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1,487 results for “Tagging”

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zenodo40/100

Water tagging experiments for the interpretation of deuterium excess in Antarctica

These simulation results are from the water-tagging experiments in the manuscript "Sublimation origin of negative deuterium excess observed in snow and ice samples from McMurdo Dry Valleys and Allan Hills Blue Ice Areas, East Antarctica" submitted to the Journal of Geophysical Research-Atmospheres. The file tag_antactic_names.pdf shows the abbreviations of all tagging region names.

opencc-by-4.0Sep 2021View details →

zenodo40/100

LOLA. Flamenco tagged audios Dataset

This dataset holds ~1500 audio samples of 3 different flamenco styles. Those styles are: <ul> <li>Bulerías</li> <li>Alegrías</li> <li>Sevillanas</li> </ul> Each audio is in a folder with the same name as the style to which it belongs. Each audio has a duration between 10 and 15 seconds and is saved in mp3 format.

opencc-by-4.0Oct 2021View details →

dryad40/100

Prior choice and data requirements of Bayesian multivariate mixed effects models fit to tag-recovery data: The need for power analyses

1. Recent empirical studies have quantified correlation between survival and recovery by estimating these parameters as correlated random effects with hierarchical Bayesian multivariate models fit to tag-recovery data. In these applications, increasingly negative correlation between survival and recovery has been interpreted as evidence for increasingly additive harvest mortality. The power of these hierarchal models to detect non-zero correlations has rarely been evaluated and these few studies have not focused on tag-recovery data, which is a common data type. 2. We assessed the power of multivariate hierarchical models to detect negative correlation between annual survival and recovery. Using three priors for multivariate normal distributions, we fit hierarchical effects models to a mallard (Anas platyrhychos) tag-recovery dataset and to simulated data with sample sizes corresponding to different levels of monitoring intensity. We also demonstrate more robust summary statistics for tag-recovery datasets than total individuals tagged. 3. Different priors lead to substantially different estimates of correlation from the mallard data. Our power analysis of simulated data indicated most prior distribution and sample size combinations could not estimate strongly negative correlation with useful precision or accuracy. Many correlation estimates spanned the available parameter space (–1,1) and underestimated the magnitude of negative correlation. Only one prior combined with our most intensive monitoring scenario provided reliable results. Underestimating the magnitude of correlation coincided with overestimating the variability of annual survival, but not annual recovery. 4. The inadequacy of prior distributions and sample size combinations previously assumed adequate for obtaining robust inference from tag-recovery data represents a concern in the application of Bayesian hierarchical models to tag-recovery data. Our analysis approach provides a means for examining prior influence and sample size on hierarchical models fit to capture-recapture data while emphasizing transferability of results between empirical and simulation studies.

opencc-zeroFeb 2023View details →

zenodo40/100

Datasets for Quantifying the impact of land use and land cover change on moisture recycling with convection-permitting WRF-tagging modeling in the agro-pastoral ecotone of northern China

These datasets are the processed and refined data that support and lead to the described results and allow other readers to assess the conclusions in the paper, entitled “Quantifying the impact of land use and land cover change on moisture recycling with convection-permitting WRF-tagging modeling in the agro-pastoral ecotone of northern China  ”.

opencc-by-4.0Aug 2022View details →

zenodo40/100

A Tagged Traffic Accident Dataset for Machine Learning

This dataset contains tagged accident data and is provided for reproducibility for our journal paper  Pablo Moriano, Andy Berres, Haowen Xu, Jibonananda Sanyal. “Spatiotemporal Features of Traffic Help Reduce Automatic Accident Detection Time.” Expert Systems with Applications 244 (2024): 122813. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.eswa.2023.122813" target="_blank" rel="noopener">https://doi.org/10.1016/j.eswa.2023.122813</a> The accompanying Data in Brief publication discusses the methodology behind the creation of these data. Berres, Andy, Pablo Moriano, Haowen Xu, Sarah Tennille, Lee Smith, Jonathan Storey, and Jibonananda Sanyal. "A Traffic Accident Dataset for Chattanooga, Tennessee." Data in Brief (2024): 110675.   The zip folder annotatedData.zip contains two subfolders: allData and bestData. The bestData folder contains all data for which a full neighborhood of five sensors upstream and five sensors downstream is available, whereas allData includes everything from bestData as well as data with a smaller number of neighboring sensors. Each folder contains one subfolder called accidents and one subfolder called non-accidents. The accidents folder contains one file per accident. The non-accidents folder contains files for the same location, day of the week and time as a corresponding accident, for each week during which there was no accident impact on the traffic. The file names in both folders are formatted as follows: yyyy-mm-dd-hhmm-rrrrrXaaa.a.csv, consisting of date (yyyy-mm-dd), time (hhmm in 24-hour format), and sensor name (rrrrrXaaa.a), which consists of road name (rrrrr; 5 alphanumerical characters), heading (X), and mile marker (aaa.a). For example, the file 2020-11-03-1611-00I24W182.8.csv  contains data for an accident which occurred at 4:11 p.m. on November 3, 2020 on I-24 Westbound near the radar sensor at mile marker 182.8. The content of each CSV file is a timeseries of radar data beginning 15 minutes prior to the reported incident and ending 15 minutes after the reported incident. It also contains metadata, such as the accident type, etc. Each CSV file contains the following columns: <ul> <li>incident at sensor(i): 1 for yes (accidents folder), 0 for no (non-accidents folder)</li> <li>road: road name with heading, e.g. 00I24E</li> <li>mile: mile marker of nearest radar sensor, e.g. 182.8</li> <li>type: accident type, e.g. “Prop Damage (over)” for property damage exceeding a certain threshold. For non-accidents, the type is given as “None”.</li> <li>date: date of the data sample. For accidents, this is the date on which the accident occurred. For non-accidents, this is the date for which the non-accident data sample is collected.</li> <li>incident_time: time the reference accident was reported in hh:mm. This is the time which is provided in E-TRIMS as the time the 911 call was made.</li> <li>incident_hour: just the hour from the incident_time, in integer format.</li> <li>data_time: timestamp for the timeseries contained in the file in hh:mm:ss format. The timeseries consists of 30 second timesteps.</li> <li>weather: weather during data_time, based on data collected from NASA POWER. We used dry bulb temperature (°C), precipitation (mm/h), and wind speed (m/s) from the raw NASA POWER data to produce the classifications of rain (at least 1mm precipitation and temperatures above 2°C), snow (at least 1mm precipitation and temperatures at or below 2°C), and wind (wind speeds over 30 mph or 13.5 m/s). If there were no inclement weather conditions, we set the category to “--".</li> <li>light: light conditions during data_time. To produce this field, we collected sunrise, sunset, civil twilight start and civil twilight end times from <a href="https://sunrise-sunset.org">https://sunrise-sunset.org</a>, and derived the categories dawn, daylight, dusk, and dark using these start and end times.</li> <li>The last 33 columns contain radar data for the 11 sensors surrounding the accident or non-accident. For each sensor, we collected speed (mean over 30-second interval in miles per hour, or empty if no vehicles passed), volume (count of all vehicles passing during 30-second interval), and occupancy (mean % of occupancy over 30-second interval).  These three variables are grouped in triples, of speed (k), volume (k), occupancy (k), where k indicates the sensor number relative to the closest sensor i to the incident, k<i indicate upstream sensors and k>i indicate downstream sensors. For example, speed (i-5) refers to the mean speed at the sensor which is 5 hops upstream from the accident, and volume(i+1) refers to the number of vehicles at the sensor immediately downstream from the accident.</li> </ul> The folder metaData.zip contains the following files: <ul> <li>Accidents.csv: cleaned-up accidents file with all accidents which happened on Chattanooga area highways between November 1, 2020 and April 29, 2021. We have removed accidents which happened on non-highway roads, and we have corrected the timestamps (which were in 12-hour format but missing a.m./p.m. markers) by cross-referencing light and weather conditions.</li> <li>WeatherDict.json: a dictionary containing the weather data synthesized from NASA POWER.</li> <li>LightDict.json: a dictionary containing the light data synthesized from Sunrise-and-Sunset.</li> <li>SensorTopology.csv: neighborhood information for each radar sensor in the Chattanooga area.</li> <li>SensorZones.geojson: polygons used to determine the nearest radar sensor for each accident location. Each polygon is tagged with the corresponding radar sensor’s name.</li> </ul>

opencc-by-4.0May 2023View details →

zenodo40/100

Radio Galaxy Zoo: Tagging Radio Subjects using Text

RadioTalk is a communication platform that enabled members of the Radio Galaxy Zoo (RGZ) citizen science project to engage in discussion threads and provide further descriptions of the radio subjects they were observing in the form of tags and comments. It contains a wealth of auxiliary information which is useful for the morphology identification of complex and extended radio sources. In this paper, we present this new dataset, and for the first time in radio astronomy, we combine text and images to automatically classify radio galaxies using a multi-modal learning approach. We found incorporating text features improved classification performance which demonstrates that text annotations are rare but valuable sources of information for classifying astronomical sources, and suggests the importance of exploiting multi-modal information in future citizen science projects. We also discovered over 10,000 new radio sources beyond the RGZ-DR1 catalogue in this dataset.

opencc-by-4.0Mar 2023View details →

zenodo40/100

EAMv1 output from simulations using tag v1_cflx_2021: annual averages

EAMv1 is version 1 of the atmosphere component of the Energy Exascale Earth System Model (E3SM, <a href="https://github.com/E3SM-Project/E3SM">https://github.com/E3SM-Project/E3SM</a>). In the official release of EAMv1, the dry removal of aerosols is calculated after the surface emissions have been applied in a timestep. The EAMv1 code posted at <a href="https://doi.org/10.5281/zenodo.7995850">https://doi.org/10.5281/zenodo.7995850</a> includes an option to apply the surface emissions after dry removal and before turbulent mixing. The option can be turned on by setting the namelist variable cflx_cpl_opt to 2. The original sequence of calculations in the official release of EAMv1 corresponds to cflx_cpl_opt = 1. This upload contains annually averaged model output from the following simulations, all of which were conducted for the year 2010 using the F20TRC5-CMIP6 compset, with the winds above 850 hPa nudged to the ERA-Interim reanalysis. <ul> <li>v1_ori_L30 (cflx_cpl_opt = 1, 30 grid layers)</li> <li>v1_ori_L72 (cflx_cpl_opt = 1, 72 grid layers)</li> <li>v1_rev_L30 (cflx_cpl_opt = 2, 30 grid layers)</li> <li>v1_rev_L72 (cflx_cpl_opt = 2, 72 grid layers)</li> </ul>

opencc-by-4.0Jun 2023View details →

zenodo40/100

EAMv1 output from simulations using tag v1_cflx_2021: instantaneous values

EAMv1 is version 1 of the atmosphere component of the Energy Exascale Earth System Model (E3SM, <a href="https://github.com/E3SM-Project/E3SM">https://github.com/E3SM-Project/E3SM</a>). This upload contains 6-hourly instantaneous model output from the following simulations, both conducted for the year 2010 using the F20TRC5-CMIP6 compset and with the winds above 850 hPa nudged to the ERA-Interim reanalysis. <ul> <li>v1_ori_L30 (30 grid layers)</li> <li>v1_ori_L72 (72 grid layers)</li> </ul>

opencc-by-4.0Jun 2023View details →

zenodo40/100

Figure 2 in Bombus impatiens (Hymenoptera: Apidae) display reduced pollen foraging behavior when marked with bee tags vs. paint

Figure 2. Curves showing the cumulative percentage of bees that performed sonication on Solanum lycopersicum L. after being marked with paint vs. bee tags, out of the total number of marked bees recovered by the end of the experiment (n paint = 83; n tag = 94; n missing = 34). The "+" symbols indicate censored data — bees that never were observed collecting pollen after being marked, within the time constraints of the experiment.

opencc-by-4.0Jul 2016View details →

zenodo40/100

Data for: European seabass show variable responses in their group swimming features after tag implantation

Data for: European seabass show variable responses in their group swimming features after tag implantationPlease read the README.txt file before working with the data.

opencc-by-4.0Oct 2023View details →

zenodo40/100

Text to audio grounding (TAG) dataset: AudioGrounding

AudioGrounding dataset, including audio files and timestamp annotations.Changes in version 2: The train/validation/test sets are re-split. The validation and test annotations are refined. ----------------------------------------------------------References[1] Xuenan Xu, Heinrich Dinkel, Mengyue Wu and Kai Yu. "Text-to-audio grounding: Building correspondence between captions and sound events." In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021, pp. 606-610.[2] Xuenan Xu, Mengyue Wu, and Kai Yu. "Investigating Pooling Strategies and Loss Functions for Weakly-Supervised Text-to-Audio Grounding via Contrastive Learning." In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). IEEE, 2023, pp. 1-5.

opencc-by-4.0Oct 2022View details →

dryad40/100