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9,302 results for “DETECT”
Raw data from "Next2You: Robust Copresence Detection Based on Channel State Information"
<p>This deposit contains the raw channel state information (CSI) data from the paper "Next2You: Robust Copresence Detection Based on Channel State Information" by Mikhail Fomichev, Luis F. Abanto-Leon, Max Stiegler, Alejandro Molina, Jakob Link, and Matthias Hollick in ACM Transactions on Internet of Things, Volume 3, Issue 2. 2022. See the <a href="https://doi.org/10.5281/zenodo.5105815">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Index of Supplementary Files from "Next2You: Robust Copresence Detection Based on Channel State Information"
<p>This record serves as an index to the other dataset releases that are part of the paper "Next2You: Robust Copresence Detection Based on Channel State Information" by Mikhail Fomichev, Luis F. Abanto-Leon, Max Stiegler, Alejandro Molina, Jakob Link, and Matthias Hollick in ACM Transactions on Internet of Things, Volume 3, Issue 2. 2022. </p> <p>We have chosen to split the dataset into several parts to meet Zenodo size requirements and make it easier to find specific pieces of data. In total, the following datasets exist:</p> <ol> <li> <p><strong>Raw data</strong>: <a href="https://doi.org/10.5281/zenodo.5592335">This dataset</a> contains raw channel state information (CSI) data in terms of CSI magnitude and phase values. The data is collected in the following environments: <em>office</em>, <em>urban apartment</em>, <em>rural house</em>, <em>moving</em> and <em>parked</em> cars, as well as additional setups in these environments such as <em>heterogeneous</em> devices (i.e., Nexus 6P and Raspberry Pi), different <em>frame types</em> to extract CSI data from (i.e., beacon), and varying transmission <em>power</em> of devices sending frames from which CSI is extracted. The data collection in additional setups was performed in the office environment.</p> <p>These raw CSI data can be used to repeat our own experiment or to develop new context-based copresence detection schemes. To collect the raw CSI data, we used the following <a href="https://github.com/seemoo-lab/next2you/tree/main/Data-collection/Nexus%205%20and%206P">Android app</a> and <a href="https://github.com/seemoo-lab/next2you/tree/main/Data-collection/Raspberry%20Pi">scripts on the Raspberry Pi 3 Model B+</a> (both include <a href="https://github.com/seemoo-lab/nexmon_csi">Nexmon</a> patches to enable CSI extraction).</p> </li> <li> <p><strong>Results Data</strong>: <a href="https://doi.org/10.5281/zenodo.5592823">The results dataset</a> contains the evaluation results (e.g., computed error rates and AUCs for different cases, Right for the Right Reasons trained models, etc.). The codebase to generate these results can be found in the <a href="https://github.com/seemoo-lab/next2you/tree/main/Scheme">source code repository</a>.</p> </li> </ol>
Data from: Detection of the elusive dwarf sperm whale (Kogia sima) using environmental DNA at Malpelo island (Eastern Pacific, Colombia)
<ol> <li>Monitoring large marine mammals is challenging due to their low abundances in general, an ability to move over large distances and wide geographical range sizes. </li> <li>The distribution of the pygmy (<i>Kogia breviceps</i>) and dwarf (<i>Kogia sima</i>) sperm whales is informed by relatively rare sightings, which does not permit accurate estimates of their distribution ranges. Hence, their conservation status has long remained Data Deficient (DD) in the Red list of the International Union for Conservation of Nature (IUCN), which prevent appropriate conservation measures.</li> <li>Environmental DNA (eDNA) metabarcoding uses DNA traces left by organisms in their environments to detect the presence of targeted taxon, and is here proved to be useful to increase our knowledge on the distribution of rare but emblematic megafauna.</li> <li>Retrieving eDNA from filtered surface water provides the first detection of the Dwarf sperm whale (<i>Kogia sima</i>) around the remote Malpelo island (Colombia).</li> <li>Environmental DNA collected during oceanic missions can generate better knowledge on rare but emblematic animals even in regions that are generally well sampled for other taxa.</li> </ol>
Sample site coordinates, environmental data, number of copies of target DNA/ul for each sample and limit of detection plot
<p>Human activities in coastal areas are accelerating ecosystem changes at an unprecedented pace, resulting in habitat loss, hydrological modifications, and predatory species declines. Understanding how these changes potentially cascade across marine and freshwater ecosystems requires knowing how mobile euryhaline species link these seemingly-disparate systems. As upper trophic level predators, bull sharks (<i>Carcharhinus leucas</i>) play a crucial role in marine and freshwater ecosystem health. Telemetry studies in Mobile Bay, Alabama suggest that bull sharks extensively use the northern portions of the bay, an estuarine-freshwater interface known as the Mobile-Tensaw Delta. To assess whether bull sharks use freshwater habitats in this region, environmental DNA surveys were conducted during the dry summer and wet winter seasons in 2018. In each season, 5 x<span> 1</span> L water samples were collected at each of 21 sites: five sites in Mobile Bay, six sites in the Mobile-Tensaw Delta, and ten sites throughout the Mobile-Tombigbee and Tensaw-Alabama Rivers. Water samples were vacuum-filtered, DNA extractions were performed on the particulate, and DNA extracts were analyzed with Droplet Digital™ Polymerase Chain Reaction using species-specific primers and an internal probe to amplify a 237-base pair fragment of the mitochondrial NADH dehydrogenase subunit 2 gene in bull sharks. One water sample collected during the summer in the Alabama River met the criteria for a positive detection, thereby confirming the presence of bull shark DNA. While preliminary, this finding suggests that bull sharks use less urbanized, riverine habitats up to 120 km upriver during Alabama's dry summer season.</p>
Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders: BNS Dataset
<p>Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders: BNS Dataset</p>
Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders: BBH Dataset
<p>Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders: BBH Dataset</p>
Supplementary material 1 from: Zangaro F, Saccomanno B, Tzafesta E, Bozzeda F, Specchia V, Pinna M (2021) Current limitations and future prospects of detection and biomonitoring of NIS in the Mediterranean Sea through environmental DNA. NeoBiota 70: 151-165. https://doi.org/10.3897/neobiota.70.71862
Table S1
Data from: The sound of recovery: coral reef restoration success is detectable in the soundscape
<p><span>1. Pantropical degradation of coral reefs is prompting considerable investment in their active restoration. However, current measures of restoration success are based largely on coral cover, which does not fully reflect ecosystem function or reef health. </span></p> <p><span>2. Soundscapes are an important aspect of reef health; loud and diverse soundscapes guide the recruitment of reef organisms, but this process is compromised when degradation denudes soundscapes. As such, acoustic recovery is a functionally important component of ecosystem recovery. </span></p> <p><span>3. Here, we use acoustic recordings taken at one of the world's largest coral reef restoration projects to test whether successful restoration of benthic and fish communities is accompanied by a restored soundscape. We analyse recordings taken simultaneously on healthy, degraded (extensive historic blast fishing) and restored reefs (restoration carried out for 1–3 years on previously-degraded reefs). We compare soundscapes using manual counts of biotic sounds (phonic richness), and two commonly used computational analyses (acoustic complexity index [ACI] and sound-pressure level [SPL]).</span></p> <p><span>4. Healthy and restored reef soundscapes exhibited a similar diversity of biotic sounds (phonic richness), which was significantly higher than degraded reef soundscapes. This pattern was replicated in some automated analyses but not others; the ACI exhibited the same qualitative result as phonic richness in a low-frequency, but not a high-frequency bandwidth, and there was no significant difference between SPL values in either frequency bandwidth. Further, the low-frequency ACI and phonic richness scores were only weakly correlated despite showing a qualitatively equivalent overall result, suggesting that these metrics are likely to be driven by different aspects of the reef soundscape. </span></p> <p><span>5<i>. Synthesis and applications: </i>These data show that coral restoration can lead to soundscape recovery, demonstrating the return of an important ecosystem function. They also suggest that passive acoustic monitoring (PAM) might provide functionally important measures of ecosystem-level recovery – but only some PAM metrics reflect ecological status, and those that did are likely to be driven by different communities of soniferous animals. Recording soundscapes represents a potentially valuable tool for evaluating restoration success across ecosystems, but caution must be exercised when choosing metrics and interpreting results. </span></p>
Supplementary material 1 from: Leite BR, Vieira PE, Troncoso JS, Costa FO (2021) Comparing species detection success between molecular markers in DNA metabarcoding of coastal macroinvertebrates. Metabarcoding and Metagenomics 5: e70063. https://doi.org/10.3897/mbmg.5.70063
Table S1, Figures S1–S5
Supplementary material 2 from: Leite BR, Vieira PE, Troncoso JS, Costa FO (2021) Comparing species detection success between molecular markers in DNA metabarcoding of coastal macroinvertebrates. Metabarcoding and Metagenomics 5: e70063. https://doi.org/10.3897/mbmg.5.70063
Tables S2, S3
Supplementary material 2 from: Van den Bulcke L, De Backer A, Ampe B, Maes S, Wittoeck J, Waegeman W, Hostens K, Derycke S (2021) Towards harmonization of DNA metabarcoding for monitoring marine macrobenthos: the effect of technical replicates and pooled DNA extractions on species detection. Metabarcoding and Metagenomics 5: e71107. https://doi.org/10.3897/mbmg.5.71107
Tables S1–S8
Supplementary material 1 from: Van den Bulcke L, De Backer A, Ampe B, Maes S, Wittoeck J, Waegeman W, Hostens K, Derycke S (2021) Towards harmonization of DNA metabarcoding for monitoring marine macrobenthos: the effect of technical replicates and pooled DNA extractions on species detection. Metabarcoding and Metagenomics 5: e71107. https://doi.org/10.3897/mbmg.5.71107
Figures S1–S14
Dataset of the Floating Objects Detection Notebook
<p>This dataset contains the data used in the notebook "Detecting floating objects using Deep Learning and Sentinel-2 imagery", published in the ocean modelling section of The Environmental Data Science Book.</p>
Floodwater Detection Challenge Training Data
<p>NA</p>
Mixed-species flocking is associated with low arthropod detectability and increased foraging efficiency by Yungas forest birds in Argentina
<p class="CuerpoB"><span>Mixed-species flocks presumably provide birds with antipredator and foraging benefits. The foraging benefits hypothesis predicts that a reduction in arthropod abundance will trigger flocking activity; however, flocking activity may also be influenced by the difficulty of detecting arthropods, a seldom explored possibility. We found that environmental traits (temperature and foliage density) combined with arthropod abundance explained arthropod detection by birds in the Yungas foothill forest of NW Argentina. Prey detection was inversely related to ambient temperature and foliage density while positively associated with arthropod abundance. Based on this result, we built a </span><span>structural equation model </span><span>using a latent proxy variable for arthropod detectability, arthropod crypsis, integrating ambient temperature, foliage density, and proportion of immature arthropods. This model allowed us to compare the relative importance of arthropod abundance and the difficulty in detecting prey items as predictors of flocking propensity. After two years of studying 129 mixed-species flocks, 1,344 bird foraging sequences, and 25,591 arthropod captures,</span> <span>we found that the flocking propensity of birds was only significantly correlated with arthropod detectability and not with arthropod abundance. </span>Flocking propensity peaked when the arthropod community was comprised of proportionately more immature and non-flying arthropods, the temperature was low, and the foliage cover was more dense; all factors are contributing to a low arthropod detectability. <span>Finally, we evaluated whether joining mixed-species flocks provided foraging benefits such as increased foraging efficiency. Individuals benefited from joining flocks by an average increase of their prey-capture attempt rate of 40%, while the search rate increased by 16%. Our results add a new perspective on the drivers of mixed-species flocking by showing that the capacity to find prey items may have a more significant effect than prey abundance per se.</span></p>
Figure 6 from: Csősz S, Kiran K, Karaman C, Lapeva-Gjonova A (2022) A striking color variation is detected in Ponera testacea Emery, 1895 (Hymenoptera, Formicidae) across its Western Palaearctic geographic range. ZooKeys 1084: 151-164. https://doi.org/10.3897/zookeys.1084.79415
Figure 6 The best morphometric ratio (petiole length / petiole height; PEL/PH) is illustrated on the head length (CL). Scatterplots of the most discriminating ratio on the head length between workers of Western Palaearctic representatives of Ponera; P. coarctata: black circles, P. testacea: red triangles. The thin dashed line illustrates best separation.
Figure 7 from: Csősz S, Kiran K, Karaman C, Lapeva-Gjonova A (2022) A striking color variation is detected in Ponera testacea Emery, 1895 (Hymenoptera, Formicidae) across its Western Palaearctic geographic range. ZooKeys 1084: 151-164. https://doi.org/10.3897/zookeys.1084.79415
Figure 7 Geographic map of Ponera species in Europe and Turkey. Color codes for species are as follows: Ponera coarctata: red circles, P. testacea "black morphs": blue triangles, P. testacea "yellow morphs": yellow rectangles.
Figure 3 from: Csősz S, Kiran K, Karaman C, Lapeva-Gjonova A (2022) A striking color variation is detected in Ponera testacea Emery, 1895 (Hymenoptera, Formicidae) across its Western Palaearctic geographic range. ZooKeys 1084: 151-164. https://doi.org/10.3897/zookeys.1084.79415
Figure 3 Dendrogram solution for the Western Palaearctic representatives of Ponera. Sample information in the dendrogram follows this format: abbreviated country code, locality name, and/or a special collection code followed by final species hypothesis separated by underscore. Two columns of rectangles represent results of partitioning resulted by method PART using two cluster methods 'hclust' and 'kmeans'.
Figure 4 from: Csősz S, Kiran K, Karaman C, Lapeva-Gjonova A (2022) A striking color variation is detected in Ponera testacea Emery, 1895 (Hymenoptera, Formicidae) across its Western Palaearctic geographic range. ZooKeys 1084: 151-164. https://doi.org/10.3897/zookeys.1084.79415
Figure 4 Gap statistic for dataset of Western Palaearctic Ponera samples. Two-cluster solution is highly supported by the elbow at 2 components by the dispersion curve (left) and by the peak at cluster number four by the gap curve (right). Number of clusters in the data (X axis), the total within-cluster dispersion for each evaluated partition (Y axis for the left plot) and the vector of length Kmax giving the Gap statistic for each evaluated partition (Y axis for the right plot) is illustrated.
Figure 1 from: Csősz S, Kiran K, Karaman C, Lapeva-Gjonova A (2022) A striking color variation is detected in Ponera testacea Emery, 1895 (Hymenoptera, Formicidae) across its Western Palaearctic geographic range. ZooKeys 1084: 151-164. https://doi.org/10.3897/zookeys.1084.79415
Figure 1 Light-colored (score 2) Ponera testacea worker from Hungary. Specimen: CASENT0906719, from www.antweb.org.
ScienceDex guides
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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.