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78 results for “gears”
Fig. 2 in First attempt to understand the effect of pingers on static fishing gear in Bulgarian Black Sea coast
Fig. 2. Comparison of number of attacks on active and control dalyans.
Fig. 1 in First attempt to understand the effect of pingers on static fishing gear in Bulgarian Black Sea coast
Fig. 1. Correlation between the numbers of observations and attacks on active dalyans.
Table 1 in Chronic stress from fishing gear entanglement is recorded in baleen from a bowhead whale (Balaena mysticetus)
<p><i>Table 1.</i> The mean (± SD), baseline (± SD), and maximum values for cortisol and corticosterone measured in baleen from a severely entangled bowhead whale (17B6) compared to healthy subadult (<i>n</i> = 3) and adult (<i>n</i> = 4) male bowhead whales. The mean and baseline values for 17B6 were calculated using data from the preentanglement period, and the values are identical because there were no outlier values (see Methods for details).</p><table><tbody><tr><th></th><th>Cortisol (ng/g)</th><th></th><th>Corticosterone (ng/g)</th></tr></tbody><tbody><tr><th></th><td>Mean</td><td>Baseline</td><td>Maximum</td><td>Mean</td><td>Baseline</td><td>Maximum</td></tr><tr><th>Entangled</th><td>0.15 ± 0.06</td><td>0.15 ± 0.06</td><td>3.23</td><td>0.53 ± 0.17</td><td>0.53 ± 0.17</td><td>1.14</td></tr><tr><th>Subadults</th><td>0.52 ± 0.24</td><td>0.49 ± 0.12</td><td>1.60</td><td>1.06 ± 0.57</td><td>1.02 ± 0.25</td><td>2.70</td></tr><tr><th>Adults</th><td>0.35 ± 0.09</td><td>0.32 ± 0.04</td><td>1.11</td><td>0.75 ± 0.22</td><td>0.74 ± 0.13</td><td>1.67</td></tr></tbody></table>
Combining sampling gear to optimally inventory species highlights the efficiency of eDNA metabarcoding
<p>Biodiversity surveys may require the use of multiple types of sampling gear to maximize the efficiency of species detections, yet few studies have investigated how to optimally distribute effort among gear. In this study, we conducted eDNA metabarcoding and capture-based sampling surveys (electrofishing, fyke netting, gillnetting, and seining) to sample fish species richness in a large northern temperate lake. We evaluated the success of the sampling methods individually and in combination to determine the allocation of effort and cost across sampling gear that provides the optimal approach for lake-wide species inventories. We found that eDNA metabarcoding detected more species than any other sampling method, including 11 species that were not detected with any capture-based approach. Optimal gear combination analyses revealed that detected species richness is maximized when most of the effort or budget is allocated to eDNA metabarcoding, with smaller allocations to seining and fyke netting. eDNA metabarcoding and capture sampling gear showed similar patterns of spatial heterogeneity in the fish community across habitat types, with pelagic samples forming a group that was distinct from nearshore samples. Our results indicate that eDNA metabarcoding is a rapid and cost-efficient tool for biodiversity monitoring and that assessing the complementarity of multiple sampling types can inform the development of optimal approaches for measuring fish species richness.</p>
Data from: Hazard and catch composition of ghost fishing gear revealed by a citizen science clean-up initiative
<p><span>Ghost fishing, the continued catch of fishes and invertebrates by lost fishing gear, represents an animal welfare issue as well as a waste of both potential food and ecosystem resources. Fishing gear is lost by both commercial and recreational fishers, and management authorities often lack an overview of gear loss and subsequently potential impact on coastal populations. </span><span>To investigate the hazard and catch composition of lost fishing gear along the Norwegian coast</span><span>, recreational divers in collaboration with scientists conducted systematic reporting of retrieved lost fishing gear. </span><span>Through this citizen science project,</span><span> a total of 12,101 gear items were retrieved and reported, including traps, gillnets and fyke nets. Combining both data on the catch ratio of the gear and its relative quantity, we identified the five most hazardous gear types to be parlor traps, gillnets, fyke nets, wrasse traps and square collapsible traps. The parlour trap was the most hazardous trap, due to high catchability and quantity. The correct classification of gear type could not be confirmed in 2.8 – 6.1 % of the pictures taken by divers, depending on reporting format, and divers reported the wrong gear type in 1.4 % of the reports. Brown crab (<em>Cancer</em> <em>pagurus</em>) was the species most often found in retrieved gear. Furthermore, the vulnerable species European lobster (<em>Homarus</em> <em>gammarus</em>) and Atlantic cod (<em>Gadus</em> <em>morhua</em>) were also common. These results can inform future clean up-initiatives and management responses to ghost fishing, including preventive measures against gear loss and gear restrictions and customization. </span></p>
Landing Gear System Models
<p>The dataset contains a translated Simulink model, of the landing gear system introduced by Boniol and Wiels (2014) and Boniol et al. (2017). Additionally, corresponding SAML and NuSMV models are included, which were created from the Simulink model using tool support.</p>
Mitigating bycatch in trammel net fisheries using species-specific gear modifications
<p>Small-scale fisheries (SSF) use static gears like hanging nets, which are thought to interact with marine ecosystems more benignly than towed gears. Despite this, trammel nets, one of the most regularly used fishing gears in the Mediterranean SSF, generate large amounts of discards, which can account for 25% or more of the captured biomass. Discarded organisms may include endangered or threatened species such as elasmobranchs, as well as non-commercial invertebrates that damage fishing gear or cause disentanglement delays. We tested various trammel-net gear modifications, including (a) the use of a guarding net attached to the footrope, (b) increasing the size of the rigging twine between the footrope and the netting panel, and (c) decreasing the mesh size of the outer panels. The last two modifications were successful in lowering captures of the marbled electric ray Torpedo marmorata, which is commonly discarded in the study area. Both sorts of modifications are relatively simple and inexpensive to implement. The current study shows that prior evaluation of the discards profile of distinct métiers is essential to accomplish species-specific gear modifications and underlines the importance of collaboration among scientists, fishers and gear manufacturers.</p>
Planetary gear dataset
<p>Planetary Gear Dataset including 6 classes of object. The dataset was generated by capturing 150 real images (Intel Realsense D435) and annotating them with the correct labels (bounding boxes, keypoints and polygons). Further augmentation of the images completes the dataset to around 170,000 images. All tools and scripts to generate and replicate the dataset are provided, as well as the trained model and visualization scripts</p>
DEBRIS FREE SEAS: Assessing Lost Gear Removals in Southern California by a Nonprofit
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Combining sampling gear to optimally inventory species highlights the efficiency of eDNA metabarcoding
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Gearing up: Methods for quantifying gear density for fixed-gear commercial fisheries in the U.S. Atlantic
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Long-term perceptions of freshwater anglers about abandoned, lost or discarded fishing gear during their fishing careers
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Data from: Hazard and catch composition of ghost fishing gear revealed by a citizen science clean-up initiative
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Data from: Recovery linked to life history of sessile epifauna following exclusion of towed mobile fishing gear
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Mitigating bycatch in trammel net fisheries using species-specific gear modifications
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Data from: Effects of gear restriction on the abundance of juvenile fishes along sandy beaches in Hawai'i
In 2007, due to growing concerns of declines in nearshore fisheries in Hawai'i, a ban on gillnets was implemented in designated areas around the island of O'ahu in the main Hawaiian Islands. Utilizing a 17 year time-series of juvenile fish abundance beginning prior to the implementation of the gillnet ban, we examined the effects of the ban on the abundance of juveniles of soft-bottom associated fish species. Using a Before-After-Control-Impact (BACI) sampling design, we compared the abundance of targeted fishery species in a bay where gillnet fishing was banned (Kailua, O'ahu), and an adjacent bay where fishing is still permitted (Waimānalo, O'ahu). Our results show that when multiple juvenile fish species were combined, abundance declined over time in both locations, but the pattern varied for each of the four species groups examined. Bonefishes were the only species group with a significant BACI effect, with higher abundance in Kailua in the period after the gillnet ban. This study addressed a need for scientific assessment of a fisheries regulation that is rarely possible due to lack of quality data before enactment of such restrictions. Thus, we developed a baseline status of juveniles of an important fishery species, and found effects of a fishery management regulation in Hawai'i.
Gears
Source: Objaverse 1.0 / Sketchfab
Analytics of collaborations and performance of citizen science teams during the GEAR cycle 2 of the Crowd4SDG project.
<p>This data frame is part of the deliverable 4.2 of the Crowd4SDG project.</p> <p>It contains the measured analytics of citizen collaborations using new metrics/descriptors developed during the first year of the Crowd4SDG project. </p> <pre>The goal of the Crowd4SDG project is to research the extent to which Citizen Science (CS) can provide an essential source of non-traditional data for tracking progress towards the SDGs, as well as the ability of CS to generate social innovations that enable such progress. In the Crowd4SDG project, the Work Package 4 aims to develop and monitor new metrics and develop statistical models of team engagement and collaboration that contribute to the many-faceted outcomes of the citizen science projects developed within the Crowd4SDG consortium over the 3-years course of the project. Here, we share a dataframe of features collected during the GEAR cycle 2 pertaining to team composition, activity, performance and interaction dynamics. In particular, we leveraged the CoSo platform for collecting self-reported data on collaborations and task allocation structure of participating teams, as well as Slack data for measuring communication networks. The related findings are presented in the deliverable 4.4 of the Crowd4SDG project and serve as a basis for i) exhibiting the potential of using digital traces to derive measures related to team process, ii) highlighting perspectives for monitoring metrics in the next GEAR cycle.</pre>
ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - Corner Clamp Part 2 - Geared Caliper Base
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Supporting data and code for: Regional variation in active bottom contacting gear footprints.
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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.
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.