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Figure 3. Combination of Neural and Symbolic Information Processing Strategies
<p>The second model developed is a model for human-like machine perception based on<br> research findings in neuroscience and neuro-psychology. The principal idea of the model is to use<br> so-called neuro-symbols as basic processing units. This concept is inspired by the fact that the brain is made up of neurons but we think in term of symbols. In analogy to the brain, starting from sensor<br> values, the sensory information is combined and condensed in a modular hierarchical manner to<br> more and more complex neuro-symbolic information until this results in a complete, unitary,<br> multimodal perception of the environment (see figure 3).</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 9. Modular Hierarchical Organization of Perceptual Neuro-Symbolic Networks
<p>In analogy to how it is reported for the brain by A. Luria, connections of the lowest levels of the architecture of Figure 9 are predefined. Higher-level connections are set via a learning process, concretely via a supervised learning process that was described in detail in . More recent research findings indicate that learning could also already take place at lower levels of<br> perception and that unsupervised learning could be crucial for setting these connections. In, first attempts have been made to develop an unsupervised learning strategy for the model.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 7. Function Principle of Neuro-Symbols
<p>In Figure 7, the basic function principle of neuro-symbols is illustrated. One characteristic of neuro-symbols is that they represent symbolic information. In the case of perception, this symbolic inforamtion are perceptual images like for instance a face or a voice (see Section 4.2.1.2 for more details). Furthermore, neuro-symbols show a number of analogies to biological neurons. They have an activation degree (AD), which indicates if the perceptual image that each neuro-symbol respresents is currently perceived in the environment. Each neuro-symbol has a certain number of inputs and one output. Via the inputs, information about the activation degree of other neurosymbols is collected. Like illustrated in the example of Figure 7, a neuro-symbol representing a face could for instance receive information from neuro-symbols representing a head, eyes, and a mouth.</p>
Рис. 7–9. Coelorinchus idiolepis sp. nov. (7 — гоΛотип, 8 — паратип) и C. anisacanthus, гоΛотип, 81,5 мм HL (9), гоΛова, виΔ сбоку. Обозначения: oo — Δиаметр гΛаза, po — посторбитаΛьная ΔΛина. Масштаб: 7 — 20 мм; 8, 9 — 10 мм Figs. 7–9. Coelorinchus idiolepis sp. nov. (7 — holotype, 8 — paratype) and C. anisacanthus, holotype, 81.5 mm HL (9), head, lateral view. Symbols: oo — diameter of eye, po — postorbital length. Scale bars: 7 — 20 mm; 8, 9 — 10 mm in Coelorinchus From The Hawaiian-Emperor Seamount Chain (The Pacific Ocean) (Teleostei, Gadiformes, Macrouridae)
Рис. 7–9. Coelorinchus idiolepis sp. nov. (7 — гоΛотип, 8 — паратип) и C. anisacanthus, гоΛотип, 81,5 мм HL (9), гоΛова, виΔ сбоку. Обозначения: oo — Δиаметр гΛаза, po — посторбитаΛьная ΔΛина. Масштаб: 7 — 20 мм; 8, 9 — 10 мм Figs. 7–9. Coelorinchus idiolepis sp. nov. (7 — holotype, 8 — paratype) and C. anisacanthus, holotype, 81.5 mm HL (9), head, lateral view. Symbols: oo — diameter of eye, po — postorbital length. Scale bars: 7 — 20 mm; 8, 9 — 10 mm
Рис. 1. Пункты сбора материаΛов в районе Ботчинского заповеΑника. Пункты сбора обозначены красными кружками, наибоΛее крупный из которых соответствует основному месту сбора — корΑону «ТепΛый КΛюч». Номера пунктов сбора соответствуют номерам в тексте при их описании. БΛизко распоΛоженные пункты сборов показаны оΑним симвоΛом Fig. 1. Points of collection of materials in the area of the Botchinsky Nature Reserve. Collection points are marked with red circles, the largest of which corresponds to the main collection point — the cordon "Teply Klyuch". The collection point numbers correspond to the numbers in the text when they are described. Closely located collection points are shown with one symbol in Fauna of the geometrid moths (Lepidoptera, Geometridae) of the eastern Sikhote-Alin in the area of the Botchinsky State Nature Reserve I: History of research and subfamilies Archiearinae, Ennominae, Desmobathrinae, and Geometrinae
Рис. 1. Пункты сбора материаΛов в районе Ботчинского заповеΑника. Пункты сбора обозначены красными кружками, наибоΛее крупный из которых соответствует основному месту сбора — корΑону «ТепΛый КΛюч». Номера пунктов сбора соответствуют номерам в тексте при их описании. БΛизко распоΛоженные пункты сборов показаны оΑним симвоΛом Fig. 1. Points of collection of materials in the area of the Botchinsky Nature Reserve. Collection points are marked with red circles, the largest of which corresponds to the main collection point — the cordon "Teply Klyuch". The collection point numbers correspond to the numbers in the text when they are described. Closely located collection points are shown with one symbol
Рис. 2. Варианты преΑсказанной Αоменной структуры патогенраспознающих моΛекуΛ гемоцитов моΛΛюсков Planorbarius corneus. a — фибриногенпоΑобные беΛки, b — гаΛектины, c — F-Λектины. УсΛовные обозначения и сокращения, зΑесь и ΑаΛее: горизонтаΛьные красные поΛоски — сигнаΛьный пептиΑ, горизонтаΛьные розовые — обΛасть низкой сΛожности, вертикаΛьные синие поΛоски — трансмембранная обΛасть, FBG — фибриногеновый Αомен, FTP — Αомен фукоΛектина, EGF — Αомен эпиΑермаΛьного фактора роста, EGF_CA — каΛьцийсвязывающий EGF-поΑобный Αомен, PAN_AP — APPLE-поΑобный Αомен, SCAN — обΛасть, богатая Λейцином, GLECT — гаΛактозосвязывающий Λектин, CLECT — Λектин C-типа, Gal-bind — гаΛактозиΑ–связывающий Λектин, ML — MD-2- поΑробный Αомен распознавания ΛипиΑов Fig. 2. Variants of the predicted domain structure of pattern recognition molecules from hemocytes of Planorbarius corneus molluscs. a — fibrinogen-related proteins, b — galectins, c — F-lectins. Symbols and abbreviations (here and further): horizontal red stripes — signal peptide, horizontal pink stripes — a low complexity region, vertical blue stripes — transmembrane region, FBG — fibrinogen-related domain, FTP — fucolectin domain, EGF — epidermal growth factor-like domain, EGF_CA — calcium-binding EGF-like domain, PAN_AP — APPLE-like domain, SCAN — leucine rich region, Apple — APPLE domain, GLECT — galactose-binding lectin, CLECT — C-type lectin, Gal-bind — galactoside-binding lectin, ML — MD-2-related lipid-recognition domain in Pathogen recognition molecules from hemocytes of Planorbarius corneus molluscs (Planorbidae, Pulmonata)
Рис. 2. Варианты преΑсказанной Αоменной структуры патогенраспознающих моΛекуΛ гемоцитов моΛΛюсков Planorbarius corneus. a — фибриногенпоΑобные беΛки, b — гаΛектины, c — F-Λектины. УсΛовные обозначения и сокращения, зΑесь и ΑаΛее: горизонтаΛьные красные поΛоски — сигнаΛьный пептиΑ, горизонтаΛьные розовые — обΛасть низкой сΛожности, вертикаΛьные синие поΛоски — трансмембранная обΛасть, FBG — фибриногеновый Αомен, FTP — Αомен фукоΛектина, EGF — Αомен эпиΑермаΛьного фактора роста, EGF_CA — каΛьцийсвязывающий EGF-поΑобный Αомен, PAN_AP — APPLE-поΑобный Αомен, SCAN — обΛасть, богатая Λейцином, GLECT — гаΛактозосвязывающий Λектин, CLECT — Λектин C-типа, Gal-bind — гаΛактозиΑ–связывающий Λектин, ML — MD-2- поΑробный Αомен распознавания ΛипиΑов Fig. 2. Variants of the predicted domain structure of pattern recognition molecules from hemocytes of Planorbarius corneus molluscs. a — fibrinogen-related proteins, b — galectins, c — F-lectins. Symbols and abbreviations (here and further): horizontal red stripes — signal peptide, horizontal pink stripes — a low complexity region, vertical blue stripes — transmembrane region, FBG — fibrinogen-related domain, FTP — fucolectin domain, EGF — epidermal growth factor-like domain, EGF_CA — calcium-binding EGF-like domain, PAN_AP — APPLE-like domain, SCAN — leucine rich region, Apple — APPLE domain, GLECT — galactose-binding lectin, CLECT — C-type lectin, Gal-bind — galactoside-binding lectin, ML — MD-2-related lipid-recognition domain
Рис. 7. Варианты преΑсказанной Αоменной структуры моΛекуΛ аΑгезии гемоцитов моΛΛюсков Planorbarius corneus. УсΛовные обозначения и сокращения: 1–3 — β-интегрины, 4–5 — α-интегрины, 6–7 — сеΛектины, 8–11 — моΛекуΛы семейства САМ (сell adhesiom molecues), INB — субъеΑиницы β-интегрина, IntegrinBcyt — цитопΛазматический Αомен β-интегрина, CY — цистатинопоΑобный Αомен, Int alpha — Αомен α-интегрина, FN3 — Αомен фибронектина типа 3, CCP — Αомен контроΛя компΛемента Fig. 7. Variants of the predicted domain structure of adhesion molecules from hemocytes of Planorbarius corneus molluscs. Symbols and abbreviations: 1–3 — β-integrins, 4–5 — α–integrins, 6–7 — selectins, 8–11 — molecules of the СAM family (cell adhesion molecules), INB — β-integrin subunits, IntegrinBcyt — cytoplasmic domain of β-integrin, CY — cystatin-like domain, Int alpha — α-integrin domain, FN3 — fibronectin type 3 domain, CCP — complement control protein domain in Pathogen recognition molecules from hemocytes of Planorbarius corneus molluscs (Planorbidae, Pulmonata)
Рис. 7. Варианты преΑсказанной Αоменной структуры моΛекуΛ аΑгезии гемоцитов моΛΛюсков Planorbarius corneus. УсΛовные обозначения и сокращения: 1–3 — β-интегрины, 4–5 — α-интегрины, 6–7 — сеΛектины, 8–11 — моΛекуΛы семейства САМ (сell adhesiom molecues), INB — субъеΑиницы β-интегрина, IntegrinBcyt — цитопΛазматический Αомен β-интегрина, CY — цистатинопоΑобный Αомен, Int alpha — Αомен α-интегрина, FN3 — Αомен фибронектина типа 3, CCP — Αомен контроΛя компΛемента Fig. 7. Variants of the predicted domain structure of adhesion molecules from hemocytes of Planorbarius corneus molluscs. Symbols and abbreviations: 1–3 — β-integrins, 4–5 — α–integrins, 6–7 — selectins, 8–11 — molecules of the СAM family (cell adhesion molecules), INB — β-integrin subunits, IntegrinBcyt — cytoplasmic domain of β-integrin, CY — cystatin-like domain, Int alpha — α-integrin domain, FN3 — fibronectin type 3 domain, CCP — complement control protein domain
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 13. Implementation of Neuro-Symbols and their Communication in AnyLogic
<p>Figure 13 and Figure 14 show screenshots of the model implementation in AnyLogic. Figure<br> 13a shows how individual neuro-symbols were implemented. Neuro-symbols are realized by socalled<br> active objects with an input port and an output port via which information is exchanged with<br> other elements. Additionally, variables are used for calculating the activation of the neuro-symbols<br> (not depicted) and for storing properties of neuro-symbols (e.g., the location property). Timers and<br> state charts serve for processing information that arrives in a certain time window or in a certain<br> temporal succession at the input port. Whenever new input information arrives at the input port, the<br> activation degree of the neuro-symbol is recalculated and checked against the threshold value.<br> Based on this, the neuro-symbol is either activated or deactivated and the corresponding<br> information is sent via the output port by using “message objects” (see Figure 13b).</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 15. Input Sources of an Affective Neuro-Symbol Representing an Emotion
<p>Based on the descriptions given above and the concept of neuro-symbolic information<br> processing outlined in Section 4.2, so-called “affective neuro-symbols” were defined for the<br> affective situation assessment architecture (see Figure 15). These affective neuro-symbols can<br> principally receive information from four different sources: (1) body states, (2) objects and events<br> perceived in the environment (external perception), (3) from other emotions and (4) cognitive<br> (reasoning) processes. An input from one of these sources can in certain circumstances already be<br> sufficient to activate an affective neuro-symbol. Different sources can either have an exhibitory or<br> inhibitory effect on the activation of an affective neuro-symbol.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 12. Affinities and Differences of Neuro-Symbolic Networks in Comparison to Classical Neural Networks
<p>After having briefly illustrated the basic function principle of neuro-symbolic networks, this<br> section aims at reviewing their affinities and differences to standard neural networks like for<br> example multi-layer perceptrons (MLPs) [58]. A summary of these affinities and differences is<br> given in Figure 12. The affinities concern certain functions of individual nodes of the networks. In<br> both cases, weighted input information is summed up and an activation function is applied to this<br> sum. In both cases, the individual nodes are interconnected to form networks. Much larger than the<br> number of affinities between neuro-symbolic networks and neural network is however the number<br> of differences. The first difference consists in the application domain. Neuro-symbolic networks<br> have so far mainly been applied for complex, large-scale sensor data processing of multimodal data<br> – an application which can so far barely be handled by neural networks.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 11. Activated Neuro-Symbols for Detecting that a Person walks around in the Room
<p>With the example of Figure 11, also the function of feedback connections can be explained.<br> According to the existing feedforward connections, the neuro-symbol “object stands” would be<br> activated together with the neuro-symbol “object moves” whenever the neuro-symbols “motion”<br> and “object moves” are active, because it is activated by a subset of the neuro-symbols that activate<br> the neuro-symbol “object moves”. This activation would however be undesired in this concrete<br> case. For this reason, an inhibitory feedback connection exists from the neuro-symbol “object<br> moves” to the neuro-symbol “object stands” that inhibits the activation of the neuro-symbol “object<br> stands”.</p>
Figure 5. AGLO Scenario Symbols-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>analyzed AGLO that is displayed to the learner for localization and selection purposes.<br> The second XML element is the scenario element containing a text description of the AGLO<br> and a set of symbols. The description is expressed in natural language and we can notice that it<br> contains four main steps:<br> i) random tree generation;<br> ii) index computation for presentation;<br> iii) parent index computation for answer validation;<br> iv) access to the first two keys for particular feedback generation.<br> In the scenario section depicted in figure 5 several symbols are defined with the following<br> semantics.</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 3. Symbols definition for a graph-based test
<p>In this scenario, we intend to generate a random graph and compute a deep first-search node list. The first defined random symbol is n, namely the number of nodes in the graph as an integer from 5 to 9. The next symbol is named g and denotes the graph object created randomly using 3 parameters: the number of nodes, the minimum, and the maximum value for the weight. For the number of nodes, we used the previously computed value of n, whereas for the weights, we used two constants 0 and 1 since the graph is not weighted</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-In Figure 15 we were able to generate the symbol H without any help from a small image we used only for row and column data
<p>In Figure 15 we were able to generate the symbol H without any help from a small image we used only for row and column data.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 14. Symbol 8 regeneration
<p>As we can see, here we were able to recover the lost middle portion of character ‘A’ using our genetic algorithm. If we can apply some noise filtering technique, the result would be far better. In figure 14, 15 there are another two examples.</p>
Text-fig. 2. Stratigraphy of western part of the Romagna Apennines (after Roveri et al. 2006). Symbol "arrow" – stratigraphical position of the studied floras of Tossignano and Monte Tondo. in Palaeoenvironmental Analysis Of The Messinian Macrofossil Floras Of Tossignano And Monte Tondo (Vena Del Gesso Basin, Romagna Apennines, Northern Italy)
Text-fig. 2. Stratigraphy of western part of the Romagna Apennines (after Roveri et al. 2006). Symbol "arrow" – stratigraphical position of the studied floras of Tossignano and Monte Tondo.
Text-fig. 1. A. Location of the sites of Capo di Fiume, Palena and Pollenzo near Alba. B. Capo di Fiume stratigraphic section. Facies of coastal-transitional marine associations – a. Freshwater marsh and tidal creeks interval, b. Swamp interval, c1–c4. Facies of eustarine bay associations, d1–d6. Facies of open shelf marine associations. Symbols: "black star" – fossiliferous horizon with plant material studied here, 1. mottled grey to dark-brown marls and clayey marls, 2. fissile dark-grey marls and shaly marls, 3. limestones, 4. marly limestones and limey marls, 5. bio-lithoclastic calcarenites, 6. lime conglomerate, 7. massive muddy deposit produced by mass-flow mechanism, 8. diatomitic marls, 9. "terra rossa" soil (modified after Carnevale et al. 2011). in Feather Palm Foliage From The Messinian Of Italy (Capo Di Fiume, Palena And Pollenzo Near Alba) Within The Framework Of Northern Mediterranean Late Miocene Flora
Text-fig. 1. A. Location of the sites of Capo di Fiume, Palena and Pollenzo near Alba. B. Capo di Fiume stratigraphic section. Facies of coastal-transitional marine associations – a. Freshwater marsh and tidal creeks interval, b. Swamp interval, c1–c4. Facies of eustarine bay associations, d1–d6. Facies of open shelf marine associations. Symbols: "black star" – fossiliferous horizon with plant material studied here, 1. mottled grey to dark-brown marls and clayey marls, 2. fissile dark-grey marls and shaly marls, 3. limestones, 4. marly limestones and limey marls, 5. bio-lithoclastic calcarenites, 6. lime conglomerate, 7. massive muddy deposit produced by mass-flow mechanism, 8. diatomitic marls, 9. "terra rossa" soil (modified after Carnevale et al. 2011).
Text-fig. 3. Schematic section through the Żyttawa (Zittau) Basin, on the Czech and Polish boundary; Hrádek n. Nisou and Turów parts of the basin. Explanation of the symbols. 1 – Overlying strata with the upper coal seam, 2 – middle and lower strata with the coal seam (Miocene), 3 – first sedimentary setting with basal coal seam (Miocene / Oligocene), 4 – alcalic volcanism (Tertiary), 5 – Upper Cretaceous deposits, 6 - underlying rocks of the basin. (Adapted after Václ 1967, Václ and Čadek, 1962, modified). in Some Monocot Pollen Taxa From The Lower Miocene Basal Coaly Deposits Of The Czech And Polish Parts Of The Żytawa (Zittau) Basin
Text-fig. 3. Schematic section through the Żyttawa (Zittau) Basin, on the Czech and Polish boundary; Hrádek n. Nisou and Turów parts of the basin. Explanation of the symbols. 1 – Overlying strata with the upper coal seam, 2 – middle and lower strata with the coal seam (Miocene), 3 – first sedimentary setting with basal coal seam (Miocene / Oligocene), 4 – alcalic volcanism (Tertiary), 5 – Upper Cretaceous deposits, 6 - underlying rocks of the basin. (Adapted after Václ 1967, Václ and Čadek, 1962, modified).
Text-fig. 4. Location Map of the examined water vole localities. From Masini et al. (2007), modified. 1: Madrid, surroundings, 2: Graz, 3: Eisfeld, 4: Langen, 5: Delta Po, 6: Rovigo, 7: Ferrara, 8: Calabria, 9: Caverna degli Orsi, 10: Arma delle Manie, 11: Riparo Mochi, 12: Grotta di Castelcivita, 13: Grotta della Serratura, 14: Grotta del Romito, 15: Scario Grotta Grande, 16: Grotta di Cucigliana, 17: Upper Valdarno Campitello, 18: Riparo di Visogliano, 19: Isernia La Pineta, 20: Baume Gigny, 21: Baume Moula Guercy, 22: Grotte de L'Eglise, 23: Grotte-Abri Suard, 24: Grotte d'Artenac, 25: Pié Lombard, 26: Abri Vaufrey, 27: Grotte du Lazaret, 28: Abri Gaudry, 29: Pisede, 30: Euerwanger Bühl, 31: Kemathenhöhle, 32: Krockstein (Rübeland 1), 33: Burgtonna, 34: Parkhöhle (Weimar), 35: Stuttgart- Untertürkheim, 36: Taubach, 37: Ehringsdorf, 38: Plaidter-Hummerich, 39: Mosbach, 40: Petersbuch 1, 41: Bilzingsleben, 42: Miesenheim 1, 43: Voigtstedt, 44: Untermassfeld. See Table 1 for symbol explanations. in Independent Water Vole (Mimomys Savini, Arvicola: Rodentia, Mammalia) Lineages In Italy And Central Europe
Text-fig. 4. Location Map of the examined water vole localities. From Masini et al. (2007), modified. 1: Madrid, surroundings, 2: Graz, 3: Eisfeld, 4: Langen, 5: Delta Po, 6: Rovigo, 7: Ferrara, 8: Calabria, 9: Caverna degli Orsi, 10: Arma delle Manie, 11: Riparo Mochi, 12: Grotta di Castelcivita, 13: Grotta della Serratura, 14: Grotta del Romito, 15: Scario Grotta Grande, 16: Grotta di Cucigliana, 17: Upper Valdarno Campitello, 18: Riparo di Visogliano, 19: Isernia La Pineta, 20: Baume Gigny, 21: Baume Moula Guercy, 22: Grotte de L'Eglise, 23: Grotte-Abri Suard, 24: Grotte d'Artenac, 25: Pié Lombard, 26: Abri Vaufrey, 27: Grotte du Lazaret, 28: Abri Gaudry, 29: Pisede, 30: Euerwanger Bühl, 31: Kemathenhöhle, 32: Krockstein (Rübeland 1), 33: Burgtonna, 34: Parkhöhle (Weimar), 35: Stuttgart- Untertürkheim, 36: Taubach, 37: Ehringsdorf, 38: Plaidter-Hummerich, 39: Mosbach, 40: Petersbuch 1, 41: Bilzingsleben, 42: Miesenheim 1, 43: Voigtstedt, 44: Untermassfeld. See Table 1 for symbol explanations.
Text-fig. 1. Palaeogeographical scheme (distribution of land and sea basins) in part of Eurasia at the beginning of the Late Cretaceous (modified from Spicer et al. 2008). The green leaf symbol indicates the site of the Arman Flora. Asterisks indicate the Okhotsk-Chukotka volcanogenic belt. Dashdotted line indicates the boundary between the Siberian- Canadian and Euro-Sinian palaeofloristic regions (modified from Vakhrameev 1991). in On The Likely Palaeoelevation Of The Turonian - Coniacian Arman Flora Site (North-Eastern Asia)
Text-fig. 1. Palaeogeographical scheme (distribution of land and sea basins) in part of Eurasia at the beginning of the Late Cretaceous (modified from Spicer et al. 2008). The green leaf symbol indicates the site of the Arman Flora. Asterisks indicate the Okhotsk-Chukotka volcanogenic belt. Dashdotted line indicates the boundary between the Siberian- Canadian and Euro-Sinian palaeofloristic regions (modified from Vakhrameev 1991).
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.