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2,610 results for “TRACK”
LMD Inconel 718 Random Tracks 12 versions 2021-03-03
<p>Description of dataset 10.5281/zenodo.5607321</p> <p>12 versions of deposition of 4 "random pattern" tracks<br> Inconel 718 tracks with process parameters:<br> - Powder flux = 0.099 (g/s)<br> - Nr. nozzles = 4<br> - Argon carrier flux = 4 (l/min)<br> - Argon shielding gas flux = 15 (l/min)<br> - Substrate temperature = Ambient<br> - Machine: Laserdyne 430<br> Power and velocity used in each experiment are described in the CNC file name (and g-code)</p> <p>The dataset is constituted by 12 .zip files (exp01.zip to exp12.zip), each one containing:</p> <p>- G-code part program, with name code Exp[n]_RandomTrack_4_rot_False_V[v]_P[p], where:<br> - [n] is the experiment index (1-12)<br> - [v] is the used deposition velocity (in mm/min)<br> - [p] is the used laser power (in W)<br> - trAll_ONEms.csv containing:<br> - t: timestamp in ms.<br> - Xpos: laser spot X position in workspace<br> - Ypos: laser spot Y position in workspace<br> - Zpos: laser spot Z position in workspace<br> - G1: binary signal indicating active deposition (G1=1) or not<br> - D: track width measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - H: track heigth measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - A: track section area measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - sdres: roughness index of section profile (std. deviation w.r.t. smoothed profile)<br> - Vnom: laser spot translational speed in m/s (computed from Xpos, Ypos, Zpos and t data)<br> - Pnom: nominal power</p>
LMD Inconel 718 Random Tracks 2021-03-03 r2
<p>Description of dataset 10.5281/zenodo.5607279</p> <p>Deposition of 4 Inconel 718 random tracks with process parameters:<br> - Nominal power = 300 (W)<br> - Nominal velocity = 600 (mm/min)<br> - Powder flux = 0.0825 (g/s)<br> - Spiral size: 60 mm side, 3 mm step<br> - Nr. nozzles = 4<br> - Argon carrier flux = 4 (l/min)<br> - Argon shielding gas flux = 15 (l/min)<br> - Substrate temperature = Ambient<br> - Machine: Laserdyne 430</p> <p>The dataset is constituted by:<br> - Melt pool images, in file Deposition_2021_03_03__15_42_36.zip, acquired at 200fps with 850 nm narrow band filter, 1ms exposure time. 400x400 px size<br> - trAll.csv containing:<br> - t: timestamp in ms. Synchronized with imAll.csv<br> - Xpos: laser spot X position in workspace<br> - Ypos: laser spot Y position in workspace<br> - Zpos: laser spot Z position in workspace<br> - G1: binary signal indicating active deposition (G1=1) or not<br> - D: track width measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - H: track heigth measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - A: track section area measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - sdres: roughness index of section profile (std. deviation w.r.t. smoothed profile)<br> - Vnom: laser spot translational speed in m/s (computed from Xpos, Ypos, Zpos and t data)<br> - Pnom: nominal power<br> - V: Vnom in mm/min<br> - imAll.csv containing:<br> - t: timestamp in ms. Synchronized with trAll.csv<br> - I_mean: mean image intensity (only on red channel)<br> - I_mean_crop: mean image intensity computed on central cropped image area (180x180 pixels)<br> - M_I_mean: I_mean after application of 8-sample moving average<br> - M_I_mean_crop: I_mean_crop after application of 8-sample moving average<br> - fileName: associated image file name<br> - beamON: laserON signal obtained from thresholding on images (background noise = off, minimal intensity level = on)</p> <p>Additional source files:<br> - G-Code part program RandomTrack_0_rot_False.cnc<br> - Machine log file Scope 20210303-154513 RANDOMTRACK_r2.mos<br> - 3D scan of sample geometry randomTrack_r2_keyence_2_skip3_surface<br> - Sample photo 20210303_RandomTracks_r2.jpeg</p>
OpenLABEL-enriched KITTI Tracking scenarios
<p>In HEADSTART T3.4 task, a conversion from the KITTI format into the ASAM OpenLABEL standard was performed.</p> <p>Additionally, several objects, actions, events, contexts and relations were added to the original annotations, creating richer descriptions of the scenes.</p> <p>The OpenLABEL JSON files were created and used in HEADSTART T3.4 to validate the concept of scenario mining from real data.</p>
(Fastq Files) Amplicon sequencing of ama1 and mdr1 to track within-host P. falciparum diversity in Kilifi, KENYA
<p>These data were generated from amplicon sequencing of <em>Plasmodium falciparum</em> <em>ama1 </em>and<em> </em><em>mdr1</em> genes in samples collected from Kilifi, at the coast of Kenya.</p> <p>The two papers that reference these data will soon be included here:</p> <ol> <li> The Journal of Infectious Diseases - https://doi.org/10.1093/infdis/jiac144</li> <li>Wellcome Open Research - https://wellcomeopenresearch.org/articles/7-95</li> </ol> <p>Two objectives were explored:</p> <ol> <li>To determine temporal changes in the genetic diversity of malaria parasites in asymptomatic and febrile infections.</li> <li>To track within-host parasite diversity, throughout treatment in a clinical drug trial.</li> </ol>
Near-field images and cross-section of guided modes in a laser-inscribed double-tracks waveguide in TZN:Ag glass sample
<p><strong>Raw images were captured</strong> with a Thorlabs beam monitoring camera, while the waveguides were injected at 633 nm.<br> The fours cross-sections were computed from these raw images.<br> These files are new data from the co-authors among those presented in the review publication "Materials 2020, 13, 3846" (DOI: 10.3390/ma13173846.<br> <strong>Extracted, centered and scaled horizontal cross-sections are given in "Fig12-b-c_final.xlsx"</strong></p> <p>Sample name : TZN:Ag.</p> <p> </p>
10-day backward trajectories from ECMWF analysis data along the ship track of the Antarctic Circumnavigation Expedition in austral summer 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains 10-day backward trajectories along the ship track of the Antarctic Circumnavigation Expedition from Nov 2016 – April 2017 calculated with the Lagrangian analysis tool LAGRANTO using the 3D-wind fields from the European Centre for Medium Range Weather Forecasts (ECMWF) operational analysis data. The trajectories were started from up to 56 vertical levels between 0 and 500 hPa a.s.l. and various variables were interpolated along the trajectories.</p> <p><strong>Dataset contents</strong></p> <ul> <li>trajs_ACE.zip: lsl_${year}${month}${day}_${hour}, trajectory files (containing all trajectories starting at ${year}${month}${day} ${hour}UTC at the ACE track from different vertical levels), comma-separated values</li> <li>fig_map.zip: map_long10_${year}${month}${day}_${hour}.png, map plots of all trajectories starting at ${year}${month}${day} ${hour}UTC coloured by pressure, portable network graphics</li> <li>fig_cross.zip: cross10_q_${year}${month}${day}_${hour}.png, cross-section plots of all trajectories starting at ${year}${month}${day} ${hour}UTC coloured by specific humidity, portable network graphics</li> <li>data_file_header.txt, metadata for lsl-files, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This 10-day backward trajectory dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Dataset: Does vendor breeding colony influence sign- and goal-tracking in Pavlovian conditioned approach?
<p>Vendor differences are thought to affect Pavlovian conditioning in rats. After observing possible differences in sign-tracking and goal-tracking behaviour with rats from different breeding colonies, we performed an empirical replication of the effect. 40 male Long-Evans rats from Charles River colonies ‘K72’ and ‘R06’ received 11 Pavlovian conditioned approach training sessions (or “autoshaping”), with a lever as the conditioned stimulus (CS) and 10% sucrose as the unconditioned stimulus (US). Each 58-min session consisted of 12 CS-US trials. Paired rats (n = 15/colony) received the US following lever retraction. Unpaired control rats (n = 5/colony) received sucrose during the inter-trial interval. Next, we evaluated the conditioned reinforcing properties of the CS, by determining whether rats would learn to nose-poke into a new, active (vs. inactive) port to receive CS presentations alone (no sucrose). Preregistered confirmatory analyses showed that during autoshaping sessions, Paired rats made significantly more CS-triggered entries into the sucrose port (i.e., goal-tracking) and lever activations (sign-tracking) than Unpaired rats did, demonstrating acquisition of the CS-US association. Confirmatory analyses showed no effects of breeding colony on autoshaping. During conditioned reinforcement testing, analysis of data from Paired rats alone showed significantly more active vs. inactive nosepokes, suggesting that in these rats, the lever CS acquired incentive motivational properties. Analysing Paired rats alone also showed that K72 rats had higher Pavlovian Conditioned Approach scores than R06 rats did. Thus, breeding colony can affect outcome in Pavlovian conditioned approach studies, and animal breeding source should be considered as a covariate in such work.Vendor differences are thought to affect Pavlovian conditioning in rats. After observing possible differences in sign-tracking and goal-tracking behaviour with rats from different breeding colonies, we performed an empirical replication of the effect. 40 male Long-Evans rats from Charles River colonies ‘K72’ and ‘R06’ received 11 Pavlovian conditioned approach training sessions (or “autoshaping”), with a lever as the conditioned stimulus (CS) and 10% sucrose as the unconditioned stimulus (US). Each 58-min session consisted of 12 CS-US trials. Paired rats (n = 15/colony) received the US following lever retraction. Unpaired control rats (n = 5/colony) received sucrose during the inter-trial interval. Next, we evaluated the conditioned reinforcing properties of the CS, by determining whether rats would learn to nose-poke into a new, active (vs. inactive) port to receive CS presentations alone (no sucrose). Preregistered confirmatory analyses showed that during autoshaping sessions, Paired rats made significantly more CS-triggered entries into the sucrose port (i.e., goal-tracking) and lever activations (sign-tracking) than Unpaired rats did, demonstrating acquisition of the CS-US association. Confirmatory analyses showed no effects of breeding colony on autoshaping. During conditioned reinforcement testing, analysis of data from Paired rats alone showed significantly more active vs. inactive nosepokes, suggesting that in these rats, the lever CS acquired incentive motivational properties. Analysing Paired rats alone also showed that K72 rats had higher Pavlovian Conditioned Approach scores than R06 rats did. Thus, breeding colony can affect outcome in Pavlovian conditioned approach studies, and animal breeding source should be considered as a covariate in such work.</p>
Months-long tracking of neuronal ensembles spanning multiple brain areas with Ultra-Flexible Tentacle Electrodes
<p>This dataset contains some of the raw and preprocessed data presented in the manuscript "Months-long tracking of neuronal ensembles spanning multiple brain areas with Ultra-Flexible Tentacle Electrodes" submitted to Nature Communications. Detailed information on each individual file is as follows: </p> <ul> <li><strong>256ch_device2_impedance_spectroscopy.csv:</strong> Impedance magnitudes presented in Fig. 2b.</li> <li><strong>rat1_impedances.csv:</strong> Impedance magnitudes belonging to Rat #1 (presented in Fig. 4c).</li> <li><strong>rat2_impedances.csv:</strong> Impedance magnitudes belonging to Rat #2 (presented in Fig. 4c).</li> <li><strong>MAX_TBY37_s1_n1_1776_1_12_hires_neuron.tif:</strong> Max. intensity projection image of Nissl staining shown in Fig. 4g. </li> <li><strong>MAX_TBY37_s1_n1_1776_1_12_hires_IBA.tif:</strong> Max. intensity projection image of IBA staining shown in Fig. 4g. </li> <li><strong>MAX_TBY37_s1_n1_1776_1_12_hires_GFAP.tif:</strong> Max. intensity projection image of GFAP staining shown in Fig. 4g. </li> <li><strong>AVG_TBY37_s1_n1_1776_1_12_neuron_4x4bins.tif: </strong>z-stack-averaged and binned image of Nissl staining used in histology analysis shown in Fig. 4g.</li> <li><strong>AVG_TBY37_s1_n1_1776_1_12_IBA_4x4bins.tif:</strong> z-stack-averaged and binned image of IBA staining used in histology analysis shown in Fig. 4g.</li> <li><strong>AVG_TBY37_s1_n1_1776_1_12_GFAP_4x4bins.tif:</strong> z-stack-averaged and binned image of GFAP staining used in histology analysis shown in Fig. 4g.</li> <li><strong>neuron_fluo_ds.npy: </strong>The downsampled sample points used in the histology analysis for Nissl staining (Fig. 4g). </li> <li><strong>gfap_fluo_ds.npy: </strong>The downsampled sample points used in the histology analysis for GFAP staining (Fig. 4g). </li> <li><strong>iba_fluo_ds.npy: </strong>The downsampled sample points used in the histology analysis for IBA staining (Fig. 4g). </li> <li><strong>256ch_device2_phase_spectroscopy.csv: </strong>Impedance phases presented in Supplementary Fig. 7a.</li> <li><strong>rat1_impedance_phases.csv: </strong>Impedance phases belonging to Rat #1 (presented in Supplementary Fig. 7b).</li> <li><strong>rat2_impedance_phases.csv:</strong> Impedance phases belonging to Rat #2 (presented in Supplementary Fig. 7b).</li> <li><strong>mouseLL2_impedances_magnitudes.csv:</strong> Impedance magnitudes presented in Supplementary Fig. 9a.</li> <li><strong>mouseLL2_impedances_phases.csv: </strong>Impedance phases presented in Supplementary Fig. 9b. </li> <li><strong>mouseLL2_single_unit_SNRs.csv: </strong>Single unit SNRs presented in Supplementary Fig. 9c.</li> <li><strong>mouseLL2_single_unit_lifetimes.csv: </strong>Single unit lifetimes presented in Supplementary Fig. 9d. </li> <li>Figure_5_data.mat: Data used in Figure 5 (can be imported into the corresponding Matlab script in the GitHub repository).</li> </ul> <p>The rest of the data supporting the figures is provided in the Source File and Supplementary Data files, which are available through the online version of the article. Any additional requests for information can be directed to, and will be fulfilled by, the corresponding author. </p>
An automatic fascicle tracking algorithm quantifying gastrocnemius architecture during maximal effort contractions
<p>This repository includes all the experimental data, tracking code, and tracked trials reported in Drazan JF, Hullfish TJ, Baxter JR. 2019. An automatic fascicle tracking algorithm quantifying gastrocnemius architecture during maximal effort contractions. <em>PeerJ</em> 7:e7120. DOI: <a href="https://doi.org/10.7717/peerj.7120">10.7717/peerj.7120</a>.</p> <p>Updated tracking code will be maintained on github https://github.com/joshrbaxter/ultrasound_tracking</p> <p>To get started - download the 'matlab' and 'Sample Videos' folders and unzip them into a common directory. If you are having path issues (will first appear when trying to pull the Data structure), then these folders are either in the wrong path or the path separators are incorrect (this was developed on Windows and linux/OSX use a different path format). </p>
THÖR - eye-tracking
<p><strong>THÖR</strong> is a dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for the position, head orientation, gaze direction, social grouping and goals. THÖR contains sensor data collected by a 3D lidar sensor and involves a mobile robot navigating the space. In comparison to other, our dataset has a larger variety in human motion behaviour, is less noisy, and contains annotations at higher frequencies.</p> <p><strong>THÖR eye-tracking -</strong> data of the participant (Helmet number 9) from the Tobii Glasses included in this dataset. The entire data from the experiment was recorded into two recordings - "Recording011" and "Recording012".</p> <p>The folder "RawData" consists of exported data from Tobii Pro Lab software using a filter called "Tobii-IVT Attention filter" (velocity threshold parameter set to 100 degrees/second), which is a recommended method for dynamic situations. The recommendation was given by the equipment manufactures Tobii Pro and from other researchers. For further information, please refer to https://www.tobiipro.com/siteassets/tobii-pro/user-manuals/Tobii-Pro-Lab-User-Manual/?v=1.86 (Appendix-B, page 85).</p> <p>The recording start times are as follows:<br> Tobii recording011 starttime: 13:34:37.267<br> Tobii recording012 starttime: 14:36:17.730</p> <p>1. "Synchronized_Qualisys_Tobii.mat" file, which consists of all the synchronised data between Qualisys data and Tobii eye-tracker data using timestamps matching. The columns (headers) in this mat file respectively represent - 'timestamp', 'Pos_X', 'Pos_Y', 'Pos_Z', 'Head_R', 'Head_P', 'Head_Y', 'GazepointX', 'GazepointY', 'Gazepoint3D_X', 'Gazepoint3D_Y', 'Gazepoint3D_Z', 'Gazedirectionleft_X', 'Gazedirectionleft_Y', 'Gazedirectionleft_Z', 'Gazedirectionright_X', 'Gazedirectionright_Y', 'Gazedirectionright_Z', 'Pupilpositionleft_X', 'Pupilpositionleft_Y', 'Pupilpositionleft_Z', 'Pupilpositionright_X', 'Pupilpositionright_Y', 'Pupilpositionright_Z', 'Pupildiameterleft', 'Pupildiameterright', 'Gazeeventduration', 'Eyemovementtype_index', 'Fixationpoint_X', 'Fixationpoint_Y', 'Gyro_X', 'Gyro_Y', 'Gyro_Z', 'Accelerometer_X', 'Accelerometer_Y', 'Accelerometer_Z'.</p> <p>2. A matlab script "Synchronizing_Qualisys_Tobii.m" used for matching the timestamps of Qualisys and Tobii and generate a data file "Synchronized_Qualisys_Tobii.mat" that can be found it git repository.</p> <p>3. All the associated data required for running the matlab script.</p> <p>Please note that 'Timestamp', 'Pos_X', 'Pos_Y', 'Pos_Z', 'Head_R', 'Head_P', 'Head_Y','GazepointX', 'GazepointY' represent the timestamps (matched to Tobii timestamps using nearest neighbor search), position and head orientation from Qualisys data and rest of the data is from Tobii eye-tracker. For more information regarding the eye-tracker data, please refer to https://www.tobiipro.com/siteassets/tobii-pro/user-manuals/Tobii-Pro-Lab-User-Manual/?v=1.86 (Section 8.7.2.1, page 68).</p> <p> </p>
Unverified GLONASS track of R/V Akademik Tryoshnikov during the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>A Global Navigation Satellite System (GLONASS) recorded the route undertaken by the R/V Akademik Tryoshnikov during a circumnavigation of the Antarctic as part of the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016/2017.</p> <p>The data provided in this dataset are raw NMEA strings containing date, time, latitude and longitude, with other NMEA variables allowing the accuracy of the location to be ascertained with one-second resolution. The data have not been quality checked or corrected.</p> <p><strong>Dataset contents</strong></p> <ul> <li>gpsdata-YYYYMMDD.log, data file, text</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p>Data files include the date (in UTC) on which the data were recorded in the format YYYYMMDD.</p> <p><strong>Dataset license</strong></p> <p>This uncorrected cruise track dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Intermediate processing steps of quality-checking of Antarctic Circumnavigation Expedition (ACE) cruise track data.
<p><strong>Dataset abstract</strong></p> <p>The Antarctic Circumnavigation Expedition (ACE), undertaken in the austral summer of 2016/2017 recorded the cruise track using two independent geo-location instruments: one using GLobal NAvigation Satellite Systems (GLONASS; hereafter referred to as GLONASS) and another primarily using the Global Positioning System (GPS; hereafter referred to as the Trimble GPS). Daily log files were recorded in real-time from both instruments during the expedition and added to MySQL database tables. Following the expedition, quality-checking work has been undertaken to provide a one-second resolution set of positions for the cruise track. This dataset presents the intermediate files that were produced during the quality-checking, therefore it could be used to check the processing steps that have been undertaken, but should not be used as a final source of the cruise track data. Both the original raw data files and final quality-checked cruise track can be found in related datasets.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_INSTRUMENT_YYYY-MM-DD.csv – daily files for each instrument that were output from the database, data file, comma-separated values</li> <li>ace_INSTRUMENT_concatenated_YYYY-MM.csv – input files concatenated by month and instrument, data file, comma-separated values</li> <li>flagging_data_ace_INSTRUMENT_YYYY-MM-DD.csv – daily output files for each instrument with flagged data points, data file, comma-separated values</li> <li>track_data_combined_overall_flags_YYYY-MM.csv – instrument data combined with overall data flag for each month, data file, comma-separated values</li> <li>track_data_prioritised_YYYY-MM.csv – prioritised data files with overall data flag for each month, data file, comma-separated values</li> <li>ace_INSTRUMENT_manual_position_errors.csv – files containing the manually-observed errors, metadata, comma-separated values</li> <li>in_port.csv - dates on when the ship was stationary in port, metadata, comma-separated values</li> <li>README.txt – metadata, text file</li> <li>data_file_header.txt – metadata, text file</li> </ul> <p><strong>Dataset license</strong></p> <p>This dataset containing intermediate processing files of the ACE cruise track is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Quality-checked, one-minute resolution cruise track of the Antarctic Circumnavigation Expedition (ACE) undertaken during the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>The Antarctic Circumnavigation Expedition (ACE), undertaken in the austral summer of 2016/2017 recorded the cruise track using two independent geo-location instruments: one using GLobal NAvigation Satellite Systems (GLONASS; hereafter referred to as GLONASS) and another primarily using the Global Positioning System (GPS; hereafter referred to as the Trimble GPS). Daily log files were recorded in real-time from both instruments during the expedition and added to MySQL database tables. Following the expedition, quality-checking work has been undertaken to provide a one-second resolution set of positions for the cruise track. Here we present the final quality-checked dataset aggregated to a resolution of one minute. This is of use for understanding the position of the vessel to a lower precision.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_cruise_track_1min_YYYY-MM.csv, data file, comma-separated values</li> <li>README.txt, metadata, text file</li> <li>data_file_header.txt, metadata, text file</li> </ul> <p><strong>Dataset license</strong></p> <p>This quality-checked cruise track dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots
<h1>Dataset and code description</h1> <p>This repository contains the codes and data for theScience Robotics paper <strong>Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots</strong>.</p> <p>The codes are in <strong>rr_scirob_analyses</strong> and the datasets are in <strong>rr_scirob_data</strong>.<strong> </strong>If you want to rerun the data processing as presented in the paper, you need both <strong>rr_scirob_analyses</strong> and <strong>rr_scirob_data. </strong>You can copy the contents of <strong>rr_scirob_data </strong>into <strong>rr_scirob_analyses, </strong>as they have the same folder structure. Alternatively, you can run the <strong>download </strong>scripts to obtain the partial datasets relevant for certain subfigures. The file <strong>rr_scirob_data_readmes</strong> contains more detailed README files (rosbag info). You can copy its contents to <strong>rr_scirob_analyses </strong>after copying the contents of the <strong>rr_scirob_data</strong>.</p> <p>The individual datasets are organised into seven folders.</p> <h2>Three Figures with Key Behavioural Metrics </h2> <p>Three of the folders correspond to the Key Behavioural Measures, which are presented in three figures in the paper. These are:</p> <ul> <li>Figure-2-KBM-1-Queen Queen - related Key Behavioural Metrics</li> <li>Figure-3-KBM-2-Workers Worker Bee - related Key Behavioural Metrics</li> <li>Figure-4-KBM-3-Comb Comb and Brood -related Key Behavioural Metrics </li> </ul> <p>Each of these <em>Figure-X</em> folders contains the relevant figure from the paper and four subfolders corresponding to the panels of that figure. These are <strong>macro</strong>, <strong>micro</strong>, <strong>mezo</strong>, <strong>social</strong>, related to the four panels of that figure.<br>Each of these subfolders contains a README file, describing how to process the data and providing further details. <br>Furthermore, there are three additional folders located in each of the 'panel' folder:</p> <ul> <li><strong>data</strong>: this is used to store the data necessary to generate the graphs. You can either populate it with the data from Zenodo, i.e., https://zenodo.org/records/13801588 Alternatively, you can use the `download.sh` script wich will download and extract the necessary data from the RoboRoyale project cloud.</li> <li><strong>tmp</strong>: This folder is used to store intermediate results of the processing scripts</li> <li><strong> output</strong>: This folder is used to store all the generated outputs of the individual scripts. These should be identical with the panels of the figure in the paper. These figures are also provided in the relevant folders.</li> </ul> <p>Running the scripts contained in the micro, mezo, macro and social folders generates images and graphs in the output subfolders. These should be identical to the ones in the panels of Figures 2-4 in the paper.</p> <h2>One Resting Analysis Figure</h2> <p>One folder corresponds to the queen resting analysis figure</p> <ul> <li>Figure-5-Resting : Queen resting time analysis</li> </ul> <p>This folder has three subfolders named <strong>data</strong>, <strong>tmp</strong> and <strong>output</strong> similar to the previous folders. Again, running the scripts will generate the figures and/or run the statistical tests as in the previous case.</p> <h2>Three Performance Assessments: Queen Tracking, Workerbee Localisation and Oviposition Detection</h2> <p>Three other folders are related to performance analysis of the core methods required to calculate the KBMs.</p> <ul> <li>KBM-1-performance evaluation: Provides datasets and scripts to assess the performance of the queen marker detector</li> <li>KBM-2-performance evaluation: Provides datasets and scripts to assess the performance of the worker bee detector</li> <li>KBM-3-performance evaluation: Provides datasets and scripts to assess the performance of the oviposition detector </li> </ul> <p>Each of these folders contains a README file explaining what to run in order to evaluate the performance of the method and to replicate the paper's results.</p> <h2>Additional materials and data</h2> <p>The core data used here is the month-long queen tracking information, consisting of 28 million entries in a file <strong>2023-month-queenpos-short.txt.</strong> <br>A description of the file structure is provided in the README of the relevant KBM folder.</p> <p>Additional data are available in the dataset section of https://roboroyale.eu.</p> <h2>Rosbags</h2> <p>The work is based on the Robot Operating System (ROS) and thus, the raw data come in the form of rosbags. We provide a few of the rosbags to allow checking examples of video and other raw data as reported by the system:</p> <ul> <li>2023-10-25-08-42-20-Queen-Feeding.bag - queen feeding (KBM-1 Social)</li> <li>KPI1_2_mezo-queen_walk_sample.bag - queen walk as drawn in (KBM-1 Mezo)</li> <li>2023-10-10-00-04-10-trophylaxis.bag - worker bee trophylaxis (KBM-2 Social)</li> <li>2023-09-19-09-00-20-egg-removal.bag - worker bee removing egg (KBM-2 Social)</li> </ul> <h2>Licence </h2> <p>This data and code are under the Creative Commons Attribution-ShareAlike 4.0 International license. If you use these data in your work, please <strong>cite</strong> the relevant paper, i.e., Ulrich, Stefanec, Rekabi-bana et al.: <strong>Autonomous tracking of honeybee behaviors over long-term periods with cooperating robots</strong>. Science Robotics, 2024.</p> <p> </p>
Graphic Illustration of our Digital Collections Data and Tracking Disease Workshop Session: Discussion and Synthesis
<p>Karina Branson of <a href="https://www.conversketch.com/" target="_blank" rel="noopener">ConverSketch</a>, graphically recorded and helped to facilitate this Discussion section of our NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Graphic Illustration of Talks in our Digital Collections Data and Tracking Disease Workshop Section: Case Studies
<p>Karina Branson of <a href="https://www.conversketch.com/" target="_blank" rel="noopener">ConverSketch</a>, graphically recorded and helped to facilitate this Case Studies section of our NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Graphic Illustration of Talks in our Digital Collections Data and Tracking Disease Workshop Section: Museum Perspectives
<p>Karina Branson of <a href="https://www.conversketch.com/" target="_blank" rel="noopener">ConverSketch</a>, graphically recorded and helped to facilitate this Museum Perspectives section of our NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Monsoon low-pressure-system tracks over South Asia (1979-2019)
<p>Monsoon LPS tracks over South Asia, computed using ERA-Interim reanalysis data.<br> Tracking algorithm described in Hunt and Fletcher (2018) [doi:10.1007/s00382-019-04744-x] and Hunt <em>et al.</em> (2016) [doi:10.1175/MWR-D-15-0138.1].<br> <br> Description of fields:<br> <em>point_id</em>: unique integer identifier for each point<br> <em>time</em>: string with format DD/MM/YY HH:MM, denoting time of detected track point<br> <em>lon</em>: longitude of detected track point<br> <em>lat</em>: latitude of detected track point<br> <em>track_id</em>: unique integer identifier for each track (constituting a group of points)<br> <em>vort</em>: relative vorticity at 850 hPa for the given point (units: s<sup>-1</sup>)<br> <em>circulation</em>: vorticity integrated over the blob of positive vorticity containing the track point (units: arb)<br> <em>eccentricity</em>: eccentricity of the vorticity blob containing the track point<br> <em>category</em>: integer from 0-6 denoting the category of the LPS at the given point, approximately matching IMD criteria. 0: low-pressure area, 1: deep low-pressure area, 2: depression, 3: deep depression, 4: cyclonic storm, 5: severe cyclonic storm, 6: very severe cyclonic storm (and above).</p>
Database for RailRad calculation method for simulating sound radiated by railway track vibrations
<p>This dataset contains precalculated acoustic transfer functions for efficiently calculating the sound radiated by railway track vibrations.</p> <p>The transfer functions contained in each file describe the complex sound pressure produced at a number of receiver locations given a unit velocity at a source element on the railway track surface, per frequency and at a fixed wavenumber along the track.</p> <p>Four different acoustic geometries are included: (1) a standard UIC60 rail in free space, (2) the rail in an acoustic half space, (3) the rail located above a slab track surface, and (4) identical geometry to (3) but including an acoustically hard hull of a passenger train geometry above the track.</p> <p>More information about the exact location of source and receiver coordinates can be found in the .hdf5 files, in the subgroup 'info'. The transfer functions themselves are located in the dataset 'tfs', which are matrices of size (Number of frequency lines x number of sources x number of receivers).</p> <p>More information can be found here https://github.com/janniktheyssen/railrad</p> <p>This collection of databases is part of ongoing work at CHARMEC / Chalmers University of Technology, Gothenburg, Sweden (https://www.charmec.chalmers.se/). Parts of the study have been funded from the European Union's Horizon 2020 research and innovation programme in the In2Track3 project under grant agreements No 101012456. The computations were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council through grant agreement no. 2018-05973.</p>
X-ray radiography 4D particle tracking of heavy spheres suspended in a turbulent jet
<p>This database report 3d trajectories of heavy spheres suspended in a turbulent upward jet. A cylindrical tank is filled with water and the jet nozzle is placed on its axis on the bottom wall, and a constant flowrate (Q) of water is fed through the nozzle. Conditions at 1700 and 2200 mL/min are considered, and the number of spheres is varied between 1 and 12 (Nsphere). The spheres are glass and are detected using X-ray radiography at 60Hz. The 4d kinematics are obtained with this setup using radioSphere (E. Ando et<br> al., Measurement Science and Technology, 32(9), 095405, 2021). Each condition has a series of files named based on the number of spheres in the tank Nsphere and the flowrate Q, with each sphere of index isphere having its own file. Each file is 3 columns of doubles representing the 3d coordinates x, y, and z of the sphere, in mm, where z is the axis of the cylinder and the points up, against gravity.</p> <p>Results from this database are published here: https://doi.org/10.1016/j.ijmultiphaseflow.2023.104406<br> O. Stamati, B. Marks, E. Ando, S. Roux, N. Machicoane, X-ray radiography 4D particle tracking of heavy spheres suspended in a turbulent jet, <em>International Journal of Multiphase Flow</em> 162, 104406, 2023.</p>
ScienceDex guides
Understand access before you commit
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.