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448 results for “analogy”
Thematic/Taxonomic Analogy Task Data Set
<p>A classic analogy paradigm (A:B::C:?) was developed to test children's understanding of thematic and taxonomic categorization of offd in children aged between 3 and 6 years old. Participants' level of food rejection disposition was also measured using the Child Food Rejection Scale (CFRS; Rioux, Lafraire, Picard, 2017) to determine how food rejection affects children's categorization ability in the food domain. </p> <p>Data sets for: children's food rejection scores, children's responses for thematic and taxonomic conditions of food categorization analogy task, and children's responses for stimuli identification task</p> <p>Supplemental material: Stimuli overview</p>
Balloon Analog Risk-taking Task
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Analogical reasoning sequential design fMRI
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VSA, Analogy, and Dynamic Similarity
<p>This file is the video of a presentation originally scheduled to be given at the <a href="https://sites.google.com/view/vsaworkshop2020/home">Workshop on Developments in Hyperdimensional Computing and Vector Symbolic Architectures</a>, 16 March 2020, Kirchoff-Institute for Physics at Heidelberg University, Heidelberg, Germany. The workshop meeting was cancelled due to the COVID-19 pandemic and this presentation was given as a webinar on 2020-05-18.</p> <p><strong>Extended Abstract</strong></p> <p>It has been argued that analogy is at the core of cognition [7, 1]. My work in VSA is driven by the goal of building a practical, effective analogical memory/reasoning system. Analogy is commonly construed as structure mapping between a source and target [5], which in turn can be construed as representing the source and target as graphs and finding maximal graph isomorphisms between them. This can also be viewed as a kind of dynamic similarity in that the<br> initially dissimilar source and target are effectively very similar after mapping.</p> <p>Similarity (the angle between vectors) is central to the mechanics of VSA/HDC. Introductory papers (e.g. [8]) necessarily devote space to vector similarityand the effect of the primitive operators (sum, product, permutation) on similarity. Most VSA examples rely on static similarity, where the vector representations are fixed over the time scale of the core computation (which is usually a single-pass, feed-forward computation). This emphasises encoding methods (e.g.<br> [12, 13]) that create vector representations with the similarity structure required by the core computation. Random Indexing [13] is an instance of the vector embedding approach to representation [11] that is widely used in NLP and ML. The important point is that the vector embeddings are developed in advance and then used as static representations (with fixed similarity structure) in the<br> subsequent computation of interest.</p> <p>Human similarity judgments are known to be context-dependent (see [3] for a brief review). It has also been argued that similarity and analogy are based on the same processes [6] and that cognition is so thoroughly context-dependent that representations are created on-the-fly in response to task demands [2]. This seems extreme, but doesn’t necessarily imply that the base representations are context-dependent as long as the cognitive process that compares them is<br> context-dependent, which can be achieved by having dynamic representations that are derived from the static base representations by context-dependent transforms (or any functionally equivalent process).</p> <p>An obvious candidate for a dynamic transformation function in VSA is substitution by binding, because the substitution can be specified as a vector and dynamically generated (see Representing substitution with a computed mapping in [8]). This implies an internal degree of freedom (a register to hold the substitution vector while it evolves) and a recurrent VSA circuit to provide the dynamics to evolve the substitution vector.</p> <p>These essential aspects are present in [4], which finds the maximal subgraph isomorphism between two graphs represented as vectors. This is implemented as a recurrent VSA circuit with a register containing a substitution vector that evolves and settles over the course of the computation. The final state of the substitution vector represents the set of substitutions that transforms the static base representation of each graph into the best subgraph isomorphism to the static base representation of the other graph. This is a useful step along the path to an analogical memory system.</p> <p>Interestingly, the subgraph isomorphism circuit can be interpreted as related to the recently developed Resonator Circuits for factorisation of VSA representations [9], which have internal degrees of freedom for each of the factors to be calculated and a recurrent VSA dynamics that settles on the factorisation. The graph isomorphism circuit can be interpreted as finding a factor (the substitution vector) such that the product of that factor with each of the graphs is the<br> best possible approximation to the other graph. This links the whole enterprise back to statistical modelling, where there is a long history of approximating matrices/tensors as the product of simpler factors [10].</p> <p>References<br> 1. Blokpoel, M., Wareham, T., Haselager, P., van Rooij, I.: Deep Analogical Inference as the Origin of Hypotheses. The Journal of Problem Solving 11(1), 1–24 (2018)<br> 2. Chalmers, D.J., French, R.M., Hofstadter, D.R.: High-level perception, representation, and analogy: A critique of artificial intelligence methodology. Journal of Experimental & Theoretical Artificial Intelligence 4(3), 185–211 (1992)<br> 3. Cheng, Y.: Context-dependent similarity. In: Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence (UAI’90), pp. 27–30. Cambridge, MA, USA (1990)<br> 4. Gayler, R.W., Levy, S.D.: A distributed basis for analogical mapping. In: Proceedings of the Second International Conference on Analogy (ANALOGY-2009), pp. 165–174. New Bulgarian University, Sofia, Bulgaria (2009)<br> 5. Gentner, D.: Structure-mapping: A theoretical framework for analogy. Cognitive Science 7(2), 155–170 (1983)<br> 6. Gentner, D., Markman, A.B.: Structure mapping in analogy and similarity. American Psychologist 52(1), 45–56 (1997)<br> 7. Gust, H., Krumnack, U., Kühnberger, K.-U., Schwering, A.: Analogical Reasoning: A core of cognition. KI - Künstliche Intelligenz 1(8), 8–12 (2008)<br> 8. Kanerva, P.: Hyperdimensional computing: An introduction to computing in distributed representation with high-dimensional random vectors. Cognitive Computation 1, 139–159 (2009)<br> 9. Kent, S.J., Frady, E.P., Sommer, F.T., Olshausen, B.A.: Resonator Circuits for factoring high-dimensional vectors. http://arxiv.org/abs/1906.11684 (2019)<br> 10. Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Review 51(3), 455–500 (2009)<br> 11. Pennington, J., Socher, R., Manning, C.D.: GloVe: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543. Association for Computational Linguistics, Doha, Qatar (2014)<br> 12. Purdy, S.: Encoding data for HTM systems. http://arxiv.org/abs/1602.05925 (2016)<br> 13. Sahlgren, M.: An introduction to random indexing. In: Proceedings of the Methods and Applications of Semantic Indexing Workshop at the 7th International Conference on Terminology and Knowledge Engineering (TKE 2005), Copenhagen, Denmark (2005)</p>
Dataset for Overscan Detection in Digitized Analog Films by Precise Sprocket Hole Segmentation
<p>This repo includes the self-generated dataset as well as the pre-trained models .</p> <p>ISVC 2020 - 15th International Symposium on Visual Computing</p> <p>Paper: Overscan Detection in Digitized Analog Filmsby Precise Sprocket Hole Segmentation</p> <p> </p> <p>Acknowledgement:</p> <p>Visual History of the Holocaust: Rethinking Curation in the Digital Age. This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Grant Agreement 822670.</p> <p>https://www.vhh-project.eu</p> <p> </p>
Dataset for: Pre-pandemic artificial MERS analog of polyfunctional SARS-CoV-2 S1/S2 furin cleavage site domain is unique among spike proteins of genus Betacoronavirus
<table> <tbody> <tr> <th> </th> <td> <div> <h3><strong>Data File Descriptions and Methods</strong></h3> <ol> <li><strong>Data file 1 [betacov_matching_IPR042578.fasta]</strong>: Representative set of 2,465 betacoronavirus S protein overlapping homologous superfamily sequences retrieved in fasta format on 4 December 2022 from the InterPro repository at https://www.ebi.ac.uk/interpro/entry/InterPro/IPR042578/.<br><br></li> <li><strong>Data File 2 [betacov_matching_IPR042578_motif.fasta]</strong>: With Data File 1 as input, extracted 98,122 furin cleavage site (FCS) output motifs of 20 amino acids length, including overlapping and redundant sequences, produced with the FindFur algorithm with preset parameters as described by (Gu, 2020). FindFur as used was deposited on 15 December 2020 at the GitHub software repository at https://github.com/chwisteeng/FindFur.<br><br></li> <li><strong>Data File 3 [table_s1s2_hits_betacov_polyf.pdf]</strong>: Compiled summary table of sequence hits (PDF) of spike S1/S2 domains across genus <em>Betacoronavirus. </em>The compiled table of hits removed from Data File 2 sequences corresponding to spike protein fragments (incomplete length spike proteins as deposited at GenBank) and duplicates (redundant parts identically overlapping within the 20 amino acids motif windows), and then selected one sequence representative for multiple but identical sequences.<em> </em>Collection dates and geographical locations were retrieved from the NCBI Genbank protein database at https://www.ncbi.nlm.nih.gov/protein/. For SARS-CoV-2 spike variants, these data were also cross-validated with the SARS-CoV-2 lineage mutation tracker (Gangavarapu, 2023) available at https://outbreak.info which was based on extensive sequencing data from the global GISAID initiative (https://gisaid.org/). Solid lines (-) depict pat7 NLS, asterisks (*) O-glycosites, and circumflex (^) symbols FCS.<br><br></li> <li> <p><strong>Data File 4 [table_s1s2_hits_betacov_polyf.xlsx]</strong>: Compiled summary table of sequence hits (MS Excel) of spike S1/S2 domains across genus <em>Betacoronavirus. </em>The compiled table of hits removed from Data File 2 sequences corresponding to spike protein fragments (incomplete length spike proteins as deposited at GenBank) and duplicates (redundant parts identically overlapping within the 20 amino acids motif windows), and then selected one sequence representative for multiple but identical sequences.<em> </em>Collection dates and geographical locations were retrieved from the NCBI Genbank protein database at https://www.ncbi.nlm.nih.gov/protein/. For SARS-CoV-2 spike variants, these data were also cross-validated with the SARS-CoV-2 lineage mutation tracker (Gangavarapu, 2023) available at https://outbreak.info which was based on extensive sequencing data from the global GISAID initiative (https://gisaid.org/). Solid lines (-) depict pat7 NLS, asterisks (*) O-glycosites, and circumflex (^) symbols FCS.<br><br></p> </li> <li> <p><strong>Data File 5 [betacov_s1s2_nls_pat7_furin_psort.txt]: </strong>Nuclear localization signal (NLS) detection output for 5 representative betacoronavirus spike sequence domains, including the positive hits for pat7 in SARS-CoV-2 and for MERS-MA30 CoV. NLS predictions used the PSORT algorithm available as a webservice at https://wolfpsort.hgc.jp/ which is based on the work of Nakai and Horton (Nakai and Horton, 1999). Numbering refers to Data File 3 and Data File 4.<br><br></p> </li> <li> <p><strong>Data File 6 [betacov_s1s2_oglyc_netogly.txt]: </strong>Detection output for 5 representative betacoronavirus spike sequence domains tested for Thr/Ser O-glycosite residue pairs with the standard prediction software NetOGlyc4.0 (Steentoft et al., 2013) as available at https://services.healthtech.dtu.dk/services/NetOGlyc-4.0/. Positive hits have scores above 0.5. Numbering refers to Data File 3 and Data File 4.<br><br></p> </li> <li> <p><strong>Data File 7 [betacov_s1s2_nls_pat7_furin_blastp.txt]</strong>: Comprehensive sequence database searches using were performed using the NCBI protein BLAST (blastp) algorithm with webservice available at https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins. The following blastp search parameters and settings were used: Word size=2; Expect value=200000; Hitlist size=500; Gapcosts=9,1; Matrix=PAM30; Filter string=F; Genetic Code=1;Window Size=40; Threshold=11; Composition-based stats=0; Database Posted date=Jan 19, 2023 2:59 AM; Number of letters=17,117,563; Number of sequences=10,766; Entrez query: Includes: Betacoronavirus (taxid:694002); Excludes: SARS-CoV-2 (taxid:2697049). The six polyfunctional input query consensus motif sequences were TXXPR(K/H/R)XRSX and TXXPRX(K/H/R)RSX.</p> </li> </ol> <h3><strong>References</strong></h3> <p>Gu, C., 2020. FindFur: A Tool for Predicting Furin Cleavage Sites of Viral Envelope Substrates. Master’s Thesis, San Jose State University, CA, USA. doi: <a href="https://doi.org/10.31979/etd.4ahv-9jya">10.31979/etd.4ahv-9jya</a> </p> <p>Gangavarapu K, Latif AA, Mullen JL, Alkuzweny M, Hufbauer E, Tsueng G, Haag E, Zeller M, Aceves CM, Zaiets K, Cano M, Zhou X, Qian Z, Sattler R, Matteson NL, Levy JI, Lee RTC, Freitas L, Maurer-Stroh S; GISAID Core and Curation Team; Suchard MA, Wu C, Su AI, Andersen KG, Hughes LD. Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations. Nat Methods. 2023. 20(4):512-522. doi: <a href="https://doi.org/10.1038/s41592-023-01769-3">10.1038/s41592-023-01769-3</a>.</p> <p>Nakai, K., Horton, P., 1999. PSORT: a program for detecting sorting signals in proteins and predicting their subcellular localization. Trends Biochem Sci 24, 34–36. doi: <a href="https://doi.org/10.1016/s0968-0004(98)01336-x">10.1016/s0968-0004(98)01336-x</a></p> <p>Steentoft, C., Vakhrushev, S.Y., Joshi, H.J., Kong, Y., Vester-Christensen, M.B., Schjoldager, K.T.-B.G., Lavrsen, K., Dabelsteen, S., Pedersen, N.B., Marcos-Silva, L., Gupta, R., Bennett, E.P., Mandel, U., Brunak, S., Wandall, H.H., Levery, S.B., Clausen, H., 2013. Precision mapping of the human O-GalNAc glycoproteome through SimpleCell technology. EMBO J 32, 1478–1488. doi: <a href="https://doi.org/10.1038/emboj.2013.79">10.1038/emboj.2013.79</a></p> </div> </td> </tr> </tbody> </table>
Buoyancy versus local stress field control on the velocity of magma propagation: insight from analog and numerical modelling, Supporting Data
<p>Experimental data and numerical codes used in the manuscript "Buoyancy versus local stress field control on the velocity of magma propagation: insight from analog and numerical modelling" by V. Pinel, S. Furst, F. Maccaferri and D. Smittarello.</p>
Transmission of optical analog signals with 16QAM modulation scheme using a 5GHz RF carrier signal
<p>The specific data sets correspond to the transmission experiments carried out in laboratory settings to assess the performance of an analog optical link. The optical link is based on a commercial InP Mach-Zehnder modulator (MZM) with approximately 25GHz 3-dB bandwidth, which modulates the CW signal of a DFB laser diode at 1560 nm. The electrical signals driving the modulator were generated using a arbitrary waveform generator (AWG) with 20GHz analog bandwidth and 65GSa/s sampling rate (Keysight M8195A). Electrical 16QAM signals at 1GBaud having a 5GHz RF carrier and utilizing Raised Cosine pulse shaping filters were used to feed the MZM. The detection of the back-to-back signals was realized by means of a single 40GHz photodiode. The signals were acquired, sampled and stored using Agilent Infinium DSO-X93304Q 33GHz, 80GSa/s real time oscilloscope.</p> <p>The data sets have the name format of "ModulatorType_ModulationFormat_RFcarierFrequency_SignalBandwidth_FIlterType_Roll-offFactor_OpticalReceivedPower_#of run.bin" . As an example "MZM_16QAM_5GHz_1Gbaud_RC_035_-3dbm_run0.bin".</p> <p>For each experimental set two instances were captured "run0, run1" in a slightly different time.</p>
Data set of "Capacitive and Inductive Characteristics of Volatile Perovskite Resistive Switching Devices with Analog Memory"
<p>The dataset of all data presented in the article published in the virtual special issue of the Journal of Physical Chemistry Letters:</p> <p>"Capacitive and Inductive Characteristics of Volatile Perovskite Resistive Switching Devices with Analog Memory"</p> <p>DOI: <a title="DOI URL" href="https://doi.org/10.1021/acs.jpclett.4c00945">https://doi.org/10.1021/acs.jpclett.4c00945</a></p> <p> </p> <p>The dataset contains the following raw data:</p> <p>## FILE DESCRIPTION<br>--------------<br>### Figure 2<br>- Fig2a.txt : Representative characteristic _I-V_ response of memristor (5 cycles)<br>- Fig2b.txt : Upper vertex-dependent multilevel/multistate analog resistive switching<br>- Fig2c.txt : Characteristic _I-V_ response of 20 distinct devices<br>- Fig2d.txt : Endurance measurements for 1000 cycles of the LRS (ON state) and HRS (OFF state)</p> <p>### Figure 3<br>- Fig3a.txt : Characteristic _I-V_ response with an upper vertex of 0.25 V<br>- Fig3b.txt : Characteristic _I-V_ response with an upper vertex of 0.75 V<br>- Fig3c.txt : Characteristic _I-V_ response with an upper vertex of 1.25 V</p> <p>### Figure 4<br>- Fig4a.txt : IS spectrum under dark conditions at 0 V<br>- Fig4b.txt : IS spectrum under dark conditions at 0.2 V<br>- Fig4c.txt : IS spectrum under dark conditions at 0.3 V<br>- Fig4d.txt : IS spectrum under dark conditions at 0.4 V<br>- Fig4e.txt : IS spectrum under dark conditions at 0.6 V<br>- Fig4f.txt : IS spectrum under dark conditions at 1.0 V</p> <p>### Figure 5<br>- Fig5a.txt : Voltage-dependent transient current response of the perovskite memristor<br>- Fig5b.txt : Magnified view of the transient current response of a single voltage pulse at representative applied voltages<br>- Fig5c.txt : Pulse width-dependent transient current response<br>- Fig5d.txt : Corresponding magnified view of the first and last transient responses<br>- Fig5e.txt : Synaptic potentiation and depression characteristic response of the memristor</p> <p>### Figure 6<br>- Fig6a.txt : Transient current response of the volatile perovskite memristor with a single long pulse vs. a train of short pulses at 0.4 V<br>- Fig6b.txt : Corresponding magnified view of the transient current response of a first voltage pulse at 0.4 V<br>- Fig6c.txt : Transient current response of the volatile perovskite memristor with a single long pulse vs. a train of short pulses at 0.8 V<br>- Fig6d.txt : Corresponding magnified view of the transient current response of a first voltage pulse at 0.8 V<br>- Fig6e.txt : Transient current response of the volatile perovskite memristor with a single long pulse vs. a train of short pulses at 1.2 V<br>- Fig6f.txt : Corresponding magnified view of the transient current response of a first voltage pulse at 1.2 V</p>
Swahili word analogy dataset
<p>Swahili Analogy dataset contains pairs of words that are organized in 4's to facilitate word analogy test. Word analogy test is used to evaluate the quality of word representation vectors from a language model. The dataset contains 12,864 questions that have been organized in 12 categories.</p>
cif files and VASP files for Li6PS5X crystals and a set of isovalent analogs
<p>This dataset is meant as supplementary material to the research paper titled "The Devil in the Details: Lessons from Li6PS5X for Robust High-Throughput Workflows" authored by Asif Iqbal Bhatti, Sandeep Kumar, Catharina Jaeken, Michael Sluydts, Danny E.P. Vanpoucke and Stefaan Cottenier. At the time of writing this description, the manuscript is under peer review (publication info will be added here once published). </p> <p>The data set contains cif files as well as VASP input and output for all crystals that are being discussed in this paper.</p>
Analog and digital sun sensor thermal test measurements
<p>Internal temperature sensor measurements for analog and digital sun sensor thermal (only thermal no vacuum) cycling tests. An additional external omega temperature logger for reference was used.</p> <p>During the first test the soaking time was wrongly configured in the thermal chamber settings.</p>
Controllable temporal dynamics of titanium oxide memristor for analog time-based neuromorphic computing: Dataset
<p>Dataset used to produce graphs related to the temporal behavior of the Pt/TiO/Au memristors.</p>
Flow dynamics and pump kinematics in polychaete burrows constructed in a transparent mud analog
We used Particle Tracking Velocimetry (PTV) to measure fluid flow within burrows constructed by the polychaete Alitta succinea in a transparent mud analog. We also measured the kinematics of the undulatory pumping by the polychaete that drives flow through the burrow. The flow velocity data is presented in the spreadsheet worm_burrow_particle_tracking_data.csv and consists of the x and y coordinates (in mm) of each tracked particle, the time at which it was tracked (in seconds) and the velocity of the particle at that time (in mm per second). The ClipID is the reference of the video clip the data is from, and is a unique identifier. The SequenceID is retained between the pump dynamics data and the particle tracking data, because worm kinematics and flow dynamics were recorded simultaneously. Each tracked particle in a given sequence has a unique TrackID. The worm kinematics data consists of the track of the peak of the undulatory wave created as an individual polychaete ventilates its burrow and is presented in the spreadsheet worm_burrow_pump_dynamics_data.csv. The variables included are the x and y coordinates of the wave peak (in mm), the time at which the point was taken (in seconds) and the instantaneous velocity of the wave peak at that time (in mm per second). The ClipID is the reference of the video clip the data is from, and is a unique identifier. The SequenceID is retained between the pump dynamics data and the particle tracking data, because worm kinematics and flow dynamics were recorded simultaneously. Each tracked wave in a given sequence has a unique TrackID. The metadata, in the spreadsheet worm_burrow_metadata.csv, gives the polychaete Individual ID (a unique identifier for each specimen used) for each Clip ID and Sequence ID from the data spreadsheets, the location in the burrow at which the video was taken (between the head of the worm and the burrow entrance is "ahead", between the tail of the worm and the burrow exit is "behind", and a video of
ASPLOS20-AE Artifact Dataset for 'Noise-Aware Dynamical System Compilation for Analog Devices with Legno'
<p>The empirical model database and dataset for the ASPLOS 2020 Paper 'Noise-Aware Dynamical System Compilation for Analog Devices with Legno'</p>
Bioactive compounds with no structural analogs (high-confidence activity data)
<p>A set of 52,815 unique bioactive compounds (human targets, high-confidence activity data) with no structural analogs with high-confidence activity data was extracted from ChEMBL. For each compound the ChEMBL compound ID (CHEMBLID_Compound) and high-confidence target annotation(s) (CHEMBLID_Targets) are provided. The data set was generated as a part of an analysis to be published in 'Medicinal Chemistry Communications'. </p>
Systematic Design of Analogs of Active Compounds Covering More than 1000 Targets
<p>The analog database consisting of 1,297,204 virtual compounds is provided. Virtual compounds are reported in SMILES representation. In addition, for each virtual compound all available ChEMBL analogs (CHEMBL_COMPOUND_ID) and their activities (CHEMBL_TARGET_IDs) are given.</p>
Analog series from ChEMBL, PubChem, and DrugBank
<p>The datasets consist of analog series and key compounds extracted from ChEMBL, PubChem, and DrugBank. For each compound structural and activity information is provided. </p>
Analog series-based scaffolds from ChEMBL with associated activity information
<p>Reported is the activity information for the 12,294 analog series-based (ASB) scaffolds extracted from ChEMBL database. For each ASB scaffold structural and activity information for all analogs comprising the analog series is provoded. </p>
PAINS containing analog series
<p>The file contains analog series that consist exclusively of compounds with PAINS motifs and analog series that contain both PAINS and non-PAINS. For all 54,384 compounds ("cid(PubChem)") in these analog series their corresponding PAINS classes ("PAINS Class") and analog series ID ("ASs ID") are given. Compound IDs ("cid(PubChem)") refer to the original PubChem compound identifiers. All analog series are annotated as 'PAINS' or 'PAINS+non-PAINS' ("ASs Type") respectively to their overall composition. </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.