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400 results for “fingerprints”
Figs. 15–18 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 15–18. Habitus images of Selenophorus species, dorsal aspect. 15) S. gagatinus; 16) S. concinnus; 17) S. semirufus; 18) S. schaefferi.
Figs. 8–14 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 8–14. Habitus images and genitalia illustrations of Selenophorus species. 8–11) S. pumilus, new species, dorsal and ventral aspects, male median lobe left lateral and dorsal views; 12) S. seriatoporus, dorsal aspect; 13) S. discopunctatus, dorsal aspect; 14) S. fossulatus, dorsal aspect.
Figs. 1–7 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 1–7. Habitus images and genitalia illustrations of Selenophorus species. 1) S. contractus, dorsal aspect; 2) S. ellipticus, dorsal aspect; 3) S. granarius, dorsal aspect; 4–7) S. nonellipticus, new species, dorsal and ventral aspects, male median lobe left lateral and dorsal views.
Interaction Fingerprints for Molecular Dynamics Simulation of MC-LR and MC-LF with PPP1 - Data
<p>This data sets contains all data to reproduce the conclusions of the following manuscript to analyse, aggregate and visualise interaction fingerprints of Molecular Dynamics Simulation data. </p> <p>S. Jaeger-Honz, K. Klein, F. Schreiber: Systematic Analysis, Aggregation and Visualisation of Interaction Fingerprints for Molecular Dynamics Simulation Data, Journal of Cheminformatics 16 (28), 2024, https://doi.org/10.1186/s13321-024-00822-3.</p> <p>The scripts and libraries necessary to rerun the analysis are published under the following DOI: 10.5281/zenodo.10424417</p> <p> </p>
Datasets for "Physicochemical graph neural network for learning protein-ligand interaction fingerprints from sequence data"
<div> <p>Datasets used for implementing the <a href="https://github.com/huankoh/PSICHIC">PSICHIC</a> experiments shown in the <a href="https://doi.org/10.1101/2023.09.17.558145">manuscript</a>.</p> <p> </p> </div>
Bluetooth Low Energy Separate Channel Fingerprinting dataset with Frequency-Scanned Antennas and Monopole
<p><span>This dataset contains Bluetooth Low Energy Separate Channel (SC BLE) Fingerprinting (FP) data recorded in an indoor facility. The dataset includes Received Signal Strength Information (RSSI) data collected from two independent location systems: one created with four BLE beacons connected to four traditional monopole antennas, and the other four beacons connected to two dual-port Frequency-Scanned Leaky Wave Antennas (FS LWA). Both systems are installed covering the same 7m x 5m area located in a basement zone. </span></p> <p><span>The dataset includes a reference radiomap file for each point equidistant 50cm to generate Fingerprinting techniques. The calibrated radiomap files are included in the Calibration_21112023 folder. In the name of each file, the {x, y} position where the data were recorded is included, with a total of 130 reference points. The data labelled as P1 and P2 are the RSSI recorded from beacons connected to FS LWA1, while P3 and P4 data corresponds to RSSI obtained from beacons of FS LWA2. Finally, P5, P6, P7 and P8 data sets belong to beacons connected to individual monopole antennas. Each calibration file contains 100 samples of RSSI received from the corresponding beacons at each one of the 130 reference points forming the calibration grid. </span></p> <p><span>The dataset also includes test samples for different days. These days are labelled as day 1, 8, 15, 22, 29, 51, 86 and 94 after the calibration day 0. This way, the variation of the different SC FP BLE antenna systems’ performance over time, can be studied. This classification of the data as a function of time, is categorized in the folders with the names Test_day_XX_date. As done with the reference calibration information for day 0, a file with the RSSI collected in each reference point (within a total of 130 grid points) can be found in each folder. The name of the file indicates the reference point where the data were collected. </span></p> <p><span>Different to the reference calibration data of day 0 (where 100 RSSI samples were considered for each one of the 130 calibrated {x, y} positions), the test data obtained in different subsequent days is composed by ten samples of RSSI for each one of the 130 test point. </span></p> <p><span>Moreover, to test the performance of the systems when some modifications occur where the calibration was performed, some data tests are collected adding several offices' furniture on the days 15, 22, 29, 51 and 94 after the calibration procedure performed at day 0. These data are stored in folders with the name Test_day_XX_with_furnitures_date. Similar to the previous test data, the name of the file includes 130 reference {x,y} points, with 10 RSSI samples each, and for the corresponding eight beacons (P1..P8), which are labelled for both monopole (P5, P6, P7 and P8) and FS LWA antenna systems (P1 and P2 for FS LWA1 and P3 and P4 for FS LWA2). <span> </span></span></p>
A fingerprint of climate change across pine forests of Sweden
<p class="AbstractSummary">Needle traits of coniferous forests reflect environmental conditions and influence tree physiology and growth. Given the sensitivity of needle traits and tree growth to climate, temperature warming of ≈1°C in the past century may have influenced structure and function of high latitude forests across the globe. Here we show that throughout a ≈1,000 km transect in cold, high latitude Scots pine (<i>Pinus</i> <i>sylvestris </i>L.) forests in Sweden, which has warmed by ≈1°C in a century, needles today (2012-2017) are 9-19% longer and have 30-31% shorter life-times than in 1914-15. These century-scale shifts in needle traits were detected by sampling 74 sites from 2012-2017 along the same transect first assessed at 57 sites in 1914-1915. Geographic variation in temperature strongly explained spatial patterns of needle length and longevity in both the early 20<sup>th</sup> and 21<sup>st</sup> centuries, with Scots pine from warmer sites further to the south having longer needles and shorter needle life-times than at colder, more northern sites. Moreover, the warming of climate along the transect in the past century has likely driven the temporal shift towards longer needles with shorter life-spans. The spatial and temporal variation in needle traits is both cause and consequence of variation in Scots pine tree growth rates, which tend to be higher in warmer times and places. These century-scale changes in Scandinavian Scots pine needles represent a fingerprint of climate change on a fundamental biological element, the leaf, which are likely to be mirrored by similar changes for evergreen conifers across the boreal biome.</p>
Limitless per Customer: Understanding and Breaking User and Device Fingerprinting in Android
<p>Dataset and artifacts for the research work "Limitless per Customer: Understanding and Breaking User & Device Fingerprinting in Android"</p>
Passive breathing of earth-air: 'fingerprint' evidence from moisture records
<p><em>Data in Brief.</em></p>
Resistance of Dickeya solani strain IPO 2222 to lytic bacteriophage ΦD5 results in fitness tradeoffs for the bacterium during infection – protein mass fingerprints dataset
<p>Protein mass fingerprints (D. solani Tn5 mutants) dataset supporting the manuscript entitled: <strong>Resistance of </strong><em><strong>Dickeya solani</strong></em><strong> strain IPO 2222 to lytic bacteriophage </strong><strong>Φ</strong><strong>D5 results in fitness tradeoffs for the bacterium during infection.</strong></p>
VULDEFF: Vulnerability detection method based on function fingerprints and code differences
<p>The dataset of VULDEFF. For more information, visit our <a href="https://github.com/das-lab/Vuldeff" target="_blank" rel="noopener">GitHub</a>.</p> <p>Due to the lack of large-scale, real, and uniformly formatted vulnerability datasets in the field of software security, we realize the automatic collection and processing of vulnerability patches and vulnerability source code and build a reliable and continuously updated patch and vulnerability source code dataset. The dataset includes 9,546 patch files of CVE and 24,920 unpatched vulnerable source code files, covering 464 programs, and can be extended to support other open source projects.</p>
Library of Two Million Unique Small Molecules with Precalculated Fingerprints, Descriptors, and Cardiotoxicity Inhibition Data
<p>This repository comprises a dataset of ~2 million unique compounds saved in an hdf5 small molecule library store, which includes the following fields for each molecule:</p> <ul> <li>InChI key</li> <li>Standardized SMILES string</li> <li>Compound source</li> <li>ChEMBL identifier if the compound exists in this open access database</li> <li>1024-bit Morgan fingerprint</li> <li>2048-bit Morgan fingerprint</li> <li>881-bit PubChem fingerprints</li> <li>854 vector-length of preprocessed and standardized Mordred descriptors</li> <li>and cardiotoxicity inhibition predictions for each of the three cardiac ion channels (hERG, Nav1.5, and Cav1.2) using <a href="https://github.com/issararab/CToxPred2">CtoxPred2</a> along with the model confidence scores.</li> </ul> <p>The repository also includes a Jupyter notebook that serves as an initial guide for querying the small molecule library store. Export both files to the same folder, allocate approximately 40 GB of available memory disk space, unzip the library store, and then launch the notebook to begin querying.</p> <p><strong>Upon usage, please cite this publication:</strong></p> <ul> <li>Issar Arab, Kris Laukens, Wout Bittremieux, <strong>Semisupervised Learning to Boost hERG, Nav1.5, and Cav1.2 Cardiac Ion Channel Toxicity Prediction by Mining a Large Unlabeled Small Molecule Data Set</strong>, <em>Journal of Chemical Information and Modeling</em>, (2024). doi:<a title="DOI URL" href="https://doi.org/10.1021/acs.jcim.4c01102">10.1021/acs.jcim.4c01102</a></li> </ul>
RUFF -- Rotating UWB For Fingerprint
<p>RUFF (Rotating UWB For Fingerprint) is a dataset for exploring the UWB device authentication through radio frequency fingerprinting.</p> <p>This dataset is composed of more then 1.5 million measurments of Chanel impulse Response (CIR) of UWB signal pulses. Each mesurment is labeled with a Device ID from 1 of 13 emitting boards and with a Device Location of 1 of 100 positions.</p> <p>The data is available in raw csv files from the recording campagn and in .npy clean format for a direct usage in python. The code and Deep Learning models from this project can be found in <a href="https://anonymous.4open.science/r/UWB-fingerprint-80CB/README.md" target="_blank" rel="noopener">This git</a>.</p> <p><em>Link to the Article with more detail will be added after review.</em></p>
Simulations data for the paper "A conformational fingerprint for amyloidogenic light chains"
<p>The dataset includes the simulations data generated for the publication "a conformational fingerprint for amyloidogenic light chains". For each protein system we distributed the aggregated simulation data generated by a metadynamic metainference simulation with the corresponding statistical weights per frame. </p>
Supplementary material 1 from: Jażdżewska AM, Corbari L, Driskell A, Frutos I, Havermans C, Hendrycks E, Hughes L, Lörz A-N, Stransky B, Tandberg AHS, Vader W, Brix S (2018) A genetic fingerprint of Amphipoda from Icelandic waters – the baseline for further biodiversity and biogeography studies. In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 55–73. https://doi.org/10.3897/zookeys.731.19931
Table S1 : Explanation note: Amphipod and outgroup accession numbers in BOLD, GenBank and station data.
Research data supporting "Surface Enhanced Raman Scattering Artificial Nose for High Dimensionality Fingerprinting"
<p>Experimental raw research data supporting the publication: Kim N., Thomas M.R. et al., 2019, Nature Communications.</p>
Nonstationarity of the Atlantic Meridional Overturning Circulation's fingerprint on sea surface temperature
<p>Associated data for "Nonstationarity of the Atlantic Meridional Overturning Circulation’s fingerprint on sea surface temperature". V2 updated to include datasets for the supplemental information.</p>
Virtual screening data sets for fragmented interaction fingerprint
<p>Data sets containing docking poses, structure-activity relationship matrix (SARM) train-test splitting, and 2048 bits extended connectivity fingerprint (ECFP) descriptors for six biological targets. Poses were generated by employing molecular docking using Molecular Operating Environment (MOE) software. Details will be described in the original publication.</p>
Dataset used for the paper Nikezić et al. "Geochemical Fingerprinting of Norway Spruce from the Eastern Carpathians: Sr Isotopic and Multi-Elemental Signatures"
<p>This is a supplementary dataset to the paper entitled "Geochemical Fingerprinting of Norway Spruce from the Eastern Carpathians: Sr Isotopic and Multi-Elemental Signatures" prepared by authors Majda NIKEZIĆ, Aurel PERŞOIU, Renata FEHER, Ionel POPA, Tea ZULIANI and currently under review.</p>
Data-driven fingerprint nanomechanical mass spectrometry
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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.