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Dataset results
122 results for “tracker”
PsPM-AOB: Eye tracker (including pupillometry) measurements from auditory oddball tasks
<p>This dataset includes eye tracker (including pupillometry) measurements from auditory oddball tasks with ITIs of 1, 2, and 3 s (in groups 1, 2, and 3 respectively). Also included are task information, keypress responses, keypress response times and key correctness for each of 66 healthy unmedicated participants (40 females and 26 males aged 24.2+/-3.9 years) participating in auditory oddball tasks. Stimuli consist of sine tones (50-ms length; 10-ms ramp; 440 or 660 Hz).</p>
PsPM-EWO: Eye tracker (including pupillometry) measurements from emotional-words tasks
<p>This dataset includes eye tracker (including pupillometry) measurements for 37 healthy unmedicated participants (25 females and 12 males, age range: 18 - 34 years, mean age: 26.2 +/- 4.7 years) participating in an emotional-words task. In each session, participants were presented with 50 neutral and 50 negative five-letter nouns from the Berlin Affective Word List Reloaded (Võ et al., 2009). Also included are task information, keypress responses, keypress response times.</p>
PsPM-RRM1-2: SCR, ECG, respiration and eye tracker measurements in response to electric stimulation or visual targets
<p>This dataset includes skin conductance response (SCR), electrocardiogram (ECG), respiration and eye tracker (including pupillometry) measurements for each of 29 healthy unmedicated participants (7 males and 22 females aged 23.5 +/- 3.6 years) in response to 10 discomforting electric stimulations to the forearm (RRM1) or 10 visual targets in a visual detection task (RRM2). The sample partly overlaps with data set <a href="https://doi.org/10.5281/zenodo.1292568">PsPM-FR</a>. Some participants did not take part in RRM1 or RRM2 such that there are 25 recordings for RRM1 and 26 recordings for RRM2. Electric shock stimuli are 0.2 ms wide square current pulse repeated at 500 Hz for 500 ms and individually adjusted amplitude just below the pain threshold. Visual stimuli are red crosses (+) embedded in a white digit stream; each stimulus is presented during 200 ms and separated by a 800 ms blank interval. ITI is selected randomly on each trial from 40 s, 45 s or 50 s. A baseline period with distractors but no targets concludes experiment RRM2. (This is in contrast to the methods description in Bach et al. (2016), according to which the baseline period was randomly either in the beginning or at the end of the experiment. This discrepancy was caused by an error in the code that controlled the experiment presentation.)</p>
Tierpsy Tracker test data
<p>Test data for Tierpsy Tracker, the MultiWorm Tracker developed at André Brown's Behavioural Genomics lab, at the MRC London Institute of Medical Sciences.</p> <p><a href="https://github.com/Tierpsy/tierpsy-tracker">https://github.com/Tierpsy/tierpsy-tracker</a></p> <p>Original paper:</p> <p>Javer, A., Currie, M., Lee, C.W. <em>et al.</em> An open-source platform for analyzing and sharing worm-behavior data. <em>Nat Methods</em> <strong>15, </strong>645–646 (2018). https://doi.org/10.1038/s41592-018-0112-1</p>
SAGE Rejected Article Tracker Training Data
<p>The enclosed dataset shows metadata for ArXiv preprints uploaded to ArXiv in 2012.</p> <p>For each preprint, there are 2 rows of search data:</p> <ul> <li>ArXiv preprint metadata plus the CrossRef API data for the <em>correct</em> search result (which is the metadata for the published version of that preprint).</li> <li>ArXiv preprint metadata and the metadata for the top <em>incorrect</em> CrossRef API search result for the title and author-names associated with the preprint.</li> </ul> <p>ArXiv preprints are referred to as 'query' documents and CrossRef documents are referred to as 'match' documents.</p> <p>This dataset is created using the <a href="https://github.com/sagepublishing/rejected_article_tracker_pkg">SAGE Rejected Article Tracker</a> and is supplementary to that project. Similar custom datasets can be created using the SAGE Rejected Article Tracker with different parameters (e.g. different timeframes).</p>
Freehand ultrasound without external trackers
<blockquote> <p><strong>We have collected a new large freehand ultrasound dataset and are organising a MICCAI2024&2025 Challenges (<a href="https://github-pages.ucl.ac.uk/tus-rec-challenge/" target="_blank" rel="noopener">TUS-REC Challenge</a>). Check <a href="../records/11178509" target="_blank" rel="noopener">Part 1</a> and <a href="../records/11180795" target="_blank" rel="noopener">Part 2</a> of the training dataset for TUS-REC2024, and <a href="https://zenodo.org/records/15224704" target="_blank" rel="noopener">Train Data</a> for TUS-REC2025. </strong></p> </blockquote> <p>Freehand US scans were acquired on both left and right forearms from 19 volunteers, using Ultrasonix machine (BK, Europe) with a curvilinear probe (4DC7-3/40), tracked by an NDI Polaris Vicra (Northern Digital Inc., Canada). On each forearm, the US probe was moved, for the study purpose, in a straight line, a ‘C’ shape and a ‘S’ shape, in a distal-to-proximal direction. These three scans were repeated, with the curvilinear transducer held (thus the US planes) perpendicular of and parallel to the forearm. B-mode images with median level of speckle reduction were recorded at ~20 fps. Each scan included frames between 36 and 430 with a size of 480×640 pixels, equivalent to a probe travel distance approximately between 100 and 200 mm. </p> <p>A total of 12 scans were acquired from each volunteer, recorded in a single ‘*.mha’ file, with the filename indicating the acquisition time. For example, “LH_Ver_S_20220425_141454.mha” means a scan acquired on 14:14:54 April 25th, 2022. The ‘valid_frames.csv’ file contains the 6 “protocols” with each arm from each volunteer: 1) RH_Par_L (right arm, straight line shape with the probe parallel to the forearm); 2) RH_Par_C (right arm, ‘C’ shape with the probe parallel to the forearm); 3) RH_Par_S (right arm, ‘S’ shape with the probe parallel to the forearm); 4) RH_Ver_L (right arm, straight line shape with the probe perpendicular of the forearm); 5) RH_Ver_C (right arm, ‘C’ shape with the probe perpendicular of the forearm); 6) RH_Ver_S (right arm, ‘S’ shape with the probe perpendicular of the forearm); 7) LH_Par_L (left arm, straight line shape with the probe parallel to the forearm); 8) LH_Par_C (left arm, ‘C’ shape with the probe parallel to the forearm); 9) LH_Par_S (left arm, ‘S’ shape with the probe parallel to the forearm); 10) LH_Ver_L (left arm, straight line shape with the probe perpendicular of the forearm); 11) LH_Ver_C (left arm, ‘C’ shape with the probe perpendicular of the forearm); 12) LH_Ver_S (left arm, ‘S’ shape with the probe perpendicular of the forearm). The ‘start’ and ‘end’ denote the start and end frame indices of a scan, respectively, in each ‘*.mha’ file. </p> <p>US images, transformation matrix obtained from the tracker, and corresponding csv file, for each scan can be found in Freehand_US_data.zip. In the “calib_matrix.csv” file, we provide a calibration matrix and a time difference in sec, obtained from our calibration experiments. </p> <p>A baseline code is provided in this <a href="https://github.com/ucl-candi/freehand" target="_blank" rel="noopener">repo</a>.</p> <p>If you find this data set useful for your research, please consider citing some of the following works: </p> <ul> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Trackerless freehand ultrasound with sequence modelling and auxiliary transformation over past and future frames." In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp. 1-5. IEEE, 2023. doi: <a href="https://doi.org/10.1109/ISBI53787.2023.10230773" target="_blank" rel="noopener">10.1109/ISBI53787.2023.10230773</a></li> <li>Qi Li, Ziyi Shen, Qianye Yang, Dean C. Barratt, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Nonrigid Reconstruction of Freehand Ultrasound without a Tracker." In <em>International Conference on Medical Image Computing and Computer-Assisted Intervention</em>, pp. 689-699. Cham: Springer Nature Switzerland, 2024. doi: <a href="https://doi.org/10.1007/978-3-031-72083-3_64" target="_blank" rel="noopener">10.1007/978-3-031-72083-3_64</a></li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker." IEEE Transactions on Biomedical Engineering, vol. 71, no. 3, pp. 1033-1042, 2024. doi: <a href="https://ieeexplore.ieee.org/abstract/document/10288201" target="_blank" rel="noopener">10.1109/TBME.2023.3325551</a>.</li> <li>Qi Li, Ziyi Shen, Qian Li, Dean C. Barratt, Thomas Dowrick, Matthew J. Clarkson, Tom Vercauteren, and Yipeng Hu. "Privileged Anatomical and Protocol Discrimination in Trackerless 3D Ultrasound Reconstruction." In International Workshop on Advances in Simplifying Medical Ultrasound, pp. 142-151. Cham: Springer Nature Switzerland, 2023. doi: <a href="https://doi.org/10.1007/978-3-031-44521-7_14" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-44521-7_14</a></li> </ul>
Scene camera movies from mobile eye tracker
<p>These are the full recorded scene camera scenes. Eye position data for each scene can also be found here as well as an excel file detailing which parts of the clips we used.</p>
Replication data for: Enhancing user awareness on inferences obtained from fitness trackers data
<p>Survey results and fitness trackers datasets used for the evaluation of PrivacyEnhAction application.</p>
Tracker Analysis File
<p>The csv data file contain the following properties of tracks:</p> <p>'Longitude [°]', 'Latitude [°]',<br> 'Mean Precipitation [mm h$^{-1}$]',<br> 'Maximum Precipitation [mm h$^{-1}$]',<br> 'Volume [km² h]', 'Rain Volume [m³ E6]',<br> 'Area [km²]', 'Duration [h]',</p> <p>'Trackers' indicates the tracker used,<br> 'OBS_ModEns' indicates whether the track originates from observations or model,<br> 'Models' indicates the model used,<br> 'Years' and 'Months' indicate the starting date,<br> 'GRIPHO_ornot' indicates whether the track centroid is within or outside of the GRIPHO-Italy domain.</p>
Bee Tracker – an open-source machine-learning based video analysis software for the assessment of nesting and foraging performance of cavity-nesting solitary bees
<p>The foraging and nesting performance of bees can provide important information on bee health and is of interest for risk and impact assessment of environmental stressors. While radio-frequency identification (RFID) technology is an efficient tool increasingly used for the collection of behavioral data in social bee species such as honey bees, behavioral studies on solitary bees still largely depend on direct observations, which is very time-consuming.</p> <p>Here, we present a novel automated methodological approach of individually and simultaneously tracking and analyzing foraging and nesting behavior of numerous cavity-nesting solitary bees. The approach consists of monitoring nesting units by video recording and automated analysis of videos by a machine learning based software. This <i>Bee Tracker</i> software consists of four trained deep learning networks to detect bees that enter or leave their nest and to recognize individual IDs on the bees' thorax as well as the IDs of their nests according to their positions in the nesting unit.</p> <p>The software is able to identify each nest of each individual nesting bee, which permits to measure individual-based measures of reproductive success. Moreover, the software quantifies the number of cavities a female enters until it finds its nest as a proxy of nest recognition, and it provides information on the number and duration of foraging trips. By training the software on 8 videos recording 24 nesting females per video, the software achieved a precision of 96% correct measurements of these parameters.</p> <p>The software could be adapted to various experimental setups by training it to an according set of videos. The presented method allows to efficiently collect large amounts of data on cavity-nesting solitary bee species and represents a promising new tool for the monitoring and assessment of behavior and reproductive success under laboratory, semi-field and field conditions.</p>
Terrestrial Parasite Tracker indexed biotic interactions and review summary
<p>PLEASE CONTACT AUTHORS IF YOU CONTRIBUTED AND WOULD LIKE TO BE LISTED AS A CO-AUTHOR.</p> <p>Terrestrial Parasite Tracker indexed biotic interactions and review summary.</p> <p>The Terrestrial Parasite Tracker (TPT) project began in 2019 and is funded by the National Science foundation to mobilize data from vector and ectoparasite collections to data aggregators (e.g., iDigBio, GBIF) to help build a comprehensive picture of arthropod host-association evolution, distributions, and the ecological interactions of disease vectors which will assist scientists, educators, land managers, and policy makers. Arthropod parasites often are important to human and wildlife health and safety as vectors of pathogens, and it is critical to digitize these specimens so that they, and their biotic interaction data, will be available to help understand and predict the spread of human and wildlife disease.</p> <p>This data publication contains versioned TPT associated datasets and related data products that were tracked, reviewed and indexed by Global Biotic Interactions (GloBI) and associated tools. GloBI provides open access to finding species interaction data (e.g., predator-prey, pollinator-plant, pathogen-host, parasite-host) by combining existing open datasets using open source software.</p> <p>If you have questions or comments about this publication, please open an issue at https://github.com/ParasiteTracker/tpt-reporting or contact the authors by email.</p> <p>Funding:<br> The creation of this archive was made possible by the National Science Foundation award "Collaborative Research: Digitization TCN: Digitizing collections to trace parasite-host associations and predict the spread of vector-borne disease," Award numbers DBI:1901932 and DBI:1901926</p> <p>References:<br> Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005.</p> <p>GloBI Data Review Report</p> <p>Datasets under review:<br> - University of Michigan Museum of Zoology Insect Division. Full Database Export 2020-11-20 provided by Erika Tucker and Barry Oconner. accessed via https://github.com/EMTuckerLabUMMZ/ummzi/archive/6731357a377e9c2748fc931faa2ff3dc0ce3ea7a.zip on 2022-10-12T18:43:37.491Z<br> - Academy of Natural Sciences Entomology Collection for the Parasite Tracker Project accessed via https://github.com/globalbioticinteractions/ansp-para/archive/5e6592ad09ec89ba7958266ad71ec9d5d21d1a44.zip on 2022-10-12T18:45:13.893Z<br> - Bernice Pauahi Bishop Museum, J. Linsley Gressitt Center for Research in Entomology accessed via https://github.com/globalbioticinteractions/bpbm-ent/archive/c085398dddd36f8a1169b9cf57de2a572229341b.zip on 2022-10-12T18:47:33.370Z<br> - Texas A&M University, Biodiversity Teaching and Research Collections accessed via https://github.com/globalbioticinteractions/brtc-para/archive/f0a718145b05ed484c4d88947ff712d5f6395446.zip on 2022-10-12T18:49:42.688Z<br> - Brigham Young University Arthropod Museum accessed via https://github.com/globalbioticinteractions/byu-byuc/archive/4a609ac6a9a03425e2720b6cdebca6438488f029.zip on 2022-10-12T18:50:01.049Z<br> - California Academy of Sciences Entomology accessed via https://github.com/globalbioticinteractions/cas-ent/archive/562aea232ec74ab615f771239451e57b057dc7c0.zip on 2022-10-12T18:50:25.480Z<br> - Clemson University Arthropod Collection accessed via https://github.com/globalbioticinteractions/cu-cuac/archive/6cdcbbaa4f7cec8e1eac705be3a999bc5259e00f.zip on 2022-10-12T18:50:53.662Z<br> - Denver Museum of Nature and Science (DMNS) Parasite specimens (DMNS:Para) accessed via https://github.com/globalbioticinteractions/dmns-para/archive/2a15f657d5e2d7a6ee6359ee30e630bde8fea2ee.zip on 2022-10-12T18:52:36.684Z<br> - Field Museum of Natural History IPT accessed via https://github.com/globalbioticinteractions/fmnh/archive/6bfc1b7e46140e93f5561c4e837826204adb3c2f.zip on 2022-10-12T19:19:24.919Z<br> - Illinois Natural History Survey Insect Collection accessed via https://github.com/globalbioticinteractions/inhs-insects/archive/38692496f590577074c7cecf8ea37f85d0594ae1.zip on 2022-10-12T19:21:30.100Z<br> - UMSP / University of Minnesota / University of Minnesota Insect Collection accessed via https://github.com/globalbioticinteractions/min-umsp/archive/3f1b9d32f947dcb80b9aaab50523e097f0e8776e.zip on 2022-10-12T19:22:18.235Z<br> - Milwaukee Public Museum Biological Collections Data Portal accessed via https://github.com/globalbioticinteractions/mpm/archive/9f44e99c49ec5aba3f8592cfced07c38d3223dcd.zip on 2022-10-12T19:22:42.835Z<br> - Museum for Southwestern Biology (MSB) Parasite Collection accessed via https://github.com/globalbioticinteractions/msb-para/archive/f13bfa0d5493057198639d566f744379c05179f3.zip on 2022-10-12T20:46:06.063Z<br> - The Albert J. Cook Arthropod Research Collection accessed via https://github.com/globalbioticinteractions/msu-msuc/archive/38960906380443bd8108c9e44aeff4590d8d0b50.zip on 2022-10-12T21:02:26.320Z<br> - Ohio State University Acarology Laboratory accessed via https://github.com/globalbioticinteractions/osal-ar/archive/876269d66a6a94175dbb6b9a604897f8032b93dd.zip on 2022-10-12T21:02:46.553Z<br> - Frost Entomological Museum, Pennsylvania State University accessed via https://github.com/globalbioticinteractions/psuc-ento/archive/30b1f96619a6e9f10da18b42fb93ff22cc4f72e2.zip on 2022-10-12T21:02:57.714Z<br> - Purdue Entomological Research Collection accessed via https://github.com/globalbioticinteractions/pu-perc/archive/e0909a7ca0a8df5effccb288ba64b28141e388ba.zip on 2022-10-12T21:03:17.696Z<br> - Texas A&M University Insect Collection accessed via https://github.com/globalbioticinteractions/tamuic-ent/archive/f261a8c192021408da67c39626a4aac56e3bac41.zip on 2022-10-12T21:03:56.509Z<br> - University of California Santa Barbara Invertebrate Zoology Collection accessed via https://github.com/globalbioticinteractions/ucsb-izc/archive/4d997dbe8e86398f9f7f4d7851013e788073ae9c.zip on 2022-10-12T21:05:27.222Z<br> - University of Hawaii Insect Museum accessed via https://github.com/globalbioticinteractions/uhim/archive/53fa790309e48f25685e41ded78ce6a51bafde76.zip on 2022-10-12T21:05:40.778Z<br> - University of New Hampshire Collection of Insects and other Arthropods UNHC-UNHC accessed via https://github.com/globalbioticinteractions/unhc/archive/f72575a72edda8a4e6126de79b4681b25593d434.zip on 2022-10-12T21:05:59.319Z<br> - Scott L. Gardner and Gabor R. Racz (2021). University of Nebraska State Museum - Parasitology. Harold W. Manter Laboratory of Parasitology. University of Nebraska State Museum. accessed via https://github.com/globalbioticinteractions/unl-nsm/archive/6bcd8aec22e4309b7f4e8be1afe8191d391e73c6.zip on 2022-10-12T21:06:07.054Z<br> - Data were obtained from specimens belonging to the United States National Museum of Natural History (USNM), Smithsonian Institution, Washington DC and digitized by the Walter Reed Biosystematics Unit (WRBU). accessed via https://github.com/globalbioticinteractions/usnmentflea/archive/ce5cb1ed2bbc13ee10062b6f75a158fd465ce9bb.zip on 2022-10-12T21:06:43.102Z<br> - US National Museum of Natural History Ixodes Records accessed via https://github.com/globalbioticinteractions/usnm-ixodes/archive/c5fcd5f34ce412002783544afb628a33db7f47a6.zip on 2022-10-12T21:06:51.935Z<br> - Price Institute of Parasite Research, School of Biological Sciences, University of Utah accessed via https://github.com/globalbioticinteractions/utah-piper/archive/43da8db550b5776c1e3d17803831c696fe9b8285.zip on 2022-10-12T21:07:03.317Z<br> - University of Wisconsin Stevens Point, Stephen J. Taft Parasitological Collection accessed via https://github.com/globalbioticinteractions/uwsp-para/archive/f9d0d52cd671731c7f002325e84187979bca4a5b.zip on 2022-10-12T21:07:14.513Z<br> - Giraldo-Calderón, G. I., Emrich, S. J., MacCallum, R. M., Maslen, G., Dialynas, E., Topalis, P., … Lawson, D. (2015). VectorBase: an updated bioinformatics resource for invertebrate vectors and other organisms related with human diseases. Nucleic acids research, 43(Database issue), D707–D713. doi:10.1093/nar/gku1117. accessed via https://github.com/globalbioticinteractions/vectorbase/archive/00d6285cd4e9f4edd18cb2778624ab31b34b23b8.zip on 2022-10-12T21:07:22.543Z<br> - WIRC / University of Wisconsin Madison WIS-IH / Wisconsin Insect Research Collection accessed via https://github.com/globalbioticinteractions/wis-ih-wirc/archive/34162b86c0ade4b493471543231ae017cc84816e.zip on 2022-10-12T21:07:52.105Z<br> - Yale University Peabody Museum Collections Data Portal accessed via https://github.com/globalbioticinteractions/yale-peabody/archive/43be869f17749d71d26fc820c8bd931d6149fe8e.zip on 2022-10-12T21:16:57.226Z</p> <p>Generated on:<br> 2022-10-12</p> <p>by:<br> GloBI's Elton 0.12.4 <br> (see https://github.com/globalbioticinteractions/elton).</p> <p>Note that all files ending with .tsv are files formatted <br> as UTF8 encoded tab-separated values files.</p> <p>https://www.iana.org/assignments/media-types/text/tab-separated-values</p> <p><br> Included in this review archive are:</p> <p>README:<br> This file.</p> <p>review_summary.tsv:<br> Summary across all reviewed collections of total number of distinct review comments.</p> <p>review_summary_by_collection.tsv:<br> Summary by reviewed collection of total number of distinct review comments.</p> <p>indexed_interactions_by_collection.tsv: <br> Summary of number of indexed interaction records by institutionCode and collectionCode.</p> <p>review_comments.tsv.gz:<br> All review comments by collection.</p> <p>indexed_interactions_full.tsv.gz:<br> All indexed interactions for all reviewed collections.</p> <p>indexed_interactions_simple.tsv.gz:<br> All indexed interactions for all reviewed collections selecting only sourceInstitutionCode, sourceCollectionCode, sourceCatalogNumber, sourceTaxonName, interactionTypeName and targetTaxonName.</p> <p>datasets_under_review.tsv:<br> Details on the datasets under review.</p> <p>elton.jar: <br> Program used to update datasets and generate the review reports and associated indexed interactions.</p> <p>datasets.zip:<br> Source datasets used by elton.jar in process of executing the generate_report.sh script.</p> <p>generate_report.sh:<br> Program used to generate the report</p> <p>generate_report.log:<br> Log file generated as part of running the generate_report.sh script</p>
Technical Debt Classification in Issue Trackers using Natural Language Processing based on Transformers
<p>In order to ensure transparency and reproducibility, we have made everything available publicly here, including the Code, Models, Datasets and more. All the files and their functionality used in this paper are explained clearly in the <strong>README.md</strong> file.</p> <p>Background: Technical Debt (TD) needs to be controlled and tracked during software development. Support to automatically track TD in issue trackers is limited. </p> <p>Aim: We explore the usage of a large dataset of developer-labeled TD issues in combination with cutting-edge Natural Language Processing (NLP) approaches to automatically classify TD in issue trackers.</p> <p>Method: We mine and analyze more than 160GB of textual data from GitHub projects, collecting over 55,600 TD issues and consolidating them into a large dataset (GTD dataset). We use such datasets to train and test Transformer ML models. Then we test the model's generalization ability by testing them on six unseen projects. Finally, we re-train the models including part of the TD issues from the target project to test their adaptability. </p> <p>Results and Conclusion: (i) We create and release the GTD dataset, a comprehensive dataset including TD issues from 6,401 public repositories with various contexts; (ii) By training Transformers using the GTD dataset, we achieve performance metrics that are promising; (iii) Our results are a significant step forward towards supporting the automatic classification of TD in issue trackers, especially when the models are adapted to the context of unseen projects after fine-tuning.</p>
Example data for the porpoise tracker program
<p>This directory contains two videos and matching flight log files acquired by a DJI Phantom 4 Pro.<br> The videos and flight logs are matched as follows:</p> <ul> <li>`DJI_0004.MOV` are covered by `DJIFlightRecord_2018-07-04_[10-42-34].txt`,</li> <li>`DJI_0013.MOV` are covered by `DJIFlightRecord_2018-07-04_[11-19-31].txt`</li> </ul> <p>In `DJI_0004.MOV` the drone circled around a point, with the camera pointing towards the center of rotation.<br> In `DJI_0013.MOV` the drone stayed in the same location and the camera pitch was changed from looking forward to downwards.</p> <p>In addition the file `FOVPhantom4Pro-new.csv` contains information about the field of view<br> of the used Phantom 4 Pro drone.</p> <p>The above files are intended to be used as example input data for the <a href="https://github.com/henrikmidtiby/PorpoiseTracker/">Porpoise Tracker</a>.</p>
AURORA Energy Tracker App Dashboard on 9 September 2024
<p>Snapshots of the AURORA Energy Tracker dashboard, showing data collected on 9 September 2024 regarding average user, energy labels and gender distribution.</p> <p>The dashboard is available at https://dashboard.aurora-h2020.eu/en-GB and shows data from 6 February 2024. Note that AURORA is an ongoing citizen science project and the data on this page is provided without guarantee.</p>
Dataset for Perspectives on Open Science and The Future of Scholarly Communication: Internet Trackers, Algorithmic Persuasion and Robotic Process Automation
<p>This data set was created between 01-04.2021, to study the current landscape of using web trackers in scholarly communication. The data set is part of an article (manuscript) that is intended to be published under the this title: Perspectives on Open Science and The Future of Scholarly Communication: Internet Trackers, Algorithmic Persuasion and Robotic Process Automation.</p>
Raw data: Validity of fitness trackers when worn by older adults
<p>Co-authors of the dataset:</p> <p>Marina Dobnik <sup>2</sup> , Stefan Loefler <sup>3,4,6</sup> , Christian Hofer <sup>4</sup> and Nejc Šarabon <sup>2,5</sup></p> <p><sup>1</sup> University of Primorska, Andrej Marušič Institute, Muzejski trg 2, 6000 Koper, Slovenia; kaja.kastelic@iam.upr.si</p> <p><sup>2</sup> University of Primorska, Faculty of Health Sciences, Polje 42, 6310 Izola, Slovenia; <a href="mailto:nejc.sarabon@fvz.upr.si">nejc.sarabon@fvz.upr.si</a></p> <p><sup>3</sup> Physiko- & Rheumatherapie, Institute for Physical Medicine and Rehabilitation, 3100 St. Pölten, Austria; stefan.loefler@kern-reha.at</p> <p><sup>4</sup> Ludwig Boltzmann Institute for Rehabilitation Research, Neugebäudeplatz 1, 3100 St. Pölten, Austria; christian.hofer@rehabilitationresearch.eu</p> <p><sup>5</sup> InnoRenew CoE, Livade 6, 6310 Izola, Slovenia</p>
Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information
<p><strong>Citation</strong></p> <p>Zhang, Alice Tianbo, and Vincent Xinyi Gu. 2023. “Global Dam Tracker: A Database of More than 35,000 Dams with Location, Catchment, and Attribute Information.” <em>Scientific Data</em> 10 (1): 111.</p> <p><a href="https://www.nature.com/articles/s41597-023-02008-2">https://www.nature.com/articles/s41597-023-02008-2</a></p> <p> </p> <p><strong>Description</strong></p> <p>We present one of the most comprehensive geo-referenced global dam databases to date. The Global Dam Tracker (GDAT) contains 35,000 dams with cross-validated geo-coordinates, satellite-derived catchment areas, and detailed attribute information. Combining GDAT with fine-scaled satellite data spanning three decades, we demonstrate how GDAT improves upon existing databases to enable the inter-temporal analysis of the costs and benefits of dam construction on a global scale. Our findings show that over the past three decades, dams have contributed to a dramatic increase in global surface water coverage, especially in developing countries in Asia and South America. This is an important step toward a more systematic understanding of the worldwide impact of dams on local communities. By filling in the data gap, GDAT would help inform a more sustainable and equitable approach to energy access and economic development.</p>
PsPM-AOB_UW: Eye tracker (including pupillometry) measurements from an auditory oddball and a luminance task
<p>This dataset includes eye tracker (including pupillometry) measurements from an auditory oddball task with an ITI of 2 s (session 1) and a luminance task in which discs with different shades of grey were presented. Also included are task information, keypress responses, keypress response times and key correctness for the oddball task. Data come from 23 healthy unmedicated female participants aged 41.87 +/- 3.9 years as a control group for a lesion patient with Urbach-Wiethe syndrome. Stimuli consist of sine tones (50-ms length; 10-ms ramp; 440 or 660 Hz).</p>
Bee Tracker – an open-source machine-learning based video analysis software for the assessment of nesting and foraging performance of cavity-nesting solitary bees
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CovidCounties is an interactive real time tracker of the COVID19 pandemic at the level of US counties
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Allen Brain Atlas
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