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338 results for “gps”
Comparison of cytosine base editors and development of the BEable-GPS database for targeting pathogenic SNVs
GEO Series GSE136749. Homo sapiens. 71 samples. Type: Other.
Figure 5 from: Milotic T, Desmet P, Anselin A, De Bruyn L, De Regge N, Janssens K, Klaassen R, Koks B, Schaub T, Schlaich A, Spanoghe G, T'jollyn F, Vanoverbeke J, Bouten W (2020) GPS tracking data of Western marsh harriers breeding in Belgium and the Netherlands. ZooKeys 947: 143-155. https://doi.org/10.3897/zookeys.947.52570
Figure 5 Number of acceleration records per year and per dataset.
Figure 4 from: Milotic T, Desmet P, Anselin A, De Bruyn L, De Regge N, Janssens K, Klaassen R, Koks B, Schaub T, Schlaich A, Spanoghe G, T'jollyn F, Vanoverbeke J, Bouten W (2020) GPS tracking data of Western marsh harriers breeding in Belgium and the Netherlands. ZooKeys 947: 143-155. https://doi.org/10.3897/zookeys.947.52570
Figure 4 Number of GPS fixes per year and per dataset.
Figure 3 from: Milotic T, Desmet P, Anselin A, De Bruyn L, De Regge N, Janssens K, Klaassen R, Koks B, Schaub T, Schlaich A, Spanoghe G, T'jollyn F, Vanoverbeke J, Bouten W (2020) GPS tracking data of Western marsh harriers breeding in Belgium and the Netherlands. ZooKeys 947: 143-155. https://doi.org/10.3897/zookeys.947.52570
Figure 3 Tracking effort: number of observations per day and per individual.
Data from: Puma energetics: laboratory oxygen consumption and GPS information from free-ranging individuals
<span><b><u>Abstract</u></b></span> <p class="Default"><b>Background</b></p> <p class="Default">Under current scenarios of climate change and habitat loss, many wild animals, especially large predators, are moving into novel energetically challenging environments. Consequently, changes in terrain associated with such moves may heighten energetic costs and effect the decline of populations in new localities.</p> <p class="Default"><b>Methods</b></p> <p class="Default">To examine locomotor costs of a carnivorous mammal moving in mountainous habitats, the oxygen consumption of captive pumas (<i>Puma concolor</i>) was measured during treadmill locomotion on level and incline (6.8ᵒ) surfaces. These data were used to predict energetic costs of locomotor behaviours of free-ranging pumas equipped with GPS/accelerometer collars in California's Santa Cruz Mountains.</p> <p class="Default"><b>Results</b></p> <p class="Default">Incline walking resulted in a 42.0%±7.2 SEM increase in transport costs compared to level performance. Wild pumas modified their locomotor behaviour in response to terrain steepness by traversing the mean hillside incline of 17.2ᵒ±0.3 SEM and choosing shallower paths of 7.3ᵒ±0.1 SEM. Pumas also walked more slowly up steeper paths, thereby minimizing the energetic impact of vertical terrains. Estimated daily energy expenditure of (DEE) based on GPS-derived speeds of free-ranging pumas was 18.3 MJ day<sup>-1</sup> ±0.2 SEM. Calculations show that a 20ᵒ increase in mean terrain steepness would increase DEE by <1% as pumas spent a small (10%) proportion of their day travelling and avoided elevated costs by utilizing slower speeds and shallower paths.</p> <p class="Default"><b>Conclusions</b></p> <p class="Default">While many factors influence survival in novel habitats, we illustrate the importance of behaviours which reduce locomotor costs when traversing new, energetically challenging environments, and demonstrate that these behaviours are utilised by pumas in the wild.</p> <p> </p> <p> </p>
Differences in GPS variables according to playing formations and playing positions in U19 male soccer players
<p>The aims of this study were 1) to investigate Global Positioning System (GPS)-based match physical performance according to players’ playing position in three different playing formations (4-4-2, 3-5-2, 4-3-3) and 2) to analyse the differences in match performance between 1st and 2nd half. Twenty-three U19 elite male soccer players (age: 18 ± 1 year, height: 1.80 ± 0.04 m, body mass: 70.65 ± 6.02 kg), categorized as Central Back (CB, n = 5), Full Back (FB, n = 4), Central Midfielders (CM, n = 4), Wingers (W, n = 3), Strikers (S, n = 7), were monitored using 10 Hz GPS during 31 competitive matches. The results showed that FB and W always had the highest very high-speed running distance and number of sprints in all playing formations. Significant decrease in all GPS variables was observed in the 2nd half of the match for all playing positions. Strength coaches should adopt specific training regimes in accordance with players’ playing position.</p>
Data from: Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds
1.In order to prevent further global declines in biodiversity, identifying and understanding key habitats is crucial for successful conservation strategies. For example, globally, seabird populations are under threat and animal movement data can identify key at-sea areas and provide valuable information on the state of marine ecosystems. To date, in order to locate these areas, studies have used Global Positioning System (GPS) to record position and are sometimes combined with Time Depth Recorder (TDR) devices to identify diving activity associated with foraging, a crucial aspect of at-sea behaviour. However, the use of additional devices such as TDRs can be expensive, logistically difficult, and may adversely affect the animal. Alternatively, behaviours may be resolved from measurements derived from the movement data alone. However, this behavioural analysis frequently lacks validation data for locations predicted as foraging (or other behaviours). 2.Here, we address these issues using a combined GPS and TDR dataset from 108 individuals by training deep learning models to predict diving in European shags, common guillemots and razorbills. We validate our predictions using withheld data, producing quantitative assessment of predictive accuracy. The variables used to train these models are those recorded solely by the GPS device: variation in longitude and latitude, altitude, and coverage ratio (proportion of possible fixes acquired within a set window of time). 3.Different combinations of these variables were used to explore the qualities of different models, with the optimum models for all species predicting non-diving and diving behaviour correctly over 94% and 80% of the time, respectively. We also demonstrate the superior predictive ability of these supervised deep-learning models over other commonly used behavioural prediction methods such as hidden Markov models. 4.Mapping these predictions provides useful insights into the foraging activity of a range of seabird species, highlighting important at sea locations. These models have the potential to be used to analyse historic GPS datasets and further our understanding of how environmental changes have affected these seabirds over time.
Elemental Concentrations in Mushroom Samples and Soils from the Bailing Cu-Zn Deposit Area, A'cheng District, Harbin, China (with GPS Coordinates for Specific Samples)
<p>The dataset includes three tables: </p> <p>Table 1. Portable X-ray fluorescence (pXRF) measured and inductively coupled plasma (ICP) determined elemental concentrations for 40 mushroom samples from China.</p> <p>Table 2. Portable X-ray fluorescence (pXRF) measured and inductively coupled plasma (ICP) determined elemental concentrations for 20 mushroom samples grown in the Bailing Cu-Zn deposit area, A’cheng District, Harbin, China.</p> <p>Table 3. Portable X-ray fluorescence (pXRF) determined As concentrations in soils and As concentrations in Lepista nuda (wood blewit) grown in the Bailing Cu-Zn deposit area, Harbin City, China.</p>
Trayectorias GPS de aves marinas en las islas del Pacífico y del Golfo de California del 6 de mayo al 8 de julio de 2016
<p>Datos de seguimiento remoto mediante GPS en el periodo del 6 de mayo al 8 de julio de 2016.</p> <ul> <li>Bobo patas rojas en Isla Clarión</li> <li>Gaviota patas amarillas en Isla Partida, bahía de los Ángeles, golfo de California</li> <li>Pardela mexicana en Isla Guadalupe</li> <li>Pardela mexicana en Isla Natividad</li> <li>Pardela mexicana en Isla San Benito (<a href="../api/records/11583544/draft/files/12Y_GPS38_290516.csv/content" target="_blank" rel="noopener noreferrer">12Y_GPS38_290516.csv</a>)</li> </ul> <p> </p> <p>Recibimos estos datos para su procesamiento entre el 2 de junio y el 21 de julio de 2016.</p>
Inventario interno de datos de seguimiento remoto (GPS y GLS) de aves marinas en las islas mexicanas de los años 2014 al 2025
<p>Las islas:</p> <ul> <li>Asunción</li> <li>Clarión</li> <li>Guadalupe</li> <li>Muertos</li> <li>Natividad</li> <li>Partida - Bahía de los Ángeles</li> <li>San Benedicto</li> <li>San Benito</li> <li>San Roque</li> <li>Socorro</li> </ul> <p>Las especies:</p> <ul> <li>Albatros de Laysan</li> <li>Albatros patas negras</li> <li>Alcuela oscura</li> <li>Bobo enmascarado</li> <li>Bobo patas rojas</li> <li>Gaviota</li> <li>Mérgulo de Guadalupe</li> <li>Pardela de Revillagigedo</li> <li>Pardela mexicana</li> <li>Petrel de Ainley</li> <li>Petrel de Leach</li> <li>Petrel negro</li> </ul>
Figure 1 from: Dantas GPS, Hamada N, Mendes HF (2016) Denopelopia amicitia, a new Tanypodinae from Brazil (Diptera, Chironomidae). ZooKeys 553: 107-117. https://doi.org/10.3897/zookeys.553.5988
Figure 1 - Denopelopia amicitia sp. n. Adult male: A head B thorax C wing.
Figure 4 from: Stienen EWM, Desmet P, Aelterman B, Courtens W, Feys S, Vanermen N, Verstraete H, Van de walle M, Deneudt K, Hernandez F, Houthoofdt R, Vanhoorne B, Bouten W, Buijs RJ, Kavelaars MM, Müller W, Herman D, Matheve H, Sotillo A, Lens L (2016) GPS tracking data of Lesser Black-backed Gulls and Herring Gulls breeding at the southern North Sea coast. ZooKeys 555: 115-124. https://doi.org/10.3897/zookeys.555.6173
Figure 4 - Number of birds grouped by number of tracking days and tracking start year.
GPS data of CMONOC (2)
<p>GPS data of CMONOC, which were used in the studying of vertical land motion throughout the Tianshan and forelands.</p>
The Most Demanding Match Periods: Should GPS Data be Normalized
ClinicalTrials.gov study NCT07321496. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
MRI And GPS Informing Choices for Prostate Cancer Treatment (MAGIC)
ClinicalTrials.gov study NCT05424783. IPD Sharing: YES. Countries: 1. Publications: 0.
Geolocation Positional System (GPS) Experience
ClinicalTrials.gov study NCT05991713. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of the NantHealth GPS Cancer Test in Patients With Advanced Cancers
ClinicalTrials.gov study NCT03073473. IPD Sharing: NO. Countries: 1. Publications: 0.
Effectiveness of Anti-Psychotic in GPs Setting
ClinicalTrials.gov study NCT00543088. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Comparison Between Exactech Guided Personalized Surgery (GPS) and Conventional Instrumentation of Shoulder Arthroplasty
ClinicalTrials.gov study NCT05615259. IPD Sharing: Not stated. Countries: 1. Publications: 0.
GPS: Adaptation Trial of an HIV Prevention Counselling Program for HIV-positive and HIV-negative Gay and Bisexual Men
ClinicalTrials.gov study NCT03186183. IPD Sharing: NO. Countries: 1. Publications: 0.
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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.