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148 results for “Telemetry”
A Bayesian Machine Learning Framework for Animal Telemetry Data
<p>The data and tutorial in this repository are intended to be used in conjunction with the tutorial with our manuscript titled "A Bayesian Machine Learning Framework for Animal Telemetry Data." Telemetry data for three lesser prairie-chickens are provided here as .csv files. For more information about the data, please refer to our manuscript or contact Andrew Whetten or David Haukos for more information.</p>
OPSSAT-AD - anomaly detection dataset for satellite telemetry
<p>This is the AI-ready benchmark dataset (OPSSAT-AD) containing the telemetry data acquired on board OPS-SAT---a CubeSat mission that has been operated by the European Space Agency.</p> <p>It is accompanied by the paper with baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms for anomaly detection. They were trained and validated using the training-test dataset split introduced in this work, and we present a suggested set of quality metrics that should always be calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. We believe that this work may become an important step toward building a fair, reproducible, and objective validation procedure that can be used to quantify the capabilities of the emerging anomaly detection techniques in an unbiased and fully transparent way.</p> <p>The included files are:</p> <ul> <li><code>segments.csv</code> with the acquired telemetry signals from ESA OPS-SAT aircraft,</li> <li><code>dataset.csv</code> with the extracted, synthetic features are computed for each manually split and labeled telemetry segment.</li> <li>code files for data processing and example modeliing (<code>dataset_generator.ipynb</code> for data processing, <code>modeling_examples.ipynb</code> with simple examples, <code>requirements.txt</code>- with details on Python configuration, and the <code>LICENSE</code> file)</li> </ul> <p> </p> <p>Please have a look at our two papers commenting on this dataset:</p> <ul> <li>The benchmark paper with results of 30 supervised and unsupervised anomaly detection models for this collection:<br>Ruszczak, B., Kotowski. K., Nalepa, J., Evans, D.:<strong> The OPS-SAT benchmark for detecting anomalies in satellite telemetry, 2024</strong>, <a href="https://arxiv.org/abs/2407.04730" target="_blank" rel="noopener">preprint arxiv: 2407.04730</a>,</li> <li>the conference paper in which we presented some preliminary results for this dataset:<br>Ruszczak, B., Kotowski. K., Andrzejewski, J., et al.: (2023). Machine Learning Detects Anomalies in OPS-SAT Telemetry. Computational Science – ICCS 2023. LNCS, vol 14073. Springer, Cham, <a href="https://doi.org/10.1007/978-3-031-35995-8_21">DOI:10.1007/978-3-031-35995-8_21</a>.</li> </ul>
Accepted Artifact for Privacy-Respecting Type Error Telemetry at Scale
<p>This artifact packages the data for the paper: <em>Privacy-Respecting Type Error Telemetry at Scale</em></p> <p>There are two files on Zenodo:</p> <ul> <li>data.tar.gz has the original Luau telemetry data</li> <li>artifact.tar.gz has a result PDF, intermediate data, and scripts for processing the data</li> </ul> <p>The artifact code and the source for the paper are also on GitHub:</p> <ul> <li><a href="https://github.com/bennn/luau-telemetry">https://github.com/bennn/luau-telemetry</a></li> </ul> <p>This artifact is primarily a **dataset**. It shows how we reached the conclusions in the paper.</p> <p>The scripts in this artifact are provided as-is for completeness. They may have bugs. They may not work as advertised.</p>
Interior Delta Effects Telemetry Data
<p>This is the telemetry data for an acoustic tagging study that investigated the effect of water management actions on fish survival in the Interior Sacramento Delta, California, USA.</p> <p> </p> <p> </p>
Data from: "Using low-fix rate GPS telemetry to expand estimates of ungulate reproductive success"
<p>Secondary datasets used for analysis in "Using low-fix rate GPS telemetry to expand estimates of ungulate reproductive success". Raw GPS relocation data are not publicly available due to potential ethical implications but are available from the corresponding author (Nathan Hooven, nathan.d.hooven@gmail.com) upon reasonable request. Datasets include:</p> <p>elk_days_part.csv: generated movement metrics and days from parturition for all cow elk for which reproductive success was confirmed</p> <p>elk_np.csv: generated movement metrics for non-parturient cow elk</p> <p>elk_unknowns.csv: generated movement metrics for cows with unknown reproductive status, but were confirmed pregnant in mid-winter</p> <p>elk_thisyear.csv: generated movement metrics for 2020 Vectronic cows monitored in 2021</p> <p>Part_dates.csv: Confirmed and predicted dates of parturition for all elk in training and testing sets</p> <p>all_prob_summary_parturient.csv: Confirmed and predicted dates of parturition and differences for confirmed successful elk</p> <p>Decision rules 1.xlsx: Spreadsheet with classification accuracy based upon varying decision rules</p> <p>Decision rules 2.csv: Plottable summary of classification accuracy based upon varying decision rules</p> <p> </p>
EDEN ISS 2020 Telemetry Dataset
<h2>Overview</h2> <p>The EDEN ISS 2020 Telemetry Dataset (edeniss2020) is a time series dataset. It consists of equidistant sensor readings stemming from 97 sensors in the [EDEN ISS](https://eden-iss.net/) research greenhouse.</p> <p>EDEN ISS was a (almost) closed loop research greenhouse build under the lead of the German Aerospace Center to study Controlled Environment Agriculture (CEA) techniques and plant growth for future long-term space missions. EDEN ISS was deployed in Antarctica in 2018 next to the german Neumayer III polar station and has been in operation for four years. </p> <p>The data contained in the edeniss2020 dataset was recorded during the third mission year in 2020 between 2020/01/01 and 2020/12/30. Every sensor within the dataset is related to one of the following Subsystems</p> <p> </p> <table> <tbody> <tr> <td><strong>Acronym</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>AMS‑FEG</td> <td>Atmosphere Management System (AMS) of the Future Exploration Greenhouse (FEG)</td> </tr> <tr> <td>AMS‑SES</td> <td>Atmosphere Management System AMS of the Service Section (SES)</td> </tr> <tr> <td>ICS</td> <td>Illumination Control System</td> </tr> <tr> <td>NDS</td> <td>Nutrient Delivery System</td> </tr> <tr> <td>TCS</td> <td>Thermal Control System</td> </tr> </tbody> </table> <p> </p> <h2>Specification</h2> <table> <tbody> <tr> <td><strong>Item</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Number of Files</td> <td>97</td> </tr> <tr> <td>Start Date</td> <td>2020/01/01 00:00:05</td> </tr> <tr> <td>End Date</td> <td>2020/12/30 23:55:00</td> </tr> <tr> <td>Sampling Rate</td> <td>1/300 Hz (5min)</td> </tr> <tr> <td>Datapoints (per file)</td> <td>105119</td> </tr> </tbody> </table> <p> </p> <h2>Contents</h2> <p>The dataset includes the following files:</p> <div> <ul> <li><code>ams-feg/*.csv</code>: Sensor readings related to the AMS-FEG</li> <li><code>ams-ses/*.csv</code>: Sensor readings related to the AMS-SES</li> <li><code>ics/*.csv</code>: Temperature readings related to the ICS</li> <li><code>nds/*.csv</code>: Sensor readings related to the NDS</li> <li><code>tcs/*.csv</code>: Sensor readings related to the</li> <li><code>edeniss2020.csv</code>: Description and Units of the measurements.</li> <li><code>README.md</code>: This file.</li> </ul> <table> <tbody> <tr> <th>Subsystem</th> <th>Sensor</th> <th>#sensors</th> <th>Note</th> </tr> </tbody> <tbody> <tr> <td>AMS-FEG</td> <td>CO2</td> <td>2</td> <td> </td> </tr> <tr> <td> </td> <td>Photosynthetic Active Radiation (PAR)</td> <td>2</td> <td> </td> </tr> <tr> <td> </td> <td>Relative Humidity (RH)</td> <td>2</td> <td> </td> </tr> <tr> <td> </td> <td>Temperature (T)</td> <td>3</td> <td> </td> </tr> <tr> <td>AMS-SES</td> <td>CO2</td> <td>2</td> <td> </td> </tr> <tr> <td> </td> <td>Photosynthetic Active Radiation (PAR)</td> <td>1</td> <td> </td> </tr> <tr> <td> </td> <td>Relative Humidity (RH)</td> <td>1</td> <td> </td> </tr> <tr> <td> </td> <td>Temperature (T)</td> <td>3</td> <td> </td> </tr> <tr> <td> </td> <td>Vapor Pressure Deficit (VPD)</td> <td>1</td> <td> </td> </tr> <tr> <td>ICS</td> <td>Temperature (T)</td> <td>38</td> <td>Measured at the LED lamp above each growth tray</td> </tr> <tr> <td>NDS</td> <td>Electrical Conductivity (EC)</td> <td>4</td> <td>EC of the nutrient solutions</td> </tr> <tr> <td> </td> <td>Level (H)</td> <td>2</td> <td>Level of the solution in the nutrient solution tanks</td> </tr> <tr> <td> </td> <td>PH-Value (PH)</td> <td>4</td> <td>PH Value of the nutrient solutions</td> </tr> <tr> <td> </td> <td>Pressure (P)</td> <td>8</td> <td>Pressure in the piping from the tanks to the growth racks in the FEG</td> </tr> <tr> <td> </td> <td>Temperature (T)</td> <td>4</td> <td>Temperature of the nutrient solutions.</td> </tr> <tr> <td> </td> <td>Volume (V)</td> <td>2</td> <td>Volume up to the level sensor</td> </tr> <tr> <td>TCS</td> <td>Pressure (P)</td> <td>3</td> <td> </td> </tr> <tr> <td> </td> <td>Relative Humidity (RH)</td> <td>2</td> <td> </td> </tr> <tr> <td> </td> <td>Temperature (T)</td> <td>12</td> <td> </td> </tr> <tr> <td> </td> <td>Valve (VALVE)</td> <td>3</td> <td> </td> </tr> </tbody> </table> </div> <p> </p> <h2>Usage</h2> <p>For usage information please refer to the README.md file.</p> <p> </p> <p> </p>
Proof-of-concept AO telemetry data using the AOT standard format
<p>Dataset containing a demonstration of AO telemetry data using the AOT standard format. Contains data from 5 different systems (CIAO, ERIS, GALACSI, NAOMI and PAPYRUS).</p> <p>The data is as follows:</p> <ul> <li>4 CIAO files "CIAO#_*.fits" where # indicates the AT where the data was produced.</li> <li>2 ERIS files, one in LGS (LTAO) mode and another in NGS (SCAO) mode.</li> <li>1 GALACSI file.</li> <li>4 NAOMI files "NAOMI#_*.fits" where # indicates the AT where the data was produced.</li> <li>2 PAPYRUS files, one using a Shack-Hartmann wavefront sensor (SHWFS) and another using a Pyramid wavefront sensor (PWFS).</li> </ul> <p>GALACSI, CIAO, NAOMI and ERIS data were gathered under ESO's program IDs 60.A-9278(B), 60.A-9278(C), 60.A-9278(D) and 60.A-9278(E) respectively. The data was translated via the functions provided in the Python package <em>aotpy</em> (<a href="https://doi.org/10.5281/zenodo.8187230">10.5281/zenodo.8187230</a>).</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101004719 (OPTICON–RadioNet Pilot).</p>
Telemetry mark-recapture data of the Northern Spring Salamander (Gyrinophilus porphyriticus), Hubbard Brook Experimental Forest, 2019 – 2021
This data set includes spatially explicit mark-recapture data of the Northern Spring Salamander (Gyrinophilus porphyriticus) collected via telemetry during the summer months (June – September) from 2019 - 2021 from eight reaches in multiple streams in the Hubbard Brook Experimental Forest. Salamanders were captured by hand and marked with PIT-tags. Telemetry surveys occurred weekly. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. These data are being used to publish the following papers: Cochrane, M. M., B. R. Addis, L. K. Swartz, and W. H. Lowe. 2023. Individual and population growth rates decline with watershed area in a stream salamander. In review Ecology. Cochrane, M. M., and W. H. Lowe. 2023. Floods increase downstream movement of adult and larval life stages of a headwater stream salamander. In prep Freshwater Biology.
Counts of tagged striped bass at forty sites throughout Plum Island estuary conducted July-October 2009 using acoustic telemetry.
Manual survey data was collected to measure striped bass distribution in Plum Island Estuary during the time period that they are in New England during their summer foraging migration. Acoustic telemetry was used to tag and track individual fish and provide measures of abundance at sample sites distributed throughout the estuary.
1kuns-pf telemetry
<p>This is telemetry data from 1kuns-pf satellite. The origin of this data is SIDS endpoint in <a href="https://db.satnogs.org/satellite/43466#data">satnogs</a>. Data parsed by <a href="https://github.com/dernasherbrezon/jradio">jradio</a>.</p>
Dataset for Evaluating habitat-specific interference in automated radio telemetry systems: implications for animal movement studies
<h1>Abstract </h1> <p>Automated radio telemetry systems have become a popular and invaluable tool in tracking the activity and movement of wild animals. However, many environmental conditions can hinder accuracy when tracking with this technology. For instance, study sites may contain multiple habitat types, each habitat uniquely affecting the signal strength received from tagged species. To investigate the influence of a structurally diverse study site on an automated radio telemetry system, we conducted this project at a restored and managed pine barren habitat that consisted of a mix of mature pitch pine, treated pitch pine, scrub oak, and hardwood forests. This site, Montague Plains Wildlife Management Area, Montague, Massachusetts, is also a known breeding ground for Eastern whip-poor-will (Antrostomus vociferus). To measure the relationship of radio signal strength with distance across each habitat, we used radio telemetry equipment manufactured by Cellular Tracking Technologies. We produced negative exponential decay functions measuring radio signal strength over distance and tested for differences among habitat types on radio signal strength (RSS). We found that decay function parameters significantly differed by habitat type, prompting us to investigate if accounting for these differences improved location estimate accuracy. To test this, we estimated known locations using trilateration methods with and without habitat calibration. Comparing these tests indicates that habitat-specific adjustments significantly improved location accuracy. Lastly, we visualized estimated RSS-based locations of one week of whip-poor-will data and compared them to GPS data generated from the same individual. Previous studies have accounted for types of environmental interference (like elevation) in the field but have avoided incorporating habitat-specific factors by working with node networks covering a relatively small area, but in this study, we examined the potential to scale up for larger areas and in more complex habitats.</p> <p> </p>
Landscape composition and life-history traits influence bat movement and space use: analysis of 30 years of published telemetry data
<p>Using temperate bats, a group of particular conservation concern, we investigated how morphological traits, habitat specialization and environmental variables affect home range sizes and daily foraging movements, using a compilation of 30 years of published bat telemetry data in Northern America and Europe for the period 1988 – 2016.</p> <p>We compiled data on home range size and mean daily distance between roosts and foraging areas at both colony and individual levels from 166 studies of 3,129 radiotracked individuals of 49 bat species. We calculated multi-scale habitat composition and configuration in the surrounding landscapes of all studied roosts. Using mixed models, we examined the effects of habitat availability and spatial arrangement on bat movements, while accounting for body mass, aspect ratio, wing loading and habitat specialization.</p> <p>We found a significant effect of landscape composition on home range size and mean daily distance at both colony and individual levels. On average, home ranges were up to 42% smaller in the most habitat-diversified landscapes while mean daily distances were up to 30% shorter in the most forested landscapes. Bat home range size significantly increased with body mass, wing aspect ratio and wing loading, and decreased with habitat specialization.</p>
Where did the finch go? Insights from radio telemetry of the medium ground finch (Geospiza fortis)
<p><span>Movement patterns and habitat selection of animals have important implications for ecology and evolution. Darwin's finches are a classic model system for ecological and evolutionary studies, yet their spatial ecology remains poorly studied. We tagged and radio-tracked five (three females, two males) medium ground finches (<em>Geospiza fortis</em>) to examine the feasibility of telemetry for understanding their movement and habitat use. Based on 143 locations collected during a three-week period, we analysed, for the first time, home-range size and habitat selection patterns of finches at El Garrapatero, an arid coastal ecosystem on Santa Cruz Island (Galápagos). The average 95% home range and 50% core area for <em>G. fortis</em> in the breeding season were 20.54 ha ± 4.04 ha SE and 4.03 ha ± 1.11 ha SE, respectively. For most of the finches, their home range covered a diverse set of habitats. Three finches positively selected the dry-forest habitat, while the other habitats seemed to be either negatively selected or simply neglected by the finches. In addition, we noted a communal roosting behaviour in an area close to the ocean, where the vegetation is greener and denser than the more inland dry-forest vegetation. We show that telemetry on Darwin's finches provides valuable data to understand the movement ecology of the species. Based on our results, we propose a series of questions about the ecology and evolution of Darwin's finches that can be addressed using telemetry.</span></p>
Dataset _ Bombus pauloensis telemetry: Spatio-temporal use of the environment and floral resources.
<p>These data includes the GPS points collected from the tracked bees (<em>Bombus pauloensis</em> queens) that were used in the analysis of the home ranges and kernal density, data were usesd in the the MCP and the LUC analysis and pollen collected from the queens.</p>
Fig. 4 in Preliminary study on the application of radio-telemetry techniques to evaluate movements of fish in the Lateral canal at Itaipu Dam, Brazil
Fig. 4. Movements of Prochilodus lineatus (fish no 1 and 4) and Pseudoplatystoma fasciatum (fish no 9, 10 and 13) in the lateral channel located near Itaipu Dam (detections in the fixed stations, in number of hours post-release, are shown in the white circles).
Fig. 3 in Preliminary study on the application of radio-telemetry techniques to evaluate movements of fish in the Lateral canal at Itaipu Dam, Brazil
Fig. 3. Radio-telemetry fixed station at "Lago das Grevilhas", in the lateral channel near Itaipu Dam.
Fig. 1 in Preliminary study on the application of radio-telemetry techniques to evaluate movements of fish in the Lateral canal at Itaipu Dam, Brazil
Fig. 1. Locations of the fixed-station receivers (numbers inside the white circles) during the pilot study conducted in the lateral channel located near Itaipu Dam.
Fig. 2 in Preliminary study on the application of radio-telemetry techniques to evaluate movements of fish in the Lateral canal at Itaipu Dam, Brazil
Fig. 2. Surgical implantation of a radiotransmitter in Pseudoplatystoma fasciatum, captured in the lateral channel located near Itaipu Dam.
Towards substitution of invasive telemetry: An integrated home cage concept for unobtrusive monitoring of objective physiological parameters in rodents - Minimal dataset
<p>Minimal dataset for unobtrusive monitoring of vital parameters in rodents.</p>
Tracking small animals in complex landscapes: a comparison of localisation workflows for automated radio telemetry systems
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