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2,206 results for “Communications”
Figure 5c. from: Defining the Scholarly Commons - Reimagining Research Communication. Report of Force11 SCWG Workshop, Madrid, Spain, February 25-27, 2016 - Research Ideas and Outcomes 2: e9340 (26 May 2016) https://doi.org/10.3897/rio.2.e9340
Figure 5c. - Fair of the Future of Scholarly CommunicationFigure 5a.Figure 5b.Figure 5c.Figure 5d. <br> Screenshot of public Trello board
Figure 5b. from: Defining the Scholarly Commons - Reimagining Research Communication. Report of Force11 SCWG Workshop, Madrid, Spain, February 25-27, 2016 - Research Ideas and Outcomes 2: e9340 (26 May 2016) https://doi.org/10.3897/rio.2.e9340
Figure 5b. - Fair of the Future of Scholarly CommunicationFigure 5a.Figure 5b.Figure 5c.Figure 5d. <br> Example of Trello card comment with link to another idea. #dep_on - idea depends on another idea; "https://trello.com/c/RaNcbKSe" - is the short link to the Trello card with the connected idea. In the visualization it would be represented as a line connecting two ideas.
Figure 7a. from: Defining the Scholarly Commons - Reimagining Research Communication. Report of Force11 SCWG Workshop, Madrid, Spain, February 25-27, 2016 - Research Ideas and Outcomes 2: e9340 (26 May 2016) https://doi.org/10.3897/rio.2.e9340
Figure 7a. - Use of Trello during the workshopFigure 7a.Example of Trello card with tags. "using the public domain" - the idea name; #G2 - the idea came from the Group 2; #viz - the idea is ready to be included in the visualization; #triple - the idea has a link to another idea in the group's vision. Figure 7b.Example of Trello card comment with link to another idea. #dep_on - idea depends on another idea; "https://trello.com/c/RaNcbKSe" - is the short link to the Trello card with the connected idea. In the visualization it would be represented as a line connecting two ideas.Figure 7c.Screenshot of public Trello board <br>
Figure 9a. from: Defining the Scholarly Commons - Reimagining Research Communication. Report of Force11 SCWG Workshop, Madrid, Spain, February 25-27, 2016 - Research Ideas and Outcomes 2: e9340 (26 May 2016) https://doi.org/10.3897/rio.2.e9340
Figure 9a. - Visualization showing one example of development of a group's vision and progress to group principlesFigure 9a.One group's vision as a collection of ideas (session 7)Figure 9b.One group's vision as a interconnected elements (triples, derived after session 7)Figure 9c.One group's suggested principles (session 11) <br>
Figure 5a. from: Defining the Scholarly Commons - Reimagining Research Communication. Report of Force11 SCWG Workshop, Madrid, Spain, February 25-27, 2016 - Research Ideas and Outcomes 2: e9340 (26 May 2016) https://doi.org/10.3897/rio.2.e9340
Figure 5a. - Fair of the Future of Scholarly CommunicationFigure 5a.Figure 5b.Figure 5c.Figure 5d. <br> Example of Trello card with tags. "using the public domain" - the idea name; #G2 - the idea came from the Group 2; #viz - the idea is ready to be included in the visualization; #triple - the idea has a link to another idea in the group's vision.
Figure 3b. from: Defining the Scholarly Commons - Reimagining Research Communication. Report of Force11 SCWG Workshop, Madrid, Spain, February 25-27, 2016 - Research Ideas and Outcomes 2: e9340 (26 May 2016) https://doi.org/10.3897/rio.2.e9340
Figure 3b. - Islands of possibilities (part of YKON facilitation) at Madrid workshopFigure 3a.Islands of possibilities located in workshop roomFigure 3b.Personality modification islandFigure 3c.Observation island <br> Personality modification island
Figure 6c. from: Defining the Scholarly Commons - Reimagining Research Communication. Report of Force11 SCWG Workshop, Madrid, Spain, February 25-27, 2016 - Research Ideas and Outcomes 2: e9340 (26 May 2016) https://doi.org/10.3897/rio.2.e9340
Figure 6c. - Workshop impressionsFigure 6a.Figure 6b.Figure 6c.Figure 6d. <br> One group's vision as a interconnected elements (triples, derived after session 7)
Figure 3c. from: Defining the Scholarly Commons - Reimagining Research Communication. Report of Force11 SCWG Workshop, Madrid, Spain, February 25-27, 2016 - Research Ideas and Outcomes 2: e9340 (26 May 2016) https://doi.org/10.3897/rio.2.e9340
Figure 3c. - Islands of possibilities (part of YKON facilitation) at Madrid workshopFigure 3a.Islands of possibilities located in workshop roomFigure 3b.Personality modification islandFigure 3c.Observation island <br> Observation island
Figure 3a. from: Defining the Scholarly Commons - Reimagining Research Communication. Report of Force11 SCWG Workshop, Madrid, Spain, February 25-27, 2016 - Research Ideas and Outcomes 2: e9340 (26 May 2016) https://doi.org/10.3897/rio.2.e9340
Figure 3a. - Islands of possibilities (part of YKON facilitation) at Madrid workshopFigure 3a.Islands of possibilities located in workshop roomFigure 3b.Personality modification islandFigure 3c.Observation island <br> Islands of possibilities located in workshop room
Figure 8a. from: Defining the Scholarly Commons - Reimagining Research Communication. Report of Force11 SCWG Workshop, Madrid, Spain, February 25-27, 2016 - Research Ideas and Outcomes 2: e9340 (26 May 2016) https://doi.org/10.3897/rio.2.e9340
Figure 8a. - Workshop visualization - collection of ideas and group visionsFigure 8a.Visualization showing all ideas generated collectively in the first round (session 3, dark grey) or second round (session 4, light grey), and those generated by the respective groups (solid colors) as part of their vision of scholarly communication. Figure 8b.Visualizaton showing all groups' visions as interconnected elements (triples), with common elements overlapping. <br>
Shared Acoustic Codes Underlie Emotional Communication in Music and Speech - Evidence from Deep Transfer Learning (Datasets)
<p>This repository contains the datasets used in the article "Shared Acoustic Codes Underlie Emotional Communication in Music and Speech - Evidence from Deep Transfer Learning" (Coutinho & Schuller, 2017). </p> <p>In that article four different data sets were used: SEMAINE, RECOLA, ME14 and MP (acronyms and datasets described below). The SEMAINE (speech) and ME14 (music) corpora were used for the unsupervised training of the Denoising Auto-encoders (domain adaptation stage) - only the audio features extracted from the audio files in these corpora were used and are provided in this repository. The RECOLA (speech) and MP (music) corpora were used for the supervised training phase - both the audio features extracted from the audio files and the Arousal and Valence annotations were used. In this repository, we provide the audio features extracted from the audio files for both corpora, and Arousal and Valence annotations for some of the music datasets (those that the author of this repository is the data curator).</p> <p>Below, you can find description of the various corpora, the details about the data stored in this repository and information on how to obtain the rest of the data used by Coutinho and Schuller (2017).</p> <p><strong>SEMAINE (speech)</strong></p> <p>The SEMAINE corpus (McKeown, Valstar, Cowie, Pantic & Schroder, 2012) was developed specifically to address the task of achieving emotion-rich interactions, and it is adequate for this task as it comprises a wide range of emotional speech. It includes video and speech recordings of spontaneous interactions between human and emotionally stereotyped `characters'. Coutinho & Schuller (2017) used a subset of this database (called <em>Solid-SAL</em>). The <em>Solid-SAL</em> dataset is freely available for scientific research purposes (see http://semaine-db.eu). This repository includes the audio features used in Coutinho & Schuller (2017) (under features/SEMAINE).</p> <p><strong>RECOLA (speech)</strong></p> <p>The RECOLA database (Ringeval, Sonderegger, Sauer & Lalanne, 2013) consists of multimodal recordings (audio, video, and peripheral physiological activity) of spontaneous dyadic interactions between French adults. Coutinho & Schuller (2017) used the RECOLA-Audio module which consists of the audio recordings of each participant in the dyadic phase of the task. In particular, they used the non-segmented high-quality audio signals (WAV format, 44.1kHz, 16bits), obtained through unidirectional headset microphones, of the first five minutes of each interaction. Annotations consist of time-continuous ratings of the level of Arousal and Valence dimensions of emotion perceived by each rater while seeing and listening the audio-visual recordings of each participant task. The publicly available annotated dataset includes only part of the data which amounts to a total number of 23 instances. The time frame length used by Coutinho & Schuller (2017) is 1s (the original annotations were downsampled). This repository includes the audio features used in Coutinho & Schuller (2017) (under features/RECOLA). To obtain the annotations you should contact the author of the original study (see https://diuf.unifr.ch/diva/recola/download.html for further details).</p> <p><strong>ME14 (music)</strong></p> <p>The MediaEval ``Emotion in Music'' task is dedicated to the estimation of Arousal and Valence scores continuously in time and value for song excerpts from the Free Music Archive. Coutinho and Schuller (2017) used the whole corpus (development and test sets for the 2014 challenge) which includes 1,744 songs belonging to 11 musical styles -- Soul, Blues, Electronic, Rock, Classical, Hip-Hop, International, Folk, Jazz, Country, and Pop (maximum of five songs per artist). This repository includes the audio features used in Coutinho & Schuller (2017) (under features/ME14). The full dataset (including annotations) can be obtained from http://www.multimediaeval.org/mediaeval2014/emotion2014/.</p> <p><strong>MP (music)</strong></p> <p>This is a corpus compiled specifically for this work described in Coutinho & Schuller (2017) using data collected in four previous studies. It consists of emotionally diverse full music pieces from a variety of musical styles (Classical and contemporary Western Art, Baroque, Bossa Nova, Rock, Pop, Heavy Metal, and Film Music). Annotations were obtained in controlled laboratory experiments whereby the emotional character of each piece was evaluated time-continuously in terms of levels of Arousal and Valence perceived by listeners (ranging between 35 to 52 in the four studies). In what follows, some details about the various studies are described.</p> <ul> <li>MP<sub>DB1</sub>: This subset of the MP corpus consists of the data reported by Korhonen (2004), and gently made available by the author. This dataset includes six full (or long excerpts) music pieces ranging from 151s to 315s in length (only classical music). Each piece was annotated by 35 participants (14 females). The time series correspondents to each music piece were collected at 1Hz. The golden standard for each piece was computed by averaging the individual time series across all raters. This repository includes the audio features used in Coutinho & Schuller (2017) (under features/MP/DB1). To obtain the labels please contact the author of the original study.</li> <li>MP<sub>DB2</sub>: The dataset by Coutinho & Cangelosi (2011) includes 9 full pieces (43s to 240s long) of classical music (romantic repertoire) annotated by 39 subjects (19 females). Values were recorded every time the mouse was moved with a precision of 1 ms. The resultant timeseries were then resampled (moving average) to a synchronous rate of 1 Hz. The golden standard for each piece was computed by averaging the individual time series across all raters. This repository includes the audio features (under features/MP/DB2) and labels (under annotations/MP/DB2) used in Coutinho & Schuller (2017).</li> <li>MP<sub>DB3</sub>: This dataset was collected by Coutinho & Dibben (2012) and it consists of 8 pieces of film music (84s to 130s long) taken from the late 20th century Hollywood film repertoire. Emotion ratings were given by 52 participants (26 females). The annotation procedure, data processing, and golden standard calculations were identical to MP<sub>DB2</sub>. This repository includes the audio features (under features/MP/DB3) and labels (under annotations/MP/DB3) used in Coutinho & Schuller (2017).</li> <li>MP<sub>DB4</sub>: This dataset was collected by Grewe, Nagel, Kopiez and Altenmüller (2007), and gently made available by the authors. It includes seven music pieces (127s to 502s in length) of heterogeneous styles (e.g., Rock, Pop, Heavy Metal, Classical). Each music piece was annotated by 38 participants (29 females) using an identical methodology to MP<sub>DB2</sub> and MP<sub>DB3</sub>. Data processing and golden standard calculations were also identical. This repository includes the audio features (under features/MP/DB4) used in Coutinho & Schuller (2017). To obtain the labels contact the authors of the original study</li> </ul> <p> </p> <p><strong>Bibliography</strong></p> <p>Coutinho, E., & Cangelosi, A. (2011). Musical emotions: predicting second-by-second subjective feelings of emotion from low-level psychoacoustic features and physiological measurements. <em>Emotion</em>, <em>11</em>(4), 921.</p> <p>Coutinho, E., & Dibben, N. (2013). Psychoacoustic cues to emotion in speech prosody and music. <em>Cognition & Emotion</em>, <em>27</em>(4), 658-684.</p> <p>Coutinho E, Schuller B (2017) Shared acoustic codes underlie emotional communication in music and speech—Evidence from deep transfer learning. PLoS ONE 12(6): e0179289. https://doi. org/10.1371/journal.pone.0179289.</p> <p>Grewe, O., Nagel, F., Kopiez, R., Altenmüller, E. (2007). Emotions over time: synchronicity and development of subjective, physiological, and facial affective reactions to music. <em>Emotion, 7</em>(4), pp. 774-788. DOI: 10.1037/1528-3542.7.4.774.</p> <p>Korhonen, M. (2004). Modeling Continuous Emotional Appraisals of Music Using System Identification. Available from: http://hdl.handle.net/10012/879.</p> <p>McKeown, G., Valstar, M., Cowie, R., Pantic, M., Schroder, M. (2012). The SEMAINE Database: Annotated Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent. <em>IEEE Transactions on Affective Computing</em>, 3, pp. 5-17. DOI: http://doi.ieeecomputersociety.org/10.1109/T-AFFC.2011.20.</p> <p>Ringeval, F., Sonderegger, A., Sauer, J. & Lalanne, D. (2013). Introducing the RECOLA Multimodal Corpus of Remote Collaborative and Affective Interactions. In <em>Proceedings of the 2nd International Workshop on Emotion Representation, Analysis and Synthesis in Continuous Time and Space (EmoSPACE 2013)</em>, Shanghai, China. IEEE</p>
Lookout Fire Time-Lapse Video Taken from the Roswell Communication Tower for the Period of Record Aug. 10 - Oct. 23, 2023.
<p>A lightning strike fire started on Saturday, August 5, within the H.J. Andrews Experimental Forest, between the base of Lookout cliff and the ridge dividing Lookout and Mack Creek drainages. As of today, August 7, the fire is 2.5 acres. Two helicopters are traveling between the Blue River reservoir and the fire carrying water. The plan is to keep knocking back the fire until ground crews can get a line around it and contain it. It is burning in steep terrain, in old growth with dense understory, which is making it a challenge for experienced ground crews. In addition to the helicopters, a hotshot crew has been assigned to the fire, most likely starting August 8.</p><p>These images were taken from remote based StarDot and NetCam IP cameras positioned 13 meters up the Roswell Communication tower located in the north east side of the HJ Andrews Experimental Forest. The HJAHQCAM video was recorded from a StarDot camera positioned on top of the HJ Andrews main office building. Andrews Forest LTER collected and manged these data in real-time and compiled a final time-lapse video of each camera using a multi-threaded python program.</p><p>Additional Resources:<br><a href="https://andrewsforest.oregonstate.edu/about/news-events/lookout-fire-updates-2023">Andrews Forest LTER</a><br><a href="https://www.youtube.com/@AndrewsForest/playlists">Andrews Forest Youtube</a><br><a href="https://inciweb.wildfire.gov/incident-information/orwif-lookout-fire">InciWeb</a></p><p><strong>This material is based upon work supported by the H.J. Andrews Experimental Forest and Long Term Ecological Research (LTER) program under the NSF grant LTER8 DEB-2025755.</strong></p>
Reassessing science communication for effective farmland biodiversity conservation.
<p>Data set and code for analysing a communication case study about biodiversity conservation and farming in the European decision-making environment. It includes: (1) a literature corpus, consisting of 5988 digital press releases and news texts, covering the period of 2015-2020, from 40 different organizations involved in European farming and food decision-making processes; (2) R code scripts for analysis and graphic representation.</p>
Transformation of social relationships in COVID-19 America: Remote communication may amplify political echo chambers
<p>The COVID-19 pandemic, with millions of Americans compelled to stay home and work remotely, presented an opportunity to explore the dynamics of social relationships in a predominantly remote world. Using the 1972-2022 General Social Surveys, we found that the pandemic significantly disrupted the patterns of social gatherings with family, friends, and neighbors, but only momentarily. Drawing from the nationwide ego-network surveys of 41,033 Americans from 2020 to 2022, we found that the size and composition of core networks remained stable, though political homophily increased among non-kin relationships compared to previous surveys between 1985 and 2016. Critically, heightened remote communication during the initial phase of the pandemic was associated with increased interaction with the same partisans, though political homophily decreased during the later phase of the pandemic when in-person contacts increased. These results underscore the crucial role of social institutions and social gatherings in promoting spontaneous encounters with diverse political backgrounds.</p>
Data for Altered Glia-Neuron Communication in Alzheimer's Disease Affects WNT, p53, and NFkB Signaling Determined by snRNA-seq
<p><strong>data.tar.gz contains all files from the data directory associated with the 230313_TS_CCCinHumanAD GitHub project and includes the following:</strong></p><ul><li><strong>CellRangerCounts/</strong><ul><li><strong>GSE157827/</strong><ul><li><strong>post_soupX/ : </strong>contains 21 directories for 21 samples, which each contain 3 files obtained from ambient RNA removal with soupX. Below is a representative example, but this repo contains 1 directory per sample:<ul><li><strong>SAMN16100290_S01_AD/</strong><ul><li><strong>barcodes.tsv</strong></li><li><strong>genes.tsv</strong></li><li><strong>matrix.mtx</strong></li></ul></li></ul></li><li><strong>pre_soupX/ : </strong>contains 21 directories for 21 samples, which each contain 2 files obtained from Cell Ranger after aligning fastq files to the reference genome. Below is a representative example, but this repo contains 1 directory per sample:<ul><li><strong>SAMN16100290_S01_AD/</strong><ul><li><strong>filtered_feature_ bc_matrix.h5</strong></li><li><strong>Raw_feature_bc_matrix.h5</strong></li></ul></li></ul></li></ul></li><li><strong>GSE174367/ : </strong>contains 19 directories for 19 samples, which contain 3 files each from Cell Ranger alignment of fastq files to the reference genome. Below is a representative example, but this repo contains 1 directory per sample:<ul><li><strong>SAMN19128610_S1_CTRL/</strong><ul><li><strong>barcodes.tsv</strong></li><li><strong>genes.tsv</strong></li><li><strong>Matrix.mtx</strong></li></ul></li></ul></li></ul></li><li><strong>ccc/</strong><ul><li><strong>nichenet_grn/</strong><ul><li><strong>gr_network_human_21122021.rds : </strong>accessed in October 2023, gene regulation network – gene regulatory information from MultiNicheNet</li><li><strong>ligand_tf_matrix_nsga2r_final.rds: </strong>accessed in October 2023, ligand tf matrix for signaling path determination from MultiNicheNet</li><li><strong>signaling_network_human_21122021.rds : </strong>accessed in October 2023, signaling network – protein-protein interaction information from MultiNicheNet</li><li><strong>weighted_networks_nsga2r_final.rds : </strong>accessed in October 2023, networks weighted by literature evidence from MultiNicheNet</li></ul></li><li><strong>nichenet_prior/</strong><ul><li><strong>ligand_target_matrix.rds : </strong>accessed in April 2023, ligand to target matrix from NicheNet</li><li><strong>lr_network.rds : </strong>accessed in April 2023, ligand-receptor matrix from NicheNet</li></ul></li><li><strong>nichenet_v2_prior/</strong><ul><li><strong>ligand_target_matrix_nsga2r_final.rds : </strong>accessed in June 2023, ligand to target matrix from MultiNicheNet used to predict target genes.</li><li><strong>lr_network_human_21122021.rds : </strong>accessed in June 2023, ligand-receptor matrix from MultiNicheNet used to predict ligand-receptor pairs.</li></ul></li><li><strong>geo_multinichenet_output.rds </strong>: MultiNicheNet output for Morabito et al., 2021 data</li><li><strong>geo_signaling_igraph_objects.rds </strong>: list of igraph objects for 17 overlapping LRTs and their signaling mediators in the Morabito et al., 2021 dataset. </li><li><strong>gse_multinichenet_output.rds</strong> : MultiNicheNet output for Lau et al., 2020 data</li><li><strong>gse_signaling_igraph_objects.rds</strong> : list of igraph objects for 17 overlapping LRTs and their signaling mediators in the Lau et al., 2020 dataset </li></ul></li><li><strong>seurat_preprocessing/</strong><ul><li><strong>geo_filtered_seurat.rds : </strong>merged and filtered seurat object of Morabito et al., 2021 data</li><li><strong>geo_integrated_seurat.rds :</strong> seurat object integrated using harmony of Morabito et al., 2021 data</li><li><strong>geo_clustered_seurat.rds : </strong>clustered seurat object of Morabito et al., 2021 data</li><li><strong>geo_processed_seurat.rds : </strong>processed seurat object with final cell type assignments at specified resolution of Morabito et al., 2021 data</li><li><strong>gse_filtered_seurat.rds : </strong>merged and filtered seurat object of Lau et al., 2020 data</li><li><strong>gse_integrated_seurat.rds : </strong>seurat object integrated using harmony of Lau et al., 2020 data</li><li><strong>gse_clustered_seurat.rds :</strong> clustered seurat object of Lau et al., 2020 data</li><li><strong>gse_processed_seurat.rds : </strong>processed seurat object with final cell type assignments at specified resolution of Lau et al., 2020 data </li></ul></li></ul>
Superadditive Communications with the Green Machine: Online Data Repository
<p>This online repository contains selected datasets and scripts for data processes in the paper "Superadditive Communications with the Green Machine: A Practical Demonstration of Nonlocality without Entanglement". arXiv.2310.05889</p>
Ripple effects in a communication network: Anti-eavesdropper defence elicits elaborated sexual signals in rival males
<p>Emitting conspicuous signals into the environment to attract mates comes with the increased risk of interception by eavesdropping enemies. As a defence, a commonly described strategy is for signallers to group together in leks, diluting each individual's risk. Lekking systems are often highly social settings in which competing males dynamically alter their signalling behaviour to attract mates. Thus, signalling at the lek requires navigating fluctuations in risk, competition, and reproductive opportunities. Here, we investigate how behavioural defence strategies directed at an eavesdropping enemy have cascading effects across the communication network. We investigated these behaviours in the túngara frog (<em>Engystomops pustulosus</em>), examining how a calling male's swatting defence directed at frog-biting midges indirectly affects the calling behaviour of his rival. We found that the rival responds to swat-induced water ripples by increasing his call rate and complexity. Then, performing phonotaxis experiments, we found that eavesdropping fringe-lipped bats (<em>Trachops cirrhosus</em>) do not exhibit a preference for a swatting male compared to his rival, but females strongly prefer the rival male. Defences to minimize attacks from eavesdroppers thus shift the mate competition landscape in favour of rival males. By modulating the attractiveness of signalling prey to female receivers, we posit that eavesdropping micropredators likely have an unappreciated impact on the ecology and evolution of sexual communication systems.</p>
Fig. 2 in Social Behavior and Communication in the Neotropical Cicada Fidicina mannifera (Fabricius) (Homoptera: Cicadidae)
Fig. 2. Waveforms of two signals of Fidicina mannifera. (A) One complete call; (B) Twelve pulses from a calling song. Total X axis length is 280 ms in (A) and 18 ms in (B).
Fig. 1 in Social Behavior and Communication in the Neotropical Cicada Fidicina mannifera (Fabricius) (Homoptera: Cicadidae)
Fig. 1. Audiospectrograms of four acoustic displays in the repertoire of Fidicina mannifera. (A) Song; (B) Calls, given in alternation by two males (numbers below the calls identify the caller); (C) Low-amplitude song; (D) Disturbance sound.
Fig. 3 in Social Behavior and Communication in the Neotropical Cicada Fidicina mannifera (Fabricius) (Homoptera: Cicadidae)
Fig. 3. Proportion of calls vs. songs given by male Fidicina mannifera in relation to nearestneighbor distance. Note that the relationship with distance is not linear (fitted curve is logarithmic).
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.