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13 results for “Fish markets”

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Fig 3.4 in Consumer trend and fish distribution in fish markets in Batticaloa district, Eastern Sri Lanka

Fig 3.4: The A and B graph shows the number of Reef fish species and Shore and Inland fish species respectively vary with the season (Dec13 to May 14).

opencc-by-4.0Dec 2020View details →
zenodo40/100

Fig 3.3 in Consumer trend and fish distribution in fish markets in Batticaloa district, Eastern Sri Lanka

Fig 3.3: Overall fish availability (%) in Urban (L)and Rural(R) markets, Here, Val-Valichennai, Odu-Oddumavadi, Bat-Batticaloa, Kal-Kaluvanchikuddy, Ara-Arampathy, Era-Eravur, Kat-Kattankuddy, Che-Chenkalady, Van-Vantharumoolai, Kir-Kiran, Kar-Kalar.

opencc-by-4.0Dec 2020View details →
zenodo36/100

Fig 3.2 in Consumer trend and fish distribution in fish markets in Batticaloa district, Eastern Sri Lanka

Fig 3.2: Availability of Shore line and Reef fish (family) in Urban and Rural Markets

opencc-by-4.0Dec 2020View details →
zenodo36/100

Fig 3.1 in Consumer trend and fish distribution in fish markets in Batticaloa district, Eastern Sri Lanka

Fig 3.1: Distribution of shore and reef fishes in urban and rural markets

opencc-by-4.0Dec 2020View details →
zenodo36/100

Fig 2.1 in Consumer trend and fish distribution in fish markets in Batticaloa district, Eastern Sri Lanka

Fig 2.1: Location of Fish Markets in Batticaloa District (Urban and Rural markets)

opencc-by-4.0Dec 2020View details →
zenodo36/100

Fig 5 in Overview of Mogadishu fish market

Fig 5: Price determination

opencc-by-4.0Dec 2022View details →
zenodo36/100

Fig 2 in Overview of Mogadishu fish market

Fig 2: Recorded Favorite Fish

opencc-by-4.0Dec 2022View details →
zenodo36/100

Fig 1 in Overview of Mogadishu fish market

Fig 1: Researcher with Yellow Fin and Skipjack Tuna at Mogadishu Fish Market

opencc-by-4.0Dec 2022View details →
zenodo36/100

Fig 3 in Overview of Mogadishu fish market

Fig 3: Mogadishu Fish Market

opencc-by-4.0Dec 2022View details →
dryad36/100

Reinforcement learning theory reveals the cognitive requirements for solving the cleaner fish market task

<p>Learning is an adaptation that allows individuals to respond to environmental stimuli in ways that improve their reproductive outcomes. The degree of sophistication in learning mechanisms potentially explains variation in behavioural responses. Here, we present a model of learning that is inspired by documented intra- and interspecific variation in the performance in a simultaneous two-choice task, the 'biological market task'. The task presents a problem that cleaner fish often face in nature: the decision of choosing between two client types; one that is willing to wait for inspection and one that may leave if ignored. The cleaners' choice hence influences the future availability of clients, i.e. it influences food availability. We show that learning the preference that maximizes food intake requires subjects to represent in their memory different combinations of pairs of client types rather than just individual client types. In addition, subjects need to account for future consequences of actions, either by estimating expected long-term reward or by experiencing a client leaving as a penalty (negative reward). Finally, learning is influenced by the absolute and relative abundance of client types. Thus, cognitive mechanisms and ecological conditions jointly explain intra and interspecific variation in the ability to learn the adaptive response.</p>

opencc-zeroOct 2019View details →
dryad36/100

Reinforcement learning theory reveals the cognitive requirements for solving the cleaner fish market task

Open the record for dataset details and reuse information.

publicOct 2019View details →
zenodo32/100

Lighthouse Fish Market on James St. N

Created with Polycam on July 14, 2021 at https://w3w.co/spend.oldest.peachy Source: Objaverse 1.0 / Sketchfab

opencc-by-nc-sa-2.0Jul 2021View details →
zenodo24/100

Fig 4 in Overview of Mogadishu fish market

Fig 4: where people buy fish

opencc-by-4.0Dec 2022View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record