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1,548 results for “Trajectories”
Fig. 3 in The Wettstein tesseract: A tool for conceptualising species-rank decisions and illustrating speciation trajectories
Fig. 3. The Wettstein tesseract as a tool for illustrating geographical (allopatric, peripatric, parapatric) or non-geographical (sympatric) pathways of speciation. Arrows indicate different paths through the tesseract from a coherent population to final stage of two evolutionary independent entities (species). Note that these paths end whenever species rank is achieved; subsequent changes of biogeographical patterns (allopatrically, peripatrically, or parapatrically formed species becoming sympatric and sympatrically formed species becoming allopatric) are not illustrated.
Fig. 2 in The Wettstein tesseract: A tool for conceptualising species-rank decisions and illustrating speciation trajectories
Fig. 2. The Wettstein tesseract unfolded, with vertices representing binary-coded (statistically significant) genealogical, geographical, ecological, and morphological differences between two sister-taxa under study (left) and their taxonomical counterparts (right). Colours of vertices are just for clarity of the illustration and have no semantic implication.
Fig. 1. The Wettstein tesseract – a in The Wettstein tesseract: A tool for conceptualising species-rank decisions and illustrating speciation trajectories
Fig. 1. The Wettstein tesseract – a four-dimensional hypercube for illustrating species delimitation and speciation trajectories, with the four dimensions representing geographical differentiation, ecological divergence, morphological difference, and genealogical independence, respectively, and species rank attributed to the vertices in red (while vertices in black and white indicate infraspecific diversification patterns without or with significant contribution of a genealogical factor, respectively).
A large dataset of detection and submeter-accurate 3-D trajectories of juvenile Chinook salmon
<p>Acoustic telemetry has been used extensively to study the behavior of aquatic animals. The Juvenile Salmon Acoustic Telemetry System (JSATS) is one such system; it was developed for studying juvenile salmonids but has been used to study numerous species. A recent innovation of the JSATS system is an injectable acoustic transmitter that is small enough to be implanted through injection instead of surgery. Use of the JSATS system involves deploying cabled acoustic receivers at hydroelectric dams and autonomous acoustic receivers in free-flowing sections of a river. The raw detections from acoustic-tagged fish are processed to remove potential false positives. The clean detections (5,147,996 total) are used to generate detection events and to compute 3-D trajectories (403,900 total), which are used to assign fish to a passage route through a dam. Controlled field testing involving a high-accuracy Global Positioning System receiver is done to validate the submeter accuracy of the trajectories. The JSATS dataset could be reused for expanding the understanding of near-dam fish behavior.</p>
Molecular trajectories and general MD files: Molecular insights on confined water in the nanochannels of self-assembled ionic liquid crystal
<p>This repository includes the MD simulation dataset of self-assembled ionic liquid crystal reported in the article of <a href="https://doi.org/10.1126/sciadv.abf0669"><em>Sci. Adv.</em> <strong>7</strong>, eabf0669 (2021) [DOI: 10.1126/sciadv.abf0669]</a>. The chemical structure of ionic liquid crystal is described in <a href="https://advances.sciencemag.org/content/advances/7/31/eabf0669/F1.large.jpg">Fig. 1A</a>. The cation involves an ionic moiety of <em>N</em>-methyl-<em>N</em>,<em>N</em>,<em>N</em>-triethylammonium group, and the terminals of the two alkyl chains are conjugated dienes. The anion is tetrafluoroborate BF<sub>4</sub>. The molecular and atomic-group charges of cation and anion are shown in the topology files named "ilc-oplsdft.itp" and "bf4-oplsdft.itp", respectively. The TIP3P and TIP4P/2005 models are employed for water molecules. After the careful equilibration process, the production MD was performed for 50 ns under the <em>NPT</em> condition at each composition. This dataset provides the configuration, topology, and general MD input files of Gromacs for all the states and models of bicontinuous and columnar structures obtained in the article. The 3D view of bicontinuous and columnar structures can be available in <a href="https://advances.sciencemag.org/content/advances/7/31/eabf0669/F2.large.jpg">Fig. 2</a>. The attached edr and log files are energy and general outputs of the MD simulation generated in our environment, and the xtc file is a trajectory output generated with a time interval of 20 ps.</p>
MD trajectories for "Communication Breakdown: Dissecting the COM Interfaces between the Subunits of Nonribosomal Peptide Synthetases"
<p>This dataset contains Amber MD trajectories for the MD simulations described in the manuscript "Communication Breakdown: Dissecting the COM Interfaces between the Subunits of Nonribosomal Peptide Synthetases" by Christopher D. Fage, Simone Kosol, Matthew Jenner, Carl Öster, Angelo Gallo, Milda Kaniusaite, Roman Steinbach, Michael Staniforth, Vasilios G. Stavros, Mohamed A. Marahiel, Max J. Cryle, and Józef R. Lewandowski published in ACS Catalysis (<a href="https://doi.org/10.1021/acscatal.1c02113">https://doi.org/10.1021/acscatal.1c02113</a>). If you use these data please cite the original manuscript (follow the manuscript DOI for the final citation, which was not available at the time of publishing this data set). </p> <p>To reduce their size the trajectories were stripped of water and ions. Only frames every 1 ns or 5 ns were saved. Please see the Supporting Information of the source manuscript for the conditions for the simulations. </p>
Long-Term Demographic Trends in Prehistoric Italy: climate impacts and regionalised socio-ecological trajectories (dataset and R script)
<p>The present digital archive is the outcome of the paper: <strong>Palmisano, A., Bevan, A., Kabelindde, A., Roberts, N., and Shennan, S., 2021. <a href="https://doi.org/10.1007/s10963-021-09159-3">Long-Term Demographic Trends in Prehistoric Italy: climate impacts and regionalised socio-ecological trajectories</a>. <em>Journal of World Prehistory, 34 (3)</em>, </strong>381-432<strong>.</strong></p> <p>The dataset included here provides a collection of <strong>4,010</strong> radiocarbon dates from <strong>947</strong> archaeological sites for a period spanning between 11,000 and 1500 BP. In addition, the digital archive related to this paper provides reproducible analyses in the form of one script written in R statistical computing language.</p> <p>List of versions:</p> <ul> <li><strong>1.0.</strong> 4 August 2021 - First public release of the dataset on Zenodo. </li> </ul>
trajectories for: Membrane-binding mechanism of the EEA1 FYVE domain revealed by multi-scale molecular dynamics simulations
<p>Coarse-grained trajectories produced and analysed for publication: </p> <p>----------------------</p> <p>Membrane-binding mechanism of the EEA1 FYVE domain revealed by multi-scale molecular dynamics simulations</p> <p>Andreas Haahr Larsen*, Lilya Tata*, Laura John & Mark S.P. Sansom</p> <p>Department of Biochemistry, University of Oxford, Oxford, United Kingdom, OX1 3QU</p> <p>PLOS comp biol (in press) </p> <p>-------------------------</p> <p> </p> <p>** file overview**</p> <p>md_X.xtc: (X=0..14) 15 repeated CG simulations (1500 ns each) of the FYVE domain from EEA1 binding to POPC:POP1 bilayer. The repeats differ in the rotation of the initial frame.<br> </p> <p>final_cg2at_aligned.pdb: initial frame for AT (after CG2AT)</p> <p>prod_cym_cent_repX.xtc (X=1,2,3) 3 repeated AT sims (500 ns each) of the FYVE domain from EEA1 binding to POPC:POP1 bilayer. </p> <p>** scripts for reproduction at GitHub**</p> <p>scripts and files for reproduction are available at: https://github.com/andreashlarsen/Larsen-Tata2021-FYVE</p>
Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Intersections
<p>A pre-trained model of the paper "Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Intersections," CoRL 2020.</p>
EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching
<p><strong>EyeFi Dataset</strong></p> <p>This dataset is collected as a part of the EyeFi project at Bosch Research and Technology Center, Pittsburgh, PA, USA. The dataset contains WiFi CSI values of human motion trajectories along with ground truth location information captured through a camera. This dataset is used in the following paper "<em>EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching</em>" that is published in the IEEE International Conference on Distributed Computing in Sensor Systems 2020 (DCOSS '20). We also published a dataset paper titled as "<em>Dataset: Person Tracking and Identification using Cameras and Wi-Fi Channel State Information (CSI) from Smartphones</em>" in Data: Acquisition to Analysis 2020 (DATA '20) workshop describing details of data collection. Please check it out for more information on the dataset.</p> <p><strong>Data Collection Setup</strong><br> <br> In our experiments, we used Intel 5300 WiFi Network Interface Card (NIC) installed in an Intel NUC and Linux CSI tools [1] to extract the WiFi CSI packets. The (x,y) coordinates of the subjects are collected from Bosch Flexidome IP Panoramic 7000 panoramic camera mounted on the ceiling and Angle of Arrivals (AoAs) are derived from the (x,y) coordinates. Both the WiFi card and camera are located at the same origin coordinates but at different height, the camera is location around 2.85m from the ground and WiFi antennas are around 1.12m above the ground.</p> <p>The data collection environment consists of two areas, first one is a rectangular space measured 11.8m x 8.74m, and the second space is an irregularly shaped kitchen area with maximum distances of 19.74m and 14.24m between two walls. The kitchen also has numerous obstacles and different materials that pose different RF reflection characteristics including strong reflectors such as metal refrigerators and dishwashers. </p> <p>To collect the WiFi data, we used a Google Pixel 2 XL smartphone as an access point and connect the Intel 5300 NIC to it for WiFi communication. The transmission rate is about 20-25 packets per second. The same WiFi card and phone are used in both lab and kitchen area.</p> <p><strong>List of Files</strong><br> Here is a list of files included in the dataset:</p> <pre><code>|- 1_person |- 1_person_1.h5 |- 1_person_2.h5 |- 2_people |- 2_people_1.h5 |- 2_people_2.h5 |- 2_people_3.h5 |- 3_people |- 3_people_1.h5 |- 3_people_2.h5 |- 3_people_3.h5 |- 5_people |- 5_people_1.h5 |- 5_people_2.h5 |- 5_people_3.h5 |- 5_people_4.h5 |- 10_people |- 10_people_1.h5 |- 10_people_2.h5 |- 10_people_3.h5 |- Kitchen |- 1_person |- kitchen_1_person_1.h5 |- kitchen_1_person_2.h5 |- kitchen_1_person_3.h5 |- 3_people |- kitchen_3_people_1.h5 |- training |- shuffuled_train.h5 |- shuffuled_valid.h5 |- shuffuled_test.h5 View-Dataset-Example.ipynb README.md </code></pre> <p>In this dataset, folder `1_person/` , `2_people/` , `3_people/` , `5_people/`, and `10_people/` contains data collected from the lab area whereas `Kitchen/` folder contains data collected from the kitchen area. To see how the each file is structured, please see below in section <em>Access the data</em><strong>.</strong> </p> <p>The training folder contains the training dataset we used to train the neural network discussed in our paper. They are generated by shuffling all the data from `1_person/` folder collected in the lab area (`1_person_1.h5` and `1_person_2.h5`). </p> <p><strong>Why multiple files in one folder?</strong></p> <p>Each folder contains multiple files. For example, `1_person` folder has two files: `1_person_1.h5` and `1_person_2.h5`. Files in the same folder always have the same number of human subjects present simultaneously in the scene. However, the person who is holding the phone can be different. Also, the data could be collected through different days and/or the data collection system needs to be rebooted due to stability issue. As result, we provided different files (like `1_person_1.h5`, `1_person_2.h5`) to distinguish different person who is holding the phone and possible system reboot that introduces different phase offsets (see below) in the system. </p> <p><strong>Special note:</strong></p> <p>For `1_person_1.h5`, this file is generated by the same person who is holding the phone, and `1_person_2.h5` contains different people holding the phone but only one person is present in the area at a time. Boths files are collected in different days as well.</p> <p><br> <strong>Access the data</strong><br> To access the data, hdf5 library is needed to open the dataset. There are free HDF5 viewer available on the official website: <a href="https://www.hdfgroup.org/downloads/hdfview/">https://www.hdfgroup.org/downloads/hdfview/</a>. We also provide an example Python code <em>View-Dataset-Example.ipynb</em> to demonstrate how to access the data.</p> <p>Each file is structured as (except the files under *"training/"* folder):<br> </p> <pre><code>|- csi_imag |- csi_real |- nPaths_1 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- nPaths_2 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- nPaths_3 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- nPaths_4 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- num_obj |- obj_0 |- cam_aoa |- coordinates |- obj_1 |- cam_aoa |- coordinates ... |- timestamp </code></pre> <p>The `csi_real` and `csi_imag` are the real and imagenary part of the CSI measurements. <strong>The order of antennas and subcarriers are as follows for the 90 `csi_real` and `csi_imag` values : [subcarrier1-antenna1, subcarrier1-antenna2, subcarrier1-antenna3, subcarrier2-antenna1, subcarrier2-antenna2, subcarrier2-antenna3,… subcarrier30-antenna1, subcarrier30-antenna2, subcarrier30-antenna3]</strong>. `nPaths_x` group are SpotFi [2] calculated WiFi Angle of Arrival (AoA) with `x` number of multiple paths specified during calculation. Under the `nPath_x` group are `offset_xx` subgroup where `xx` stands for the offset combination used to correct the phase offset during the SpotFi calculation. We measured the offsets as:</p> <pre><code>|Antennas | Offset 1 (rad) | Offset 2 (rad) | |:-------:|:---------------:|:-------------:| | 1 & 2 | 1.1899 | -2.0071 | 1 & 3 | 1.3883 | -1.8129 </code></pre> <p>The measurement is based on the work [3], where the authors state there are two possible offsets between two antennas which we measured by booting the device multiple times. The combination of the offset are used for the `offset_xx` naming. For example, `offset_12` is offset 1 between antenna 1 & 2 and offset 2 between antenna 1 & 3 are used in the SpotFi calculation.</p> <p>The `num_obj` field is used to store the number of human subjects present in the scene. The `obj_0` is always the subject who is holding the phone. In each file, there are `num_obj` of `obj_x`. For each `obj_x1`, we have the `coordinates` reported from the camera and `cam_aoa`, which is estimated AoA from the camera reported coordinates. The (x,y) coordinates and AoA listed here are chronologically ordered (except the files in the `training` folder) . It reflects the way the person carried the phone moved in the space (for `obj_0`) and everyone else walked (for other `obj_y`, where `y` > 0). </p> <p>The `timestamp` is provided here for time reference for each WiFi packets.</p> <p>To access the data (Python):</p> <pre><code class="language-python">import h5py data = h5py.File('3_people_3.h5','r') csi_real = data['csi_real'][()] csi_imag = data['csi_imag'][()] cam_aoa = data['obj_0/cam_aoa'][()] cam_loc = data['obj_0/coordinates'][()] </code></pre> <p><strong>For file inside `training/` folder:</strong></p> <p>Files inside training folder has a different data structure:</p> <pre><code> |- nPath-1 |- aoa |- csi_imag |- csi_real |- spotfi |- nPath-2 |- aoa |- csi_imag |- csi_real |- spotfi |- nPath-3 |- aoa |- csi_imag |- csi_real |- spotfi |- nPath-4 |- aoa |- csi_imag |- csi_real |- spotfi </code></pre> <p><br> The group `nPath-x` is the number of multiple path specified during the SpotFi calculation. `aoa` is the camera generated angle of arrival (AoA) (can be considered as ground truth), `csi_image` and `csi_real` is the imaginary and real component of the CSI value. `spotfi` is the SpotFi calculated AoA values. The SpotFi values are chosen based on the lowest median and mean error from across `1_person_1.h5` and `1_person_2.h5`. All the rows under the same `nPath-x` group are aligned (i.e., first row of `aoa` corresponds to the first row of `csi_imag`, `csi_real`, and `spotfi`. There is no timestamp recorded and the sequence of the data is not chronological as they are randomly shuffled from the `1_person_1.h5` and `1_person_2.h5` files. </p> <p><strong>Citation</strong><br> If you use the dataset, please cite our paper:</p> <pre><code>@inproceedings{eyefi2020, title={EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching}, author={Fang, Shiwei and Islam, Tamzeed and Munir, Sirajum and Nirjon, Shahriar}, booktitle={2020 IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS)}, year={2020}, organization={IEEE} } </code></pre> <p>Thanks!</p> <p><strong>References</strong></p> <p>1. Halperin, Daniel, et al. "Tool release: Gathering 802.11 n traces with channel state information." ACM SIGCOMM Computer Communication Review 41.1 (2011): 53-53.</p> <p>2. Kotaru, Manikanta, et al. "Spotfi: Decimeter level localization using wifi." Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication. 2015.</p> <p>3. Zhang, Dongheng, et al. "Calibrating Phase Offsets for Commodity WiFi." IEEE Systems Journal (2019).<br> </p>
Backtracked trajectories of passive particles in the Virgin Islands Basin (2007-2017) using Ocean Parcels
<p>Backtracked trajectories of 100 passive particles released in the Virign Islands Basin daily from 2007-2017. Backtracking is done for 100 days. Surface velocity fields are from GLORYS reanalysis. <br> <br> Corresponding author: Giovanni Seijo-Ellis (giovanni.seijo@colorado.edu)</p>
Data from: Distinct developmental trajectories for risky and impulsive decision-making in chimpanzees
<p>Human adolescence is characterized by a suite of changes in decision-making and emotional regulation that promote risky and impulsive behavior. Accumulating evidence suggests that behavioral and physiological shifts seen in human adolescence are shared by some primates, yet it is unclear if the same cognitive mechanisms are recruited. We examined developmental changes in risky choice, inter-temporal choice, and emotional responses to decision outcomes in chimpanzees, our closest-living relatives. We found that adolescent chimpanzees were more risk-seeking than adults, as in humans. However, chimpanzees showed no developmental change in inter-temporal choice, unlike humans, although younger chimpanzees did exhibit elevated emotional reactivity to waiting compared to adults. Comparisons of cortisol and testosterone indicated robust age-related variation in these biomarkers, and patterns of individual differences in choices, emotional reactivity, and hormones also supported a developmental dissociation between risk and choice impulsivity. These results show that some but not all core features of human adolescent decision-making are shared with chimpanzees.</p>
Leaf gene expression trajectories during the growing season are consistent between sites and years in American beech
<p>Transcriptomics, the quantification of gene expression, provides a versatile tool for ecological monitoring. Here, we show that through genome-guided profiling of transcripts mapping to 33,042 loci, gene expression differences can be discerned among multi-year and seasonal leaf samples collected from American beech trees at two latitudinally separated sites. Despite a bottleneck imposed due to large-scale post-Columbian deforestation, the SNP-based population genetic background analysis has yielded sufficient variation to account for differences between populations and among individuals. Our time series of expression analyses during spring-summer and summer-fall transitions for two consecutive years involved 4197 differentially expressed protein coding genes. A global comparison of 12 seasons has revealed that spring gene expression sets the pace for the rest of the growing season. Using \textit{Populus} orthologs of the differentially expressed genes, we reconstructed a protein-protein interactome as a representation of the leaf physiological states of trees during the seasonal transitions. Gene set enrichment analysis revealed GO terms that highlight molecular functions and biological processes possibly influenced by abiotic forcings such as recovery from drought and response to excess precipitation. Further, based on 324 co-regulated transcripts, we focused on a subset of terms that could be putatively attributed to phenological shifts due to late spring. Our conservative results indicate that extended transcriptome-based monitoring of forests can capture ranges of responses arising from other factors including air quality, chronic disease as well as herbivore outbreaks that require activation and/or downregulation of genes collectively tuning reaction norms needed for the survival of long living trees such as the American beech.</p>
G-Code associated to the trajectories of the brush in ApPEARS deliverable D5.1 Database of vector based brush strokes and sample prints that demonstrate the range of printed materials
<p>This is appendix for the ApPEARS Deliverable: D5.1 – Database of vector based brush strokes and sample prints that demonstrate the range of printed materials. It contains G-Code Paths associated to the trajectories of the brush.</p>
Long-term coastal macrobenthic Community Trajectory Analysis reveals habitat-dependent stability patterns
<p>Long-term monitoring programs are fundamental to detecting changes in ecosystem health and understanding ecological processes. In the current context of increasing anthropogenic threats on marine ecosystems, understanding the dynamics and response of communities becomes essential. We used data collected over 14 years in the REBENT benthic coastal invertebrates monitoring program, at a regional scale in the North-East Atlantic, covering a total of 26 sites and 979 taxa. Four distinct habitats were studied: two biogenic habitats associated with foundation species in the intertidal and subtidal zones and two bare sedimentary habitats in the same respective tidal zones. We used Community Trajectory Analysis, a statistical approach that allows for quantitative measures and comparisons of temporal trajectories of ecosystems. We compared observed community trajectories to trajectories simulated under a non-directional null model in order to better understand the dynamics of the communities, their potential drivers, and the role of the studied habitats in these dynamics. Despite strong differences in the community compositions between sites and habitats, the communities followed non-directional dynamics during the 14 years monitored, which suggested stability at the regional scale. However, the shape, size, and direction of the trajectories of benthic communities were more similar within than among habitats, also suggesting the influence of the nature of the habitat on community dynamics. Results showed a higher variability in community composition in the first years of the monitoring in the intertidal bare habitat and confirmed the role of biogenic habitats in maintaining temporal stability. They also highlighted the need to apprehend the role of transient and rare species and the scale of observation in temporal beta diversity analyses. Finally, our study confirmed the usefulness of Community Trajectory Analysis to link observed trajectory patterns to fundamental ecological processes.</p>
The 400 ns molecular dynamic (MD) trajectories for the wild type and mutant forms of the S. tuberosum eIF4E1 and eIF4E2
<p>Truncated from the N termini models of the wild type and mutant forms of the S. tuberosum eIF4E1 and eIF4E2. <br> The molecular dynamic (MD) trajectories with 400-ns length for each wild type and mutant forms of the eIF4E in the water environment according to the standard MD procedure.</p>
DATA7: A dataset that uses synthetic trajectories of vehicles and real cellular tower locations to simulate the workload of Edge nodes in the city of Pisa
<p><strong>Description</strong></p> <p>The dataset contains observations of vehicles in the range of edge nodes (cellular towers). The trajectories of vehicles are synthetically generated with <a href="https://www.eclipse.org/sumo/">SUMO</a>. The cellular tower positions have been taken from <a href="https://opencellid.org/">OpenCelliD</a>. The dataset is in the comma-separated values (CSV) format, and is around 220MB decompressed.</p> <p><br> The CSV contains the following fields:<br> * edge_id: unique identifier of the edge devices<br> * edge_lat: latitude coordinate of the edge device<br> * edge_lon: longitude coordinate of the edge device<br> * time: simulation step of the observation<br> * vehicle_id: unique identifier of the vehicle<br> * vehicle_lat: latitude coordinate of the vehicle<br> * vehicle_lon: longitude coordinate of the vehicle<br> * distance: geodesic distance in meters from the vehicle and the edge device</p>
Time-Resolved Plasmon-Assisted Generation of Arbitrary Optical-Vortex Pulses - Supporting Information for Trajectories
<p>We provide videos of trajectories for a test charge, bound by Lennard-Jones potential. First video, titled "PW Trajectory - Point 2 " refers to the particle under the effect of a plane wave pulse. The other video , titled "Emitter Trajectory - Point 2" refers to the particle affected by an orbital angular momentum carrying pulse.</p> <p>These videos are supplementary materials for the article titled "Time-Resolved Plasmon-Assisted Generation of Arbitrary Optical-Vortex Pulses".</p>
PNSDs and trajectory history datasets (Khadir et al., 2023)
<p>Three datasets (for ATTO Tower, Hyytiälä and Zeppelin stations):</p> <p>Contain hourly Particle Number Size Distributions and air mass history (96 hours) calculated using Hysplit - Hybrid Single Particle Lagrangian Integrated Trajectory model. Precipitation rates along trajectories are collocated using TRMM for the Atto dataset, and other meteorological conditions are given from GDAS1 reanalysis (output from Hysplit).</p>
Lagrangian trajectory dataset for AMOC lower limb
<p>These Lagrangian trajectory files were generated by TRACMASS, a Lagrangian parcel tracing algorithm, using data from a high-resolution (1/12<sup>o</sup>) ocean sea-ice hindcast. Two set of experiments were performed to trace the Atlantic Meridional Overturning Circulation (AMOC) lower limb; 1) Initiated only southward trajectories across the Fram Strait <strong>(fs)</strong> that corresponds to Arctic outflow and 2) traced only northward trajectories across the easten Subpolar North Atlantic (SPNA) Section which corresponds to Atlantic inflow and associated with the North Atlantic Current <strong>(nac)</strong>.</p> <p>_ini.csv = store positions and properties of trajectories at the starting location</p> <p>_run.csv = store positions and properties of trajectories during the trajectory simulation</p> <p>_out.csv = store positions and properties of trajectories at the ending location</p> <p>_rerun.csv = This file is used to select trajectories that have reached a particular ending section. Column 2 in this file contain kill zone flag. Flag 1 means trajectories reaching the surface, 2 indicates trajectories reaching the Fram Strait , 3 means trajectories reaching the eastern SPNA section and finally 4 illustrate trajectories aprroaching the Barents Sea.</p> <p>TRACMASS documentation is available at <strong>https://www.tracmass.org/docs.html</strong></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.