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10 results for “marine benthic invertebrates”
Code and data for "Global warming generates predictable extinctions of warm- and cold-water marine benthic invertebrates via thermal habitat loss"
<pre>This repository contains the following information: Datasets S1 to S4 can all be loaded, manipulated, and analysed in R using script provided in Data S5 to obtain the results of the paper, Reddin et al. 2022, "Global warming generates predictable extinctions of warm and cold-water marine benthic invertebrates via thermal habitat loss". Data S1. (separate file) The original downloaded PaleoDB dataset. Data S2. (separate file) The pre-prepared dataset of occurrences. Data S3. (separate file) The finished environmental dataset. Data S4. (separate file) Additional environmental dataset. Data S5. (separate file) The R-code for the main analysis. Data S6. (compressed directory) Output data and code from the simulations. Table S7 (separate file). List of data source publications for PaleoDB data used in our study. Listed are the data source author list (ref_author), year (ref_pubyr), and reference number as appears in the PaleoDB (reference_no). </pre>
Figure 2 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 2. – Three images of organisms obtained by cropping images of lots; from left to right: Chalinidae (Porifera), Polyclinidae (Chordata), Hormatidae (Cnidaria).
Figure 3 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 3. – Image of a batch of macro-invertebrate bycatch organisms from Kerguelen Exclusive Economical Zone (Poker 4 survey, 2017), including corals, a crinoïd, an ophiurid, a sea urchin and a brachiopoda; organisms are incomplete and have been quickly spread out over a small plate to take the picture.
Figure 6 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 6. – Example of detection and classification obtained with an image including an Ophiuroid, a piece of coral and a sea star with network 2; red squares and annotations have been provided by the computer with no human action.
Figure 5 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 5. – Example of detection and classification obtained with an image including Ascidians and a sea star with network 2; red squares and annotations have been provided by the computer with no human action.
Figure 2 in Bendima: a database for marine macro-invertebrate bycatch data designed to improve reproducibility in benthic ecology
Figure 2. – The proportion of the various phyla represented in the Bendima database, based on the number of single organisms or colonies.
Figure 1 in Bendima: a database for marine macro-invertebrate bycatch data designed to improve reproducibility in benthic ecology
Figure 1. – Images and samples collection; sorting of the caught organisms (A), full photographing (B), taxa identification/counting/measurement and storage in the Bendima database in the form of cropped images (C), conservation of representative sub-samples for taxonomy and DNA barcoding (D).
Figure 3 in Bendima: a database for marine macro-invertebrate bycatch data designed to improve reproducibility in benthic ecology
Figure 3. – The geographical coverage of the Bendima database; the location of the zoomed area is in provided in the inset world map; stations are aggregated into presence data according to a grid of cells of 1°; the letters and num- bers relate to the name of the French Economic Exclusive Zones (EEZ) and various geomorphic structures located in international waters: A: Kerguelen EEZ, B: Crozet EEZ, C: Saint-Paul et Amsterdam EEZ, 1: Del Cano ridge, 2: Elan Bank, 3: Ob et Lena Bank, 4 and 5: Antarctica shelf.
Dataset of Larval dispersal simulations of marine benthic invertebrates around New Zealand
<p>Dataset generated from biophysical simulations modelling larval dispersal around New Zealand with OpenDrift and the Moana Hindcast Backbone from 15 different locations and 4 different maximum PLD lengths. </p> <p>Within each file, the first 2 columns are a particles starting co-ordinates, columns 3 and 4 are ending co-ordinates, column 5 was state at the end of simulations (contact Charles Michie for more information), and column 6 is the origin.</p> <p>Abbreviations for locations: LMR - Leigh Marine Reserve, CTC - Cathedral Cove, RKK - Rongokako, CST - Castlepoint, ISB - Island Bay, KAI - Kaikoura, SPO - Seal Point, HMB - Half Moon Bay, DBS - Doubtful Sound, JSB - Jackson Bay, CFW - Cape Foulwind, OKB - Okiwi Bay, CPE - Cape Egmont, PIH - Piha Beach, CPR - Cape Reinga (also called FRN - Far North elsewhere).</p> <p>Data will accompany the publication "Variation in the dispersal of marine invertebrate larvae around coastal New ZealandVariation in the dispersal of marine invertebrate larvae around coastal New Zealand" </p>
Figure 7 in Using deep-learning for automatic identification of images of marine benthic macro-invertebrate bycatch: a proof of concept
Figure 7. – Example of detection and classification obtained with an image including Crinoïds, a Gastropod and pieces of seaweed with network 2; red squares and annotations have been provided by the computer with no human action.
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.