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Figs. 32–34 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Figs. 32–34. Elaphropeza obscura, new species: 32, right epandrial lamella, lateral view; 33, epandrium with cerci, dorsal view; 34, left surstylus, lateral view. Scale bar = 0.1 mm.
Figs. 6–7 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Figs. 6–7. Elaphropeza asexa Shamshev & Grootaert, 2007: 6, habitus male lateral (photo Abdullah Samoh); 7, habitus female dorsal.
Fig. 22 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Fig. 22. Elaphropeza lowioides, new species, male habitus lateral. Genitalia removed and illustrated in Figs. 23–25.
Figs. 3–5 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Figs. 3–5. Elaphropeza albicornis, new species: 3, right epandrial lamella, lateral view; 4, epandrium with cerci, dorsal view; 5, left surstylus, lateral view. Scale bar = 0.1 mm. lc: left cercus; ls: left surstylus; rel: right epanderial lamella, lel: left epandrial lamella.
Figs. 41–43 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Figs. 41–43. Elaphropeza singulata Shamshev & Grootaert, 2007. 41, right epandrial lamella, lateral view; 42, epandrium with cerci, dorsal view; 43, left surstylus, lateral view. Scale bar = 0.1 mm.
Fig. 40 in New species of fast-running flies (Diptera: Empidoidea, Hybotidae, Tachydromiinae) from mangroves in Singapore
Fig. 40. Elaphropeza singulata Shamshev & Grootaert, 2007 male habitus lateral. Genitalia removed and illustrated in Figs. 41–43.
Echolocating toothed whales use ultra-fast echo-kinetic responses to track evasive prey
<p>Visual predators rely on fast-acting optokinetic responses to track and capture agile prey. Most toothed whales, however, rely on echolocation for hunting and have converged on biosonar clicking rates reaching 500/s during prey pu rsuits. If echoes are processed on a click by click basis, as assumed, neural responses 100x faster than those in vision are required to keep pace with this information flow. Using high resolution bio-logging of wild predator prey interactions we show that toothed whales adjust clicking rates to track prey movement within 50 200 ms of prey escape responses. Hypothesising that these stereotyped biosonar adjustments are elicited by sudden prey accelerations, we measured echo kinetic responses from trained harb our porpoises to a moving target and found similar latencies. High biosonar sampling rates are, therefore, not supported by extreme speeds of neural processing and muscular responses. Instead, the neuro kinetic response times in echolocation are similar to those of tracking responses in vision, suggesting a common neural underpinning.</p>
Microsatellite dataset from: The fast invasion of Europe by the box tree moth: An additional example coupling multiple introduction events, bridgehead effects and admixture events.
<p>Genotypes of native and invasive populations of Cydalima perspectalis for 15 microsatellites generated for our paper "The fast invasion of Europe by the box tree moth: An additional example coupling multiple introduction events, bridgehead effects and admixture events."</p> <p>This folder contains:<br> - ReadMe file giving the following information</p> <p>- "Data_Cydalima_genotype.xlsx” containing populations information and individuals genotype for the 15 microsatellites developed by Bras et al. 2018 (Eur. J. Entomol.)</p> <p> </p> <p>This work was done in the frame of a regional project called INCA (grant from Région Centre Val de Loire).</p>
Data for publication "Fast and Accurate Distance-based Phylogenetic Placement using Divide and Conquer" (APPLES-2)
<p>Data and scripts used in the paper "Fast and Accurate Distance-based Phylogenetic Placement using Divide and Conquer"</p>
Development of an OpenFOAM Multiphysics Solver for Solid Fission Products Transport in the Molten Salt Fast Reactor - Dataset
<p>Dataset associated with the PHYSOR 2022 conference paper "Development of an OpenFOAM Multiphysics Solver for Solid Fission Products Transport in the Molten Salt Fast Reactor"</p>
Procalcitonin detection in human plasma specimens using a fast version of proximity extension assay.
<p>Proximity Extension Assay (PEA), an homogeneous, dual-recognition immunoassay, has proven to be sensitive, specific and convenient for detection or quantitation of one or multiple analytes in human plasma. In this paper, the PEA principle was applied to the detection of procalcitonin (PCT), a widely used biomarker for the identification of bacterial infection. A simple, short PEA protocol, with an assay time suitable for point-of-care diagnostics, is presented here as a proof of concept. Pairs of oligonucleotides and monoclonal antibodies were selected to generate tools specifically adapted to the development of an efficient PEA for PCT detection. The assay time was reduced by more than 13-fold compared to published versions of PEA, without significantly affecting assay performance. It was also demonstrated that T4 DNA polymerase could advantageously be replaced by other polymerases having strong 3’>5’ exonuclease activity. The sensitivity of this improved assay was determined to be about 0.1 ng/mL of PCT in plasma specimen.</p>
Trap parameters for the fast OSL signal component obtained through analytical separation for various quartz samples
<p>Dataset for article</p> <p>Trap parameters for the fast OSL signal component obtained through analytical separation for various quartz samples</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>
Dataset for Fast Li-ion Storage and Dynamics in TiO2 Nanoparticle Clusters Probed by Smart Scanning Electrochemical Cell Microscopy
<p>This dataset provides the raw data to the manuscript </p> <p><strong>"Fast Li-ion Storage and Dynamics in TiO<sub>2</sub> Nanoparticle Clusters Probed by Smart Scanning Electrochemical Cell Microscopy"</strong> published in Angewandte Chemie International Edition.</p> <p>Specifically, the following measurements are provided:</p> <ul> <li>Powder XRD data for the commercial anatase TiO<sub>2</sub>.</li> <li>Cyclic voltammetry data from SECCM measurements.</li> <li>SEM images of the TiO<sub>2</sub> nanoparticles after SECCM tip landing and electrochemical measurement.</li> </ul>
Magnetic field reversal in the turbulent environment around a repeating fast radio burst
<p>All the bursts of FRB 20190520B detected from the Green Bank Telescope (GBT) that was used in the Anna-Thomas et al (2022) paper. This dataset contains both L-Band (1.4 GHz) and C-Band (6 GHz) bursts. The data is in pulse archive format, which can be read using the software suite PSRCHIVE or the package PyPulse. The L-band bursts are not calibrated. The C-Band bursts which has an extension .calib is calibrated for both flux and polarization and those which has an extension .calibP is only calibrated for polarization. </p>
Fig.1 in Distribution Of Ixodes Ricinus (Arachnida, Ixodidae) In Ukraine In The Context Of Tick Hazard, And Factors Favoring Its Persistence In Conditions Of Fast-Going Environmental Change
Fig.1. The hiatus in the range that has been noted on the territory of Ukraine: 1 — European area of distribution; 2 — Crimean area of distribution; 3 — Caucasian-Western Asian area of distribution; 4 — zone of disjunction (after fig. 1 Akimov & Nebogatkin, 1996 with changes).
Fig. 2 in Distribution Of Ixodes Ricinus (Arachnida, Ixodidae) In Ukraine In The Context Of Tick Hazard, And Factors Favoring Its Persistence In Conditions Of Fast-Going Environmental Change
Fig. 2. Distribution of I. ricinus on the territory of Ukraine, from the point of view of tick-borne danger.
Fast(Text) Analysis
<p>This is the data used for and generated during our research for the article "Fast(Text) Analysis of Mesopotamian Divine Names" published in Vanessa Bigot Juloux, Alessandro di Ludovico, and Sveta Matskevich (eds.), <em>The Ancient World Goes Digital: Case Studies on Archaeology, Texts, Online Publishing, Digital Archiving, and Preservation</em>. Digital Biblical Studies, 6. Brill, 2023.</p> <p>We thank the Open Richly Annotated Cuneiform Corpus (Oracc) for their efforts in making linguistically annotated cuneiform texts available online. We are indebted to everyone involved in creating this research data, including the authors of the original publications and researchers who made the data Oracc-compatible and enriched it through lemmatizations and by adding other metadata. For a list of projects and their contributors, see the file OraccCredits.txt.</p> <p> </p>
Fast mvSLOUCH: Model comparison for multivariate Ornstein--Uhlenbeck-based models of trait evolution on large phylogenies
<p>These are the Supplementary Material, R scripts and numerical results accompanying Bartoszek, Fuentes Gonzalez, Mitov, Pienaar, Piwczyński, Puchałka, Spalik and Voje "Model Selection Performance in Phylogenetic Comparative Methods under multivariate Ornstein–Uhlenbeck Models of Trait Evolution".</p> <p>The four data files concern two datasets. Ungulates: measurements of muzzle width, unworn lower third molar crown height, unworn lower third molar crown width and feeding style and their phylogeny; Ferula: measurements of ratio of canals, periderm thickness, wing area, wing thickness, and fruit mass, and their phylogeny.</p>
Thermosphere Hough Mode Extensions (HMEs) for Ultra-Fast Kelvin Waves (UFKWs)
<p>The scientific basis for the UFKW HME files contained here is described in Forbes et al. (2023). The directory "Hough-UV functions" contains solutions to Laplace's Tidal Equation for the s = -1 eastward-propagating first symmetric (Kelvin) modes with periods 2.0-5.0d (48hr-120hr), and a README file containing further information on the data files. The directory "UFKW output files" provides UFKW amplitudes and phases (UT hour of maximum at 0 longitude) of eastward, southward, and vertical winds (m/s), temperatures (K), density perturbations relative to mean, and geopotential height (m) as a function of height (z, every ~4 km) and latitude (deg, every 3 deg), corresponding to wave periods between 2.0d and 5.0d, and a README file containing further information on the data files. The data for each HME extend from pole to pole and from 0 to 400 km altitude.</p> <p>Forbes, J.M., Zhang, X., & Palo, S.E. (2023). UFKW propagation in the dissipative thermosphere. Journal of Geophysical Research: Space Physics, 128, e2022JA030921. https://doi.org/10.1029/2022JA030921</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.