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”
Uruguayan candombe drumming - beat and downbeat tracking data set
<p>Uruguayan Candombe drumming - beat and downbeat data set</p> <p><br>===========<br>Description<br>===========</p> <p>This dataset includes more than 2 hours of Candombe recordings, with annotated beats and downbeats. It features 35 complete performances by renowned players, playing in groups of three to five drums. A total of 26 tambor players from various generations took part, representing the three key traditional Candombe styles. These recordings were created in a studio setting over a period of more than two decades as part of musicological research. The sessions from 1992 and 1995 were produced by Luis Jure, and the session from 2014 was produced by Luis Jure and Martín Rocamora.</p> <p>The audio files are in stereo with a sampling rate of 44.1 kHz and 16-bit precision. An expert annotated the location of beats and downbeats, totaling more than 4700 downbeats. The audio is provided in flac format and the annotations are in .csv files. The values in the first column of the CSV file represent the time instants of the beats. The numbers in the second column indicate both the bar number and the beat number within the bar. For example, 1.1, 1.2, 1.3, and 1.4 represent the four beats of the first bar. Therefore, each label ending in .1 indicates a downbeat. Another set of annotations is provided as .beats files in which the bar numbers are removed.</p> <p><br>========<br>Citation<br>========</p> <p>This dataset was released with the publication of the following paper. So if you find the dataset useful and want to reference it in your publications, please cite it.</p> <p>"Beat and Downbeat Tracking Based on Rhythmic Patterns Applied to the Uruguayan Candombe Drumming”. Leonardo Nunes, Martín Rocamora, Luis Jure, Luiz W. P. Biscainho. Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015), pages 264-270, Málaga, Spain, 26-30 October, 2015.</p> <p>@inproceedings{Nunes2015,<br> author = {Leonardo Nunes and Martín Rocamora and Luis Jure and Luiz W. P. Biscainho},<br> title = {{Beat and Downbeat Tracking Based on Rhythmic Patterns Applied to the Uruguayan Candombe Drumming}},<br> booktitle = {Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR 2015)},<br> month = {Oct.},<br> address = {Málaga, Spain},<br> pages = {264--270},<br> year = {2015}<br>}</p> <p><br>========<br>Candombe<br>========</p> <p>Candombe is a vital part of Uruguayan popular culture, with thousands of practitioners and its rhythm influencing various genres of popular music. In 2009, UNESCO recognized it as part of the Intangible Cultural Heritage of Humanity. While it originated in Uruguay, Candombe has its roots in the culture brought by African slaves in the 18th century. Over time, it has evolved to incorporate the descendants of European immigrants and has become a part of the entire society. Candombe drumming, with its unique rhythm, is the essential element of this tradition, which also includes dancing, symbolic characters, and costumes.</p> <p>The drum used in Candombe is called tambor, which is Spanish for "drum." There are three different sizes: chico (small), repique (medium), and piano (big). Each size has its own unique sound, ranging from high to low frequency, and its own specific rhythmic patterns. All three drums are played with a stick in the dominant hand and the other one hitting the skin directly. The stick is also used to hit the shell when playing the clave or madera pattern. A minimal ensemble of drums (cuerda de tambores) must have at least one of each of the three drums. During a llamada de tambores, the ensemble usually consists of around 20 to 60 drums. When marching, the players walk forward with short steps synchronized with the beat, which is important for embodying the rhythm, even though it is not audible.</p> <p>===============<br>Acknowledgments<br>===============</p> <p>This work was partially supported by the funding agency Comisión Sectorial de Investigación Científica, Universidad de la República, Uruguay, and by the Department of Culture of the Municipality of Montevideo.</p> <p>This is the complete list of performers, in alphabetical order: Mariano Barroso, Eduardo 'Cacho' Giménez, Eduardo 'Malumba' Giménez, Francisco Giménez, José Luis Giménez, Jorge 'Foqué' Gómez, José Pedro 'Perico' Gularte, Luis 'Pocholo' Maciel, Julio Magariños, Raúl 'Neno' Magariños, Marcelo Magariños, Javier 'Cerdo' Martirena, Wilson Martirena, Eduardo 'Tierra' Nilo, Sergio Ortuño, Fernando 'Lobo' Núñez, Edinson 'Palo' Oviedo, Gustavo Oviedo, Egdardo Pintos, Luis 'Mocambo' Quiroz, Rodolfo 'Pelado' Rodríguez, Fernando 'Hurón' Silva, Juan Silva, Raúl Silva, Waldemar 'Cachila' Silva, and Héctor Manuel Suárez.</p>
Data from: Hidden in plain sight: migration routes of the elusive Anadyr bar-tailed godwit revealed by satellite tracking
<p><strong>Abstract</strong></p> <p>Satellite and GPS tracking technology continues to reveal new migration patterns of birds which enables comparative studies of migration strategies and distributional information useful in conservation. Bar-tailed godwits in the East Asian–Australasian Flyway <em>Limosa lapponica baueri </em>and <em>L. l. menzbieri</em> are known for their long non-stop flights, however these populations are in steep decline. A third subspecies in this flyway, <em>L. l. anadyrensis</em>, breeds in the Anadyr River basin, Chukotka, Russia, and is morphologically distinct from <em>menzbieri</em> and <em>baueri</em> based on comparison of museum specimens collected from breeding areas. However, the non-breeding distribution, migration route and population size of <em>anadyrensis </em>are entirely unknown. Among 24 female bar-tailed godwits tracked in 2015–2018 from northwest Australia, the main non-breeding area for <em>menzbieri</em>, two birds migrated further east than the rest to breed in the Anadyr River basin, i.e. they belonged to the <em>anadyrensis </em>subspecies. During pre-breeding migration, all birds staged in the Yellow Sea and then flew to the breeding grounds in the eastern Russian Arctic. After breeding, these two birds migrated southwestward to stage in Russia on the Kamchatka Peninsula and on Sakhalin Island en route to the Yellow Sea. This contrasts with the other 22 tracked godwits that followed the previously described route of <em>menzbieri</em>, i.e. they all migrated northwards to stage in the New Siberian Islands before turning south towards the Yellow Sea, and onwards to northwest Australia. Since the Kamchatka Peninsula was not used by any of the tracked <em>menzbieri</em> birds, the 4 500 godwits counted in the Khairusova–Belogolovaya estuary in western Kamchatka may well be <em>anadyrensis</em>. Comparing migration patterns across the three bar-tailed godwits subspecies, the migration strategy of <em>anadyrensis </em>lies between that of <em>menzbieri </em>and <em>baueri</em>. Future investigations combining migration tracks with genomic data could reveal how differences in migration routines are evolved and maintained.</p> <p> </p> <p><strong>Data set</strong></p> <p>Stopping sites and migration timing of satellite-tracked bar-tailed godwits in the East Asian-Australasian Flyway</p> <p>file name: Chan et al. 2022 BARG_Stops_Timing.xlsx</p> <p>The sheet 'stopping_sites' contains stopping sites of bar-tailed godwits tracked with solar Argos satellite transmitters, and their respective arrival and departure times at each site. The sheet 'timing' contains departure and arrival times at the non-breeding and breeding sites in 2017. The transmitters were deployed in Roebuck Bay and Eighty Mile Beach, Australia, and were operating on an 8 h on and 25 h off duty cycle. </p> <p> </p> <p>Measurements of satellite-tracked bar-tailed godwits in the East Asian-Australasian Flyway</p> <p>file name: Chan et al. 2022 BARG_measurements.csv</p> <p>The datafile contains bill, wing and tarsus lengths and sex of bar-tailed godwits tracked with solar Argos satellite transmitters. The birds were captured in Roebuck Bay and Eighty Mile Beach, Australia. </p> <p> </p> <p><strong>Journal Article</strong></p> <p>Chan, Y.-C., Tibbitts, T. L., Dorofeev, D., Hassell, C. J. and Piersma T. (2022) Hidden in plain sight: migration routes of the elusive Anadyr bar-tailed godwit revealed by satellite tracking. J Avian Biol e02988. <a href="https://doi.org/10.1111/jav.02920">https://doi.org/10.1111/jav.02988</a></p>
Data from: Fundamental study of multi-track friction surfacing deposits for dissimilar aluminum alloys with application to additive manufacturing
<p>This dataset contains the data for the publication "Fundamental study of multi-track friction surfacing deposits for dissimilar aluminum alloys with application to additive manufacturing".</p>
WRF 3.5km North Atlantic August - October 2020 (TC tracks and atmospheric composites data)
<p>This zip file contains simulated TC tracks and some atmospheric composites for August to October 2020.</p> <p>- TC tracks information: timing, intensity, and location</p> <p>- Composites: vertical wind shear (200 - 850 hPa), potential intensity, 850 hPa absolute vorticity, and 700 hPa specific humidity.</p>
Data associated with Versatile Multiple Object Tracking in Sparse 2D/3D Videos via Deformable Image Registration (2024)
<p>This includes a volumetric whole-brain calcium recording of a freely behaving worm (<em>C. elegans</em>) captured at 4 Hz with tracked fluorescent neuronal nuclei, used to demonstrate the performance of a multi-object tracking algorithm (ZephIR) described in the associated publication.</p>
Data from: A cerebellar granule cell–climbing fiber computation to learn to track long time intervals
<p>In classical cerebellar learning, Purkinje cells (PkCs) associate climbing fiber (CF) error signals with predictive granule cells (GrCs) active just prior (~150ms). Cerebellum also contributes to behaviors characterized by longer timescales. To investigate how GrC-CF-PkC circuits might learn seconds-long predictions, we imaged simultaneous GrC-CF activity over days of forelimb operant conditioning for delayed water reward. As mice learned reward timing, numerous GrCs developed anticipatory activity ramping at different rates until reward delivery, followed by widespread time-locked CF spiking. Relearning longer delays further lengthened GrC activations. We computed CF-dependent GrC→PkC plasticity rules, demonstrating that reward-evoked CF spikes sufficed to grade many GrC synapses by anticipatory timing. We predicted and confirmed that PkCs could thereby continuously ramp across seconds-long intervals from movement to reward. Learning thus leads to new GrC temporal bases linking predictors to remote CF reward signals—a strategy well-suited to learn to track long intervals common in cognitive domains.</p>
Dataset for 'The State of Data Curation at NeurIPS: An Assessment of Dataset Development Practices in the Datasets and Benchmarks Track'
<p>This packages comprises of analyses and evaluations of 60 datasets from the NeurIPS Datasets and Benchmarks track. It is part of a paper currently under review at the 2024 the NeurIPS Datasets and Benchmarks track, titled, "The State of Data Curation at NeurIPS: An Assessment of Dataset Development Practices in the Datasets and Benchmarks Track". </p>
Supplementary Data for "Combining genomics and epidemiology to track mumps virus transmission in the United States"
<p>Supplementary data and scripts used to produce figures for "Combining genomics and epidemiology to track mumps virus transmission in the United States."</p> <p>Files are divided into "code" and "data" directories and a README is provided for each.</p>
Pioneer 11 Doppler Tracking Data
<p>This Pioneer 11 Doppler tracking data set was created by John Anderson from NAVIO format. The file is ASCII text, and is a collection of Doppler data records ordered sequentially.</p>
Pioneer 10 Doppler Tracking Data
<p>This Pioneer 10 Doppler tracking data set was created by John Anderson from NAVIO format. The file is ASCII text, and is a collection of Doppler data records ordered sequentially.</p>
Data from: FMRI speech tracking in primary and non-primary auditory cortex while listening to noisy scenes
<p>This data set was analysed for the publication "FMRI speech tracking in primary and non-primary auditory cortex while listening to noisy scenes" by Hausfeld, Hamers, and Formisano (<em>Communications Biology</em>, 2024). Anatomical and functional MRI was acquired at 7 Tesla. Participants listened to speech of 1 or 2 (concurrent) audiobooks. To analyze fMRI-based speech tracking, participants were asked to listen to one speaker by performing a task. </p> <p>The dataset is arranged as follows:</p> <p>- MRI data [single particpant folders S1-15] (preprocessed) and individual speech tracking maps are contained in the participant-specific files S[participant_ID].zip in folder "MRI"</p> <p>- Stimulus descriptions (i.e., envelopes) are included in the folder "ENVELOPES"</p> <p>- Individual results (tracking map similarities and behavioral outcomes) are included in "INDIV_RESULTS"</p> <p>- Code to recreate figures is provided in folder "CODE" </p> <p>- the README contains information on the repository's content</p> <p> </p> <p>Please note additional information in the original publication</p> <p> </p> <p>Abstract of corresponding manuscript</p> <p>Invasive and non-invasive electrophysiological measurements during “cocktail-party”-like listening indicate that neural activity in the human auditory cortex (AC) “tracks” the envelope of relevant speech. However, due to limited coverage and/or spatial resolution, the distinct contribution of primary and non-primary areas remains unclear. Here, using 7-Tesla fMRI, we measured brain responses of participants attending to one speaker, in the presence and absence of another speaker. Through voxel-wise modeling, we observed envelope tracking in bilateral Heschl’s gyrus (HG), right middle superior temporal sulcus (mSTS) and left temporo-parietal junction (TPJ), despite the signal’s sluggish nature and slow temporal sampling. Neurovascular activity correlated positively (HG) or negatively (mSTS, TPJ) with the envelope. Further analyses comparing the similarity between spatial response patterns in the <em>single speaker </em>and<em> concurrent speakers</em> conditions and envelope decoding indicated that tracking in HG reflected both relevant and (to a lesser extent) non-relevant speech, while mSTS represented the relevant speech signal. Additionally, in mSTS, the similarity strength correlated with the comprehension of relevant speech. These results indicate that the fMRI signal tracks cortical responses and attention effects related to continuous speech and support the notion that primary and non-primary AC process ongoing speech in a push-pull of acoustic and linguistic information.</p> <p> </p> <p>Author contact: lars.hausfeld@maastrichtuniversity.nl</p> <p> </p>
Supplementary data and code for "Predicting anthropogenic food supplementation from individual tracking data"
<p><span>This repository contains data and R-Scripts to reproduce the results presented in the paper “</span><span>Predicting anthropogenic food supplementation from individual tracking data</span><span>” published in Ibis. Please note that some data (basemap of Switzerland, forests, buildings) are publicly available from </span><span><a href="https://map.geo.admin.ch/"><span>https://map.geo.admin.ch/</span></a></span><span> and are therefore not provided here. The abstract of the article is as follows: </span><span>Many wildlife species consume food or refuse provided by humans. To understand the effect of anthropogenic food subsidies on wildlife populations, we first need to quantify where and when individuals can access such food sources. The red kite <em>Milvus milvus</em> is an opportunistic raptor species and uses both inadvertent and deliberate food subsidies provided by citizens. Here we present a new approach using GPS-tracking data to predict where anthropogenic food subsidies likely occur. We tracked 497 individuals with solar-powered GPS transmitters over an average of 3.2 (range 1 – 9) breeding seasons in Switzerland, and combined these data with locations of 125 known feeding sites obtained through interviews. We used two sequential random forest models, at both individual movement and population levels, to predict where anthropogenic food subsidies were attended by red kites. The first model classified locations that were frequently and regularly revisited, and successfully predicted 85% of locations that were within 50 m of an externally validated feeding site. These predicted locations were aggregated in 500 m grid cells to calculate the proportion of individuals and locations associated with predicted food subsidy. A second model related the presence of known food subsidies to the aggregated predictions. In our study area, 80% of known anthropogenic food provision locations could be correctly identified using red kite tracking data, but data sparsity beyond the core range of tracked individuals limits predictions of anthropogenic food subsidies at larger geographic scales. Nonetheless, biologging data can identify ephemeral food sources, and facilitate an assessment of the importance of anthropogenic food subsidies on the fitness of individuals in tracked populations.</span></p>
Data Echoes: Tracking Data Availability and Integrity in Software Engineering Research
<p><strong>This is the dataset of the report: Data Echoes: Tracking Data Availability and Integrity in Software Engineering Research</strong></p> <p>It contains the following information of all the papers from ASE, FSE, and ICSE in 2023:</p> <ul> <li>Paper title</li> <li>Keyword</li> <li>Is the source data available and accessible in the paper?</li> <li>If the source data is not available, do the authors explain why?</li> <li>Hosting platforms</li> <li>Access mode</li> <li>License</li> <li>Is their experiment data reused from previous work, or newly generated specifically for this study, or combination of both? </li> <li>Do the authors change/modify their experiment data before experiment?</li> <li>What modifications do they perform?</li> <li>Does the link provide detailed instructions about how to replicate their paper?</li> <li>Does the link contains their complete experiment data, their source code or other materials that are necessary to replicate their experiments?</li> <li>What's the data format inside the link?</li> <li>What's the content of the link?</li> </ul> <p> </p> <p>We collect the data in a rush.</p> <p>If you want to use this dataset and find any errors, please contact us ;-)</p> <p> </p> <p>Our emails:</p> <ul> <li>echo.xiangchen@gmail.com</li> <li>zhifengyao731@gmail.com</li> </ul>
Digital Collections Data and Tracking Disease Workshop 2: Introduction and Museum Perspectives Talks
<p>Recordings of talks by speakers in the Museum Perspectives section of our Digital Collections Data and Tracking Disease Workshop 2 at the American Society of Mammalogists Meeting in Boulder, Colorado in June of 2024.</p> <ul> <li><strong>Topic -- A Museum Perspective</strong> <ul> <li><strong>Museums and Emerging Pathogens in the Americas</strong>, Joe Cook, University of New Mexico<strong><br></strong></li> <li><strong>Museums and pathogens: Challenges, potential, and management</strong>, <a href="https://orcid.org/0000-0002-6931-6420" target="_blank" rel="noopener">Adam Ferguson</a>, Field Museum of Natural History</li> <li><strong>The UX Side of Extended Specimen Databases</strong>, <a href="https://orcid.org/0000-0002-8775-7254" target="_blank" rel="noopener">Kelly Speer</a></li> <li><strong>Preventing the next viral pandemic via elucidation of mammalian reservoirs</strong>, Rick White, University of North Carolina-Charlotte</li> <li><strong>Scientific collections and public health surveillance: institutional weaknesses and perspectives from Uruguay,</strong> <a href="https://orcid.org/0000-0002-4055-9277" target="_blank" rel="noopener">Germán Botto Nuñez</a>, Universidad de la República <ul> <li>(note talk sound did not work for this presentation)</li> </ul> </li> <li><strong>Building integrated archives for parasite and pathogen discovery</strong><br><a href="https://orcid.org/0000-0002-8065-0833" target="_blank" rel="noopener">Kurt Galbreath</a>, Northern Michigan University</li> <li><strong>The Dark Side of the Light: Fireflies, Snails, and Viromes</strong>, <a href="https://orcid.org/0000-0001-5067-3316" target="_blank" rel="noopener">Oliver Keller</a>, University of Michigan</li> </ul> </li> </ul>
Data and scripts belonging to "Time lags of nitrate, chloride, and tritium in streams assessed by dynamic groundwater flow tracking in a lowland landscape"
<p>Data and scripts belonging to Kaandorp et al., 2021 "Time lags of nitrate, chloride, and tritium in streams assessed by dynamic groundwater flow tracking in a lowland landscape". Hydrology and Earth System Sciences. </p>
Movement data from a Malayan krait (Bungarus candidus) tracked in the Sakaerat Biosphere Reserve, Nakhon Ratchasima province, Thailand
<p>Movement data gained through radio-telemetry of a single adult male Malayan krait (<em>Bungarus candidus</em>) among a protected forest of the Sakaerat Biosphere Reserve in Nakhon Ratchasima province, Thailand for a period of 103 days (with 90 location fixes/datapoints).</p> <p> </p> <p>BUCA40_movement_data.csv file column headings:</p> <p>track_ID: number corresponding to the location check datapoint</p> <p>animal_ID: <em>B. candidus</em> identifier number from the Sakaerat tracking project</p> <p>datetime: Date and time when the snake was located (mm/dd/yyyy hh:mm)</p> <p>easting: UTM easting (UTM Zone 47N; Datum WGS84)</p> <p>northing: UTM northing (UTM Zone 47N; Datum WGS84)</p> <p>utm_zone: Universal Transverse Mercator zone (47N World Geodetic System 84)</p> <p>GPS_accuracy: GPS location accuracy in meters (m)</p> <p>behavior: The animal’s behavior observed during the location check (e.g. sheltering, foraging, moving, or feeding)</p> <p>shelter_type: Identified selected shelter type</p> <p>habitat_type: Land-use type where the snake was located </p>
Tracking Data I/II of the publication "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"
<p>DATA belonging to the paper<br> "A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction"<br> Katharina Löffler, Tim Scherr, Ralf Mikut<br> doi: https://doi.org/10.1101/2021.03.16.435631</p> <p>-----------------------------</p> <p>To investigate the influence of different segmentation errors on the tracking performance we simulate errorneous segmentation data:<br> - under-segmentation (referred to as "merge" in the folders), over-segmentation("split"), False Negatives ("remove"), combination of the aforementioned errors ("mixed")<br> - percentages: 1,2,5,10,20 of errorneous masks per dataset<br> - runs: 5 randomly initialized runs per combination<br> - datasets: Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ each with two image sequences<br> ---> in total 4 (error types) * 5 (percentage) * 5 (runs) * 2 (data sets) * 2 (image sequences) = 400 datasets</p> <p>The datasets can be recreated by running our code https://git.scc.kit.edu/KIT-Sch-GE/2021-cell-tracking<br> ----------------------------</p> <p>RESULTS<br> We evuated the four tracking algorithms KIT-Sch-GE(1), KTH-SE, MU-Lux-CZ and our proposed algorithm on the aforementioned datasets and compare their performance using the CTC metrics DET, SEG and TRA.<br> This repository contains all metrics as xls files and all tracking results as image sequences.</p> <p><br> <strong>xls files</strong><br> -----------<br> compare_all_trackers_on_synt_bm.csv<br> Comparing the tracking algorithms MU-Lux-CZ, KTH-SE, KIT-Sch-GE(1) and the proposed tracking algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig8 and Fig9 and Supplementary Figures 3 and 4 are created from this data)</p> <p><br> compare_postprocessing_on_synth_bm.csv<br> Comparing the different post-processing strategies of the proposed tracking algorithm algorithm on synthetically degraded segmentation data Fluo-N2DH-SIM+ and Fluo-N3DH-SIM+ (Cell Tracking Challenge datasets).<br> Reported scores are DET, SEG and TRA from the Cell Tracking Challenge<br> (Fig7 and Fig8 and Supplementary Figures 1 and 2 are created from this data)</p> <p><strong>PLEASE NOTE: the folder compare_postprocessing_synth_bm is provided in the repository 10.5281/zenodo.5227610 due to size restrictions.</strong></p> <p><strong>folders </strong>(decompressed approximately 90GB of data!)<br> -----------<br> tracking_data<br> compare_all_synth_bm<br> Contains all tracking results for each tracking algorithm on the synthetically degraded datasets ()</p> <p> compare_all_synth_bm_no_error<br> Contains the tracking results for each tracking algorithm provided with the perfect ground truth segmentation data</p> <p> compare_postprocessing_synth_bm [<strong>will be stored in 10.5281/zenodo.5227610 due to size restrictions</strong>]<br> Contains all tracking resuls for each postprocessing configuration of the proposed cell tracking algorithm<br> the leaf folders are names run_xPOSTPROCESSING where x is the run number and POSTPROCESSING the postprocessing key<br> Postprocessing keys: ("no untangle" or "no masks" is indicated by an overline in the paper)<br> all ("untangle + masks" in the paper)<br> nd ("no untangle + masks")<br> nd_ns-l ("no untangle + no masks")<br> ns-l ("untangle + no masks")</p>
Data and scripts for: track2KBA: An R package for identifying important sites for biodiversity from tracking data
<p>Data derivates and analysis scripts (in R) used for the companion paper for the R package track2KBA.</p>
Suggested Taxonomy: Tracking Technologies to Effectively Capture and Input Key Data on the Blockchain
<p>Within the paper titled "Transparency with Blockchain and Physical Tracking Technologies: Enabling Traceability in Raw Material Supply Chains" (Mater. Proc. 2021, 5(1), 1; <a href="https://doi.org/10.3390/materproc2021005001">https://doi.org/10.3390/materproc2021005001</a>), we consider the majority of tracking technologies to be part of the IoT ecosystem and suggest a taxonomy with their key features, benefits and use cases in the mining industry. Although technologies such as markers and QR/Barcodes are not necessarily electronic devices, they can integrate with other IoT objects and provide or qualify a digital identity. The common element connecting all these technologies is that they include functionalities that can capture and communicate granular, timely, relevant and accurate data, which can be automatically or manually entered into the blockchain. </p> <p>We have analysed the following most common physical tracking methods which will be described and exemplified in more detail below:</p> <ul> <li> <p>Video monitoring (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t001">Table 1</a>)</p> </li> <li> <p>Bar and QR codes (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t002">Table 2</a>)</p> </li> <li> <p>Markers and taggants (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t003">Table 3</a>)</p> </li> <li> <p>Cellular, near range and low power network tracking tools (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t004">Table 4</a>)</p> </li> <li> <p>Satellite network tracking tools (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t005">Table 5</a>)</p> </li> </ul>
Data for: Tracking the temporal dynamics of insect defoliation by high-resolution radar satellite data
<p><span>1. Quantifying tree defoliation by insects over large areas is a major challenge in forest management, but it is essential in ecosystem assessments of disturbance and resistance against herbivory. However, the trajectory from leaf-flush to insect defoliation to refoliation in broadleaf trees is highly variable. Its tracking requires high temporal- and spatial-resolution data, particularly in fragmented forests. </span></p> <p><span>2. In a unique replicated field experiment manipulating gypsy moth <i>Lymantria dispar</i> densities in mixed-oak forests, we examined the utility of publicly accessible satellite-borne radar (Sentinel-1) to track the fine-scale temporal trajectory of defoliation. The ratio of backscatter intensity between two polarizations from radar data of the growing season constituted a canopy development index (CDI) and a normalized CDI (NCDI), which were validated by optical (Sentinel-2) and terrestrial laser scanning (TLS) data as well by intensive caterpillar sampling from canopy fogging. </span></p> <p><span>3. The CDI and NCDI strongly correlated with optical and TLS data (Spearman's ρ=0.79 and 0.84, respectively). The ∆NCDI<sub><sub>Defoliation</sub><sub> (</sub><sub>A</sub><sub>-</sub><sub>C</sub><sub>)<i> </i></sub></sub>significantly explained caterpillar abundance (R<sup>2</sup>=0.52). The NCDI at critical time-steps and ΔNCDI related to defoliation and refoliation well discriminated between heavily and lightly defoliated forests. </span></p> <p><span>4. We demonstrate that the high spatial and temporal resolution and the cloud independence of Sentinel-1 radar potentially enable spatially unrestricted measurements of the highly dynamic canopy herbivory. This can help monitor insect pests, improve the prediction of outbreaks, and facilitate the monitoring of forest disturbance, one of the high priority Essential Biodiversity Variables, in the near future.</span></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.