Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6
datasets available to search
ShareScore release 0.9.0
Dataset results
6 results for “object categorization”
A dataset for evaluating one-shot categorization of novel object classes
<p>From just a single example, we can derive quite precise intuitions about what other class members look like. This stands in stark contrast to machine learning algorithms, which typically require tens or even hundreds of thousands of examples to learn a new category. One of the most important open questions in our field is: How do humans achieve this? The stimuli and data provided here (in MATLAB format) are from thousands of crowd-sourced human responses to novel objects. The data can be used to test machine learning generalization as compared to human and also can be used as a test bed for various kinds of category learning models. </p>
Heatmap of global collection units Any collection object in any museum can be categorized into only one of the 304 cells (19 collection types by 16 geographic regions).A "collection unit" is a single museum's holdings within a single cell. For 73 museums, there are 22,192 possible collection units. The heatmap shows the 1957 collection units with more than 10,000 objects. See supplementary materials for details and for a heatmap of the 242 collection units with more than 1 million objects. in A global approach for natural history museum collections
Heatmap of global collection units Any collection object in any museum can be categorized into only one of the 304 cells (19 collection types by 16 geographic regions).A "collection unit" is a single museum's holdings within a single cell. For 73 museums, there are 22,192 possible collection units. The heatmap shows the 1957 collection units with more than 10,000 objects. See supplementary materials for details and for a heatmap of the 242 collection units with more than 1 million objects.
Data from: Are categorical spatial relations encoded by shifting visual attention between objects?
Perceiving not just values, but relations between values, is critical to human cognition. We tested the predictions of a proposed mechanism for processing categorical spatial relations between two objects—the shift account of relation processing—which states that relations such as 'above' or 'below' are extracted by shifting visual attention upward or downward in space. If so, then shifts of attention should improve the representation of spatial relations, compared to a control condition of identity memory. Participants viewed a pair of briefly flashed objects and were then tested on either the relative spatial relation or identity of one of those objects. Using eye tracking to reveal participants' voluntary shifts of attention over time, we found that when initial fixation was on neither object, relational memory showed an absolute advantage for the object following an attention shift, while identity memory showed no advantage for either object. This result is consistent with the shift account of relation processing. When initial fixation began on one of the objects, identity memory strongly benefited this fixated object, while relational memory only showed a relative benefit for objects following an attention shift. This result is also consistent, although not as uniquely, with the shift account of relation processing. Taken together, we suggest that the attention shift account provides a mechanistic explanation for the overall results. This account can potentially serve as the common mechanism underlying both linguistic and perceptual representations of spatial relations.
Data from: Emerging Representational Geometries in the Visual System Predict Reaction Times for Object Categorization
Recognizing an object takes just a fraction of a second, less than the blink of an eye. Applying multivariate pattern analysis, or "brain decoding", methods to magnetoencephalography (MEG) data has allowed researchers to characterize, in high temporal resolution, the emerging representation of objects that underlie our capacity for rapid recognition. Shortly after stimulus onset, exemplar stimuli cluster by category in high-dimensional activation spaces. In these emerging activation spaces, the decodability of exemplar category varies over time, reflecting the brain's transformation of visual inputs into coherent categorical representations. How do these emerging representations relate to categorization behavior? Recently it has been proposed that the distance of an exemplar representation from a categorical boundary in an activation space is critical for perceptual decision-making, and that reaction times should therefore correlate with distance from the boundary. The predictions of this distance hypothesis have been born out in human inferior temporal cortex (IT), an area of the brain crucial for the representation of object categories. The time of peak decoding is the optimal time for category information to be "read out" from the brain's time varying representation of the stimuli. In this study, we tested the distance hypothesis, and specifically whether or not the brain reads out at the optimal time for choice behavior. Using MEG decoding methods, we show that the distance of a pattern of activity from a decision boundary through a high-dimensional activation space correlates with reaction times in a visual categorization task, but only during the period of peak decodability. Our results suggest the brain uses the optimal stimulus representation for choice behavior, and that neural representations for objects are partially constitutive of the decision process in visual perception.
Data from: Are categorical spatial relations encoded by shifting visual attention between objects?
Open the record for dataset details and reuse information.
Data from: Emerging Representational Geometries in the Visual System Predict Reaction Times for Object Categorization
Open the record for dataset details and reuse information.
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.