Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
559
datasets available to search
ShareScore release 0.9.0
Dataset results
559 results for “Track data”
Data from: Tracking migration of black-headed buntings Emberiza melanocephala reveals the Iranian Plateau as an ecological barrier along the Indo-European flyway
<p><strong>Abstract</strong></p> <p>The black-headed bunting is one of the few species that migrate along the Indo- European flyway, and its migration routes and phenology are poorly understood. We provide the first individual-based year-round tracking study describing route choice and timing of black-headed buntings migrating from a breeding site in Croatia to a 6000 km distant non-breeding region in Maharashtra, Central India. To evaluate landscapes important for the black-headed bunting migration, we quantified land covers and climates along the migration corridor. Stopover areas during the post-breeding migration were concentrated in four distinct regions: the Balkan Peninsula, central Anatolia, along with the Zagros Mountain range and from the Indus River delta to the Kathiawar Peninsula. Post-breeding migration routes followed the shortest path connecting breeding and non-breeding sites, except for the initial detour from the breeding sites to the first stopovers at the Balkan Peninsula. The pre-breeding migration routes occur along the Arabian Peninsula, about 1000 km south of the post-breeding routes – creating a clockwise loop migration pattern. Post-breeding migration lasted about two months, whereas pre-breeding migration was almost two times shorter, conforming to energy- and time-minimisation strategies, respectively. During the postbreeding migration, birds seem to track ecological niches found on their breeding grounds. Post-breeding stopover areas were rich in mosaic lands and were in warm and dry climates of Mediterranean character, while forests and bare areas, as well as arid and humid climates, were avoided.</p> <p> </p>
A New Tracking Algorithm Based on Foshan Total Lightning Data and the Influence of Historical Velocity and Matching Method on the Results
<p>This paper introduces a new tracking algorithm (V2CM) based on total lightning data from the Foshan Total Lightning Location System (FTLLS), and the other four algorithms are used to test the influence of historical velocity and matching method on the results during the formation of V2CM. Different methods are used to quantitatively evaluate the performance of five tracking methods. Additionally, the number of splits and merges along a track is used as a new index to describe the complexity of the track. It is found that the best tracking method V2CM has the highest probability of detection (POD), the false alarm rate (FAR) and the critical success index (CSI) being 65.6%, 39.2%, 46.4%, respectively. It also produces longer and more coherent tracks than those of other four methods. The average number of splits and merges of the best method is 2.40 and lightning clusters matching method between two successive time intervals has a greater impact on the evaluation results than the historical velocity. In addition, lightning clusters move in the northeast direction, with a mean speed of 48.48 km/h and a median speed of 39.08 km/h. Complex tracks, involving splitting or merging, move faster than simple track.</p>
Detection of the Fire Drill anti-pattern: 15 real-world projects with ground truth, issue-tracking data, source code density, models and code
<p>This package contains artifacts for <strong>15</strong> real-world software projects. The data is supposed to aid the detection of the presence of the Fire Drill anti-pattern. We include original data, ground truth, code (experimental setups and models), and notebooks. The data supports two distinct methods of detecting the AP: a) through issue-tracking data, and b) through the underlying source code. This version of the dataset corresponds to <strong>v8</strong> of the <a href="https://arxiv.org/abs/2104.15090v8">technical report</a> and the <a href="https://github.com/MrShoenel/anti-pattern-models/releases/tag/arxiv-v8">GitHub repository</a>. The package includes the following:</p> <p>Original data:</p> <ul> <li>For each project, its <strong>original</strong> artifacts (e.g., wikis, meeting minutes, mentor's notes, etc.)</li> <li>Evaluation of raters' notes by the assessor</li> </ul> <p>Fire Drill in issue-tracking data:</p> <ul> <li><strong>Ground truth</strong> for whether and how strong each project exhibits the Fire Drill AP, on a scale from [0,10]. This was determined by two individual raters, who also reached a consensus.</li> <li>Coefficients for indicators for the first method, per project.</li> <li>Detailed issue-tracing data for each project: what occurred and when.</li> <li>Time logs for each project.</li> </ul> <p>Fire Drill in source-code data:</p> <ul> <li><strong>Four</strong> technical reports that document the developed method of how to translate a description into a detectable pattern, and to use the pattern to detect the presence and to score it (similar to the rating). Also includes a report for how activities were assigned to individual commits.</li> <li>Source code density data (metrics) for each commit in each of the nine projects as a separate dataset.</li> <li>Code: a snapshot of the repository that holds all code, models, notebooks, and pre-computed results, for utmost reproducibility (the code is written in R).</li> </ul>
Sequencing data for "Identifying and tracking mobile elements..." by van Dijk et al. (2023)
<p>The dataset contains the following directories:</p> <p><strong>All_raw_MAGs</strong>: all cross-assembled MAGs per community, with a text file for their BAT annotation results. Cross-assembly was done by combining all time points. <br> <strong>Candidatus_Saccharibacterium_MAGs</strong>: identified nanobacterium MAGs from various communities. <br> <strong>Cellvibrio_MAGs</strong>: identified cellvibrio spp. across communities. <br> <strong>Cellvibrio_Plasmids:</strong> Cp plasmids belonging to Cellvibrio (C1) which transferred from communities 8 to 4, 6, 9, and 10 (in horizontal communities)<br> <strong>Interactive_Dataset:</strong> HTML5 (Plotly) graphs to explore all xenotypic sequences and MAG abundance plots<br> <strong>Rscript_mock_data</strong>: R script used to design the mock data for the benchmark set. </p> <p>And a single spreadsheet (<strong>Xenoseq_mastersheet.xlsx, </strong>Supplementary Table 1) of data discussed in the main text.</p>
eye-tracking data from a survey on zooming in a pan-scalar map
<p><strong>Recording and processing a survey using an eye tracker </strong></p> <p>The eye-tracker used is a Pupil Core from Pupil Labs. The basic eye tracker configuration, i.e. a fixation time of 80 ms to 200 ms, is kept for this experiment.</p> <p> </p> <p>The aim of the experiment is to understand what a person looks at to find their way around a multi-scale map and to understand the different strategies used. To do this, the user will be free to use the map as he wishes, i.e. he can use pan and zoom at will. Four types of tasks will be asked in order to have a maximum of types of use of multi-scale map.The first task is to simulate that a user is using an application like map or Google map and is looking for a specific address. The map application will then zoom in very strongly on the address. The user has little spatial context and it often takes some time to find his way around. To simulate the application, a point is placed on Paris or its surroundings and the display is very zoomed (Paris was chosen because most people have a more or less detailed mental map of Paris). The user is then asked to interact with the map (zooming and panning) until he feels he is sufficiently located, as he would if he had to search for a place on his mobile phone. When he is located, he just needs to move on to the next stage without asking for validation. This stage is carried out in four locations. The four points are located near Montmartre, at the entrance to the catacombs of Paris, in Vincennes and finally at Porte d'Asnières</p> <p><br> The second task is to find a place from an aerial image. The aerial image of a specific area is displayed and the map is zoomed out to the city where the location is located. The user must then try to find the location in the image. Unlike the first task, the user must request validation before proceeding to the next stage.<br> This task is repeated in two different cities. The two images are the tête d'or park in Lyon and a building block next to a railway in Dijon.</p> <p><br> The third task also consists of finding a precise location using textual indications. The user still has to ask for validation to go to the next stage .</p> <p>This task is repeated in two different cities.The first was "to find the town hall which is just south of the town centre and next to the library" and the second was "to find the stadium east of the town centre and north of the river Vilaine with a north/south orientation.</p> <p><br> The last task builds on tasks 2 and 3. The map is again zoomed out, an aerial image appears and textual indications are given. This task is repeated on two different cities.</p> <p>The first image is of a building in beauvais with the indication: "the building is in the north west of sqare next to the SNCF station". The second one is a picture of a stadium in lyon with the indication: "the stadium is west of the confluence of lyon".</p> <p><strong>data format :</strong><br> <strong>Coord_fixation_on_map_x_y</strong>: geolocated fixation point with x the survey type 1 or 2 and y the candidate number (id_fixation,x,y,zoom,etape)</p> <p><strong>Pan</strong>: pan on the map during the survey</p> <p><strong>Pan_fixation_on_map</strong> : fixation during a pan</p> <p><strong>zoom</strong>: zoom on the map during the survey</p> <p><strong>zoom_fixation_on_map</strong>: fixation during a zoom</p> <p><strong>stat</strong>: number of zoom, pan and fixation per step</p> <p><strong>result_map_x </strong>= map status every 100 ms during the survey x</p> <p><strong>00x </strong>: export file of the eye-tracker pupil Lab</p> <p> </p>
Data for "Tracking physical delivery of electricity from generators to loads with power flow tracing"
<p>Data and analysis code for "Tracking physical delivery of electricity from generators to loads with power flow tracing".</p> <p>Data includes the following folders:</p> <ul> <li>validation: Ground truth generation data for 2020</li> <li>tracing: Power flow tracing results for one day</li> <li>secondary_analysis: Node-level power flow statistics</li> <li>networks: Solved networks, including Eastern U.S., Europe, Texas at 4 levels of clustering, and Europe with altered dispatch</li> <li>meta: between-node straight line distance for Eastern U.S. and Europe</li> <li>boundaries: shapefiles and per-node mappings for existing boundaries (Eastern U.S. and Europe) and example boundaries (Texas)</li> <li>analysis: physical delivery and deliverability metrics for all networks over all modeled days</li> </ul> <p>Code (`flow-delivery-analysis-main`) includes notebooks and helper functions for all visualizations and statistics in "Tracking physical delivery of electricity from generators to loads with power flow tracing". See the README for more information.</p>
Data for "Tracking the as yet unknown nearfield evolution of a shallow, neutrally-buoyant plane jet over a sloping bottom boundary"
<p>This file gives the datasets of our manuscript "Tracking the as yet unknown nearfield evolution of a shallow, neutrally-buoyant plane jet over a sloping bottom boundary".</p>
Data Used for the Publication "Use of Threshold Parameter Variation for Tropical Cyclone Tracking"
<p>Data used to produce the figures in "Use of Threshold Parameter Variation for Tropical Cyclone Tracking," as submitted to GMD.</p>
Data set for "Tracking the as yet unknown nearfield evolution of a shallow, neutrally-buoyant plane jet over a sloping bottom boundary"
<p>Data set for the paper titled "Tracking the as yet unknown nearfield evolution of a shallow, neutrally-buoyant plane jet over a sloping bottom boundary".</p>
Collection of The tropical cyclones best-track data (CMABSTdata) and the daily surface climatological stations dataset for China (V3.0) from CMA
<p>This dataset contains the tropical cyclones best-track data from the China Meteorological Administration (CMABSTdata)<strong>[1] </strong>and the daily surface climatological stations dataset for China (V3.0)<strong>[2] </strong>from 1979-2019 from China Meteorological Administration.</p> <p>[1] Y<span>ing, M., Zhang, W., Yu, H., Lu, X., Feng, J., Fan, Y., . . . Chen, D. (2014). An overview of the China Meteorological Administration tropical cyclone database. <em>Journal of Atmospheric and Oceanic Technology, 31</em>(2), 287-301. </span></p> <p>[2]<span>(CMA, http://data.cma.cn)</span></p>
antGait program file for speed estimation of ants and ant tracking data
<p>This is the MATLAB code containg the method of ant speed estimation based on the gait pattern of ants. It also contains the dataset of recorded ant trajectories, artificial noise applied to the trajectories, and the simulated trajectory analysis results.</p>
The Bowel Research Collaborative Data Tracking System Implementation Study
ClinicalTrials.gov study NCT00728975. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Do cost functions for tracking error generalize across tasks with different noise levels?
Open the record for dataset details and reuse information.
Data from: Three dimensional tracking of a wide-ranging marine predator: flight heights and vulnerability to offshore wind farms
Open the record for dataset details and reuse information.
Data from: Predicted tracking error triggers catch-up saccades during smooth pursuit
Open the record for dataset details and reuse information.
Data from: Tracking trends in monarch abundance over the 20th century is currently impossible using museum records
Open the record for dataset details and reuse information.
Data from: Lake sediment multi-taxon DNA from North Greenland records early post-glacial appearance of vascular plants and accurately tracks environmental changes
Open the record for dataset details and reuse information.
Data from: Territory surveillance and prey management: wolves keep track of space and time
Open the record for dataset details and reuse information.
Data from: Small-mammal isotope ecology tracks climate and vegetation gradients across western North America
Open the record for dataset details and reuse information.
Data from: The chemical basis of a signal of individual identity: Shell pigment concentrations track the unique appearance of Common Murre eggs
Open the record for dataset details and reuse information.
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.