether manual or automatic) to analyse continuous field recordings collected by spatially and temporally large-scale monitoring of animal species. It can be trained to recognise new species without difficulty, and if several species are sought simultaneously, filters can be run in parallel.</p>
Supplementary data for the manuscript: Image2SMILES: Transformer-based Molecular Optical Recognition Engine
<p>This is the supplementary data for the manuscript: <a href="https://chemrxiv.org/engage/chemrxiv/article-details/60c758c6469df4169bf45744">Image2SMILES: Transformer-based Molecular Optical Recognition Engine</a></p> <p>It contains pairs of image-string, generated from 1M SMILES strings. These strings were randomly chosen from PubChem database.<br> It was prepared using the code, published at <a href="https://github.com/syntelly/img2smiles_generator/">https://github.com/syntelly/img2smiles_generator/</a></p> <p>To unpack do:<br> <em>tar xvf subset_1M.tar.xz && tar xvf subset_1M_dump.tar.gz && rm subset_1M_dump.tar.gz</em></p> <p>You'll get the following data:</p> <ul> <li>subset_1M.smi - list of 1M source SMILES</li> <li>subset_1M_dump - directory with images </li> <li>subset_1M_result.csv - list of pairs FGSMILES - pathcode, first 3 chars of pathcode are corresponding subdirs in subset_1M_dump</li> <li>subset_1M_fails.csv - list of failed molecules from subset_1M.smi</li> <li>subset_1M_grpcounter.lst - list of counted groups, used in this generation</li> </ul> <p>You can generate your own data using <a href="https://github.com/syntelly/img2smiles_generator/">https://github.com/syntelly/img2smiles_generator/</a> </p>
NLM-Gene, a richly annotated gold standard dataset for gene entities that addresses ambiguity and multi-species gene recognition
<p>The automatic recognition of gene names and their corresponding database identifiers in biomedical text is an important first step for many downstream text-mining applications. The NLM-Gene corpus is a high-quality manually annotated corpus for genes, covering ambiguous gene names, with an average of 29 gene mentions (10 unique identifiers) per article, and a broader representation of different species (including <i>Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Arabidopsis thaliana, Danio rerio,</i> etc.) when compared to previous gene annotation corpora. NLM-Gene consists of 550 PubMed articles from 156 biomedical journals, doubly annotated by six experienced NLM indexers, randomly paired for each article to control for bias. The annotators worked in three annotation rounds until they reached a complete agreement. Using the new resource, we developed a new gene finding algorithm based on deep learning which improved both on precision and recall from existing tools. The NLM-Gene annotated corpus is freely available at Dryad and at <a href="https://www.ncbi.nlm.nih.gov/research/bionlp/">https://www.ncbi.nlm.nih.gov/research/bionlp/</a>. The gene finding results of applying this tool to the entire PubMed/PMC are freely accessible through our web-based tool PubTator.</p>
Handwritten Text Recognition Ground Truth Set: StABS Ratsbücher O10, Urfehdenbuch X
<p>Ground Truth for "Urfehdenbuch X der Stadt Basel (1563-1569)" at Staatsarchiv Basel-Stadt (StABS).</p> <p>Images and text aligned, using text-to-image (provided within Transkribus, <a href="https://www.readcoop.eu">www.readcoop.eu</a>).</p> <p>ALTO and Page XML are available for the text alignment.</p> <p>TEI to txt on page basis by Peter Dängeli.</p> <p>Derived from Transcription/TEI file: Urfehdenbuch X der Stadt Basel (1563-1569), in: Die Urfehdebücher der Stadt Basel – digitale Edition, hg. v. Susanna Burghartz, Sonia Calvi und Georg Vogeler Basel/Graz 2016. (zuletzt verändert am 31.1.2017): <a href="http://hdl.handle.net/11471/1010.2.1">hdl:11471/1010.2.1</a>.</p> <p>Image source: http://dokumente.stabs.ch/view/2010/Ratsbuecher_O_10/</p> <p>CC-BY-NC-SA: The license is inherited from the project "Urfehdebücher der Stadt Basel – digitale Edition": http://gams.uni-graz.at/o:ufbas.1563.</p>
database/Fornix volumetric increase and microglia morphology contribute to spatial and recognition-like memory decline during ageing
<p>Published along with the journal paper: Fornix volumetric increase and microglia morphology contribute to spatial and recognition-like memory decline during ageing</p>
Concept Recognition as Translation
<p>Large files for the Concept Recognition as Translation project on GitHub with the associated paper titled: "Concept Recognition as a Machine Translation Problem" in BMC Bioinformatics.</p>
Structural determinants of ligands recognition by the human mitochondrial basic amino acids transporter SLC25A29. Insights from molecular dynamics simulations of the c-state.
<p>Initial coordinates, molecular dynamics trajectories and representative snapshots resulting from the study "Structural determinants of ligands recognition by the human mitochondrial basic amino acids transporter SLC25A29. Insights from molecular dynamics simulations of the c-state." by Pasquadibisceglie and Polticelli.</p> <p>The MD folders contain the parameter/topology (parm7) and initial coordinates (rst7) for the molecular dynamics simulations. Moreover, a NetCDF trajectory "prod.nc" of the production phase is also included.<br> In detail:<br> - MD0 -> SLC25A29 in absence of ligands;<br> - MD1, MD3, MD4 -> SLC25A29-ARG complex;<br> - MD1-LYS, MD3-LYS, MD4-LYS -> SLC25A29-LYS complex.</p> <p>The folder PDB_figures contains the PDB files used to produce the figures presented in the manuscript.</p>
Dataset for "A graph-theoretic approach for spatial filtering and its impact on mixed-type spatial pattern recognition in wafer bin maps"
<p>This is the dataset used in the paper, Ezzat, Liu, Hochbaum, and Ding, 2021, “A graph-theoretic approach for spatial filtering and its impact on mixed-type spatial pattern recognition in wafer bin maps,” <em>IEEE Transactions on Semiconductor Manufacturing</em>, Vol. 34, pp. 194-206.</p>
Table Recognition Benchmark on Biomedical Literature on Neurological Disorders
<p>The dataset contains 1650 tables from 1164 PMC OA articles in the context of neurological disorders. The tables are structured in the International Conference on Document Analysis and Recognition (ICDAR) format.</p> <p>The additional csv file contains a labeling into 3 different complexity classes in the format:</p> <p>class document_id table_id</p> <p>with the classes being:</p> <p>0 = simple<br> 1 = complicated<br> 2 = complex</p> <p>You may use the scripts from <a href="https://github.com/TimAdams84/pmc-downloader">this repository</a> to bulk download PDF sources from PMC.</p> <p>A script for evaluating results against the groundtruth data can be found <a href="https://github.com/mnamysl/benchmarking_table_recogn">here</a>.</p>
Static visual predator recognition in jumping spiders
<p>Visually detecting, recognizing, and responding appropriately to predators increases survival. Failure to detect a predator or long decision times carry high and potentially fatal costs. Consequently, many animals show general anti-predatory responses toward threatening stimuli, e.g., looming objects. However, in the context of lurking or stalking ambush predators, visual recognition is based on static visual cues, making this task computationally demanding.</p> <p>Jumping spiders (Salticidae) have superb vision and are excellent ambush predators but they can equally fall prey to other jumping spiders. In a hierarchical decision-making setup, we tested whether the common zebra jumping spider (<em>Salticus scenicus</em>) can visually recognize stationary predators. We measured the spiders’ behavioural responses towards predator (naturally co-occurring, non-co-occurring and artificial) and non-predator objects as well as towards objects with modified features.</p> <p>Our experiments show that salticids demonstrate a robust, fast, and repeatable “freeze and retreat” behaviour when presented with stationary predators, but not similarly sized non-predator objects. Anti-predator responses were triggered by co-occurring and non-co-occurring salticid predators, as well as by 3D-printed salticid models (based on micro-CT scans), suggesting a generalized predator detection/classification. Using modified 3D-printed models, we found evidence that eyes act as an important cue. However, eyes alone did not explain the responses, suggesting that underlying processes rely on multiple rather than single features.</p> <p>To address the role of learning and memory, we tested newly emerged spiderlings and found the same behavioural responses towards predator objects suggesting an innate response. The ability of jumping spiders to innately recognize a non-moving threat is surprising in terms of underlying cognitive processes and the evolution thereof.</p> <p>Escaping from a predator before an attack has been launched likely carries sufficient selective benefits. From a cognitive perspective, the overlap of static visual characteristics between salticid predators, prey, and conspecifics invites further questions considering the mechanisms of such nuanced visual discrimination and categorization in animals with complex vision but relatively small nervous systems.</p>
Datasets of "An Automatically Generated Annotated Corpus for Albanian Named Entity Recognition"
<p>This is an Albanian named entities annotation corpus generated automatically (silver-standard) from Wikipedia and WikiData. It is offered in Apache OpenNLP annotation format.</p> <p>Details of the generation approach may be found in the respective published paper: https://doi.org/10.2478/cait-2018-0009</p> <p>Attached are also the files that were used for generating the Albanian named entities gazetteer and the gazetteer itself in JSON format.</p>
Interpretable Geotechnical Artificial Intelligence (XGeoT-AI) Application to Demystify Image Recognition of Soil Cracks [Datasets]
<p>Here is the test data for the paper "Interpretable Geoscience Artificial Intelligence (XGeoS-AI): Application to Demystify Image Recognition".</p>
Data from: Predicting and measuring decision rules for social recognition in a Neotropical frog
<p>Many animals use signals to recognize familiar individuals but risk mistakes because the signal properties of different individuals often overlap. Further, outcomes of correct and incorrect decisions yield different fitness payoffs, and animals incur these payoffs at different frequencies depending on interaction rates. To understand how signal variation, payoffs, and interaction rates shape recognition decision rules, we studied male golden rocket frogs, which recognize the calls of territory neighbors and are less aggressive to neighbors than to strangers. We first quantified patterns of individual variation in call properties and predicted optimal discrimination thresholds using signal variation. We then measured thresholds for discriminating between neighbors and strangers using a habituation-discrimination field playback experiment. Territorial males discriminated between calls differing by 9% to 12% in temporal properties, slightly higher than the predicted thresholds (5-10%). Finally, we used a signal detection theory model to explore payoff and interaction rate parameters and found that the empirical threshold matched those predicted under ecologically realistic assumptions of infrequent encounters with strangers and relatively costly missed detections of strangers. We demonstrate that receivers group continuous variation in vocalizations into discrete social categories and that signal detection theory can be applied to understand evolved decision rules.</p>
Data from: Highlighting potential physical and chemical cues involved in conspecific recognition system in a predator nematode, Seinura caverna
<p class="MsoNormal"><span>Conspecific recognition is the ability to distinguish and respond to individuals of the same species. In nematodes, this behavior can mediate aggregation, feeding behavior, or mating. Here, we investigated whether and how the predatory nematode <em>Seinura caverna</em> recognizes and avoids conspecifics to prey on. In predation assays, <em>S. caverna</em> did not kill conspecifics, but killed nematodes of three heterospecific species. Interestingly, <em>S. caverna</em> did not kill <em>Ektaphelenchoides</em> <em>spondylis</em> nematodes. <em>S. caverna</em> did not eject its stylet when encountering conspecifics or <em>E. spondylis</em>. The characterization of the internal cuticle structure of 13 nematode species suggested that the cuticle may play a role in the preying decision, as <em>E. spondylis</em> and <em>S. caverna</em> exhibited similar, type III, cuticle layers. Chemical extracts from <em>S.</em> <em>caverna</em> further repelled conspecifics. We discuss the potential hierarchical use of physical and chemical cues in <em>S. caverna</em> predation behavior and provide insights into the evolutionary adaptations and behavior of this organism. </span></p>
Figure 1 in Temperature increase impairs recognition among nestmates in the social wasp Polybia paulista H. von Ihering, 1896 (Vespidae: Polistinae: Epiponini)
Figure 1. Graphic representation of the experimental design. Containers inside the BOD represent wasps subjected to different temperatures used to evaluate the effect of temperature on behavior. Containers outside the BOD (24℃) represent wasps kept under laboratory conditions and used in the paired encounters with experimental wasps. Control containers represent wasps used in the experiments to evaluate isolation′s effect on nestmates′ recognition.
Recognition of conspecific immunological status in a neotropical paper wasp
<p><span>Host immune activation is common under a pathogen invasion. This physiological response can promote changes in body surface compounds, thus providing chemical cues related to health that might be useful to conspecifics. By recognizing the current immunological status of social partners, individuals can modulate their behavior to minimize the risk of infection. Tegument wounding, an immune elicitor, is a required step for many parasites to become established in a host. By using the neotropical eusocial paper wasp <em>Mischocyttarus metathoracicus</em> as a model organism, we first performed a lure presentation experiment in the field to test if wasps discriminate conspecific immunological status (experimentally manipulated by wounding) during on-nest social interactions. Then, we performed gas chromatography-mass spectrometry analyses to test if immunostimulation by wounding alters the wasps' cuticular hydrocarbon profile. We found that wasps reduce the duration of aggressive physical contact when interacting with wounded lures, despite displaying a similar frequency of inspective and aggressive behaviors toward both the wounded and the control lures. Besides, we found a subtle increase in a single cuticular hydrocarbon in the wounded wasps. Thus, wasps recognize conspecific immunological status, likely by chemical cues, and modulate their behavior in order to defend the colony against intruders while minimizing the personal risk of infection.</span></p>
Structural similarities between SAM and ATP recognition motifs and detection of ATP binding in a SAM binding DNA methyltransferase
<p>We have provided all the necessary files, outputs, as well as the ReadMe file for this work to be redone.</p>
Dual domain recognition determines SARS-CoV-2 PLpro selectivity for human ISG15 and K48-linked di-ubiquitin
<p>The Papain-like protease (PLpro) is a domain of a multi-functional, non-structural protein 3 of coronaviruses. PLpro cleaves viral polyproteins and posttranslational conjugates with poly-ubiquitin and protective ISG15, composed of two ubiquitin-like (UBL) domains. Across coronaviruses, PLpro showed divergent selectivity for recognition and cleavage of posttranslational conjugates despite sequence conservation. We show that SARS-CoV-2 PLpro binds human ISG15 and K48-linked di-ubiquitin (K48-Ub<sub>2</sub>) with nanomolar affinity and detect alternate weaker-binding modes. Crystal structures of untethered PLpro complexes with ISG15 and K48-Ub<sub>2</sub> combined with solution NMR and cross-linking mass spectrometry revealed how the two domains of ISG15 or K48-Ub<sub>2</sub> are differently utilized in interactions with PLpro. Analysis of protein interface energetics predicted differential binding stabilities of the two UBL/Ub domains that were validated experimentally. We emphasize how substrate recognition can be tuned to cleave specifically ISG15 or K48-Ub<sub>2</sub> modifications while retaining capacity to cleave mono-Ub conjugates. These results highlight alternative druggable surfaces that would inhibit PLpro function.</p>
ScienceDex guides
Understand access before you commit
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.