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400 results for “fingerprints”
BLE RSS dataset for fingerprinting radio map calibration
<p>The dataset contains Bluetooth Low Energy signal strengths measured in a fully furnished flat. The dataset was originally used in the study concerning RSS-fingerprinting based indoor positioning systems. The data were gathered using a hybrid BLE-UWB localization system, which was installed in the apartment and a mobile robotic platform equipped for a LiDAR. The dataset comprises power measurement results and LiDAR scans performed in 4104 points. The scans used for initial environment mapping and power levels registered in two test scenarios are also attached.</p> <p>The set contains both raw and preprocessed measurement data. The Python code for raw data loading is supplied.</p> <p>The detailed dataset description can be found in the <em>dataset_description.pdf</em> file.</p> <p>When using the dataset, please consider citing the original paper, in which the data were used:</p> <p>M. Kolakowski,<strong> “Automated Calibration of RSS Fingerprinting Based Systems Using a Mobile Robot and Machine Learning”</strong>, <em>Sensors</em> , vol. <em>21</em>, 6270, Sep. 2021 <a href="https://doi.org/10.3390/s21186270">https://doi.org/10.3390/s21186270</a></p> <p> </p>
Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset
<p><b>Structural Interaction Fingerprints and Machine Learning for predicting and explaining binding of small molecule ligands to RNA: a benchmark dataset.</b></p><p>Ribonucleic acids (RNA) play crucial roles in living organisms as they are involved in key processes necessary for proper cell functioning. Some RNA molecules, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, while others, e.g., bacterial riboswitches or viral RNA motifs are considered as potential therapeutic targets. Thus, the continuous discovery of new functional RNA increases the demand for developing compounds targeting them and for methods for analyzing RNA—small molecule interactions. We recently developed fingeRNAt - a software for detecting non-covalent bonds formed within complexes of nucleic acids with different types of ligands. The program detects several non-covalent interactions, such as hydrogen and halogen bonds, ionic, Pi, inorganic ion- and water-mediated, lipophilic interactions, and encodes them as computational-friendly Structural Interaction Fingerprint (SIFt). Here we present the application of SIFts accompanied by machine learning methods for binding prediction of small molecules to RNA targets. We show that SIFt-based models outperform the classic, general-purpose scoring functions in virtual screening. We discuss the aid offered by Explainable Artificial Intelligence in the analysis of the binding prediction models, elucidating the decision-making process, and deciphering molecular recognition processes.</p>
Model Weights for "Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting"
<p>Model weights for use with the SatIQ fingerprinting models used in the paper “Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting”. The models are used to authenticate Iridium satellites from high sample rate message headers.</p> <p>The data collection and model code can be found at the following URL: <a href="https://github.com/ssloxford/SatIQ">https://github.com/ssloxford/SatIQ</a></p> <p>The preprint is available on arXiv at the following URL: <a href="https://arxiv.org/abs/2305.06947">https://arxiv.org/abs/2305.06947</a></p> <p>The final trained model is <code>ae-triplet-final.h5</code>. The others are from the additional experiments and analyses described in the paper, and are included for completeness.</p> <p>When using this data, please cite the following paper: “Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting”. The BibTeX entry is given below:</p> <pre><code>@inproceedings{smailesWatch2023, author = {Smailes, Joshua and K{\"o}hler, Sebastian and Birnbach, Simon and Strohmeier, Martin and Martinovic, Ivan}, title = {{Watch This Space}: {Securing Satellite Communication through Resilient Transmitter Fingerprinting}}, year = {2023}, publisher = {Association for Computing Machinery}, booktitle = {Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security}, location = {Copenhagen, Denmark}, series = {CCS '23} }</code></pre> <p> </p>
The near‐infrared autofluorescence fingerprint of the brain
<p>The brain is a vital organ involved in most of the central nervous system disorders. Their diagnosis and treatment require fast, cost‐effective, high‐resolution and high‐sensitivity imaging. The combination of a new generation of luminescent nanoparticles and imaging systems working in the second biological window (near‐infrared II [NIR‐II]) is emerging as a reliable alternative. For NIR‐II imaging to become a robust technique at the preclinical level, full knowledge of the NIR‐II brain autofluorescence, responsible for the loss of image resolution and contrast, is required. This work demonstrates that the brain shows a peculiar infrared autofluorescence spectrum that can be correlated with specific molecular components. The existence of particular structures within the brain with well‐defined NIR autofluorescence fingerprints is also evidenced, opening the door to in vivo anatomical imaging. Finally, we propose a rational selection of NIR luminescent probes suitable for low‐noise brain imaging based on their spectral overlap with brain autofluorescence.</p>
Dataset used for fingerprinting of DNS over HTTPS responses.
<p><strong> </strong>The dataset consists of multiple different data sources:</p> <ol> <li>DoH enabled Firefox on Linux OS</li> <li>DoH enabled Firefox on Windows 10 OS</li> <li>DoH enabled Chrome on Windows 10 OS</li> </ol> <p> </p> <p>We captured the traffic from the DoH enabled web-browsers using tcpdump. To automate the process of traffic generation, we installed Google Chrome and Mozilla Firefox into separate virtual machines and controlled them with the Selenium framework shows detailed information about used browsers and environments). Selenium simulates a user's browsing according to the predefined script and a list of domain names (i.e., URLs from Alexa's top websites list in our case). The selenium was configured to visit pages in random order multiple times. For capturing the traffic, we used the default settings of each browser. We did not disable the DNS cache of the browser, and the random order of visiting webpages secures that the dataset contains traces influenced by DNS caching mechanisms. Each virtual machine was configured to export TLS cryptographic keys, that was used for decrypting the traffic using WireShark application. </p> <p>The WireShark text output of the decrypted traffic is provided in the dataset files. The detailed information about each file is provided in dataset README.</p> <p> </p> <p> </p> <p> </p>
WIDEFT: A Corpus of Radio Frequency Signals for Wireless Device Fingerprint Research
<p>The WIDEFT data corpus has been created to provide bursts from wireless devices in the spectrum of Bluetooth, WiFi, and other RF signals to further research of the acquisition and usage of wireless device fingerprints. WIDEFT was developed through the efforts of the Physical Science Laboratories (PSL) at New Mexico State University (NMSU). Data collection was recorded at PSL and at NMSU main campus and cataloged, maintained, and prepared for release at PSL.</p> <p>Please cite the following article:</p> <p>A. Bucker Siddik, D. Drake, T. Wilkinson, P. L. De Leon, S. Sandoval, and M. Campos, “WIDEFT: A Corpus of Radio Frequency Signals for Wireless Device Fingerprint Research,” <em>IEEE Int. Symp. Technol. Homel. Secur. (HST)</em>, 2021.</p>
Supporting data for: Vocal fingerprinting reveals a substantially smaller global population of the Critically Endangered cao vit gibbon (Nomascus nasutus) than previously thought
<p>These data were used in the publication "Vocal fingerprinting reveals a substantially smaller global population of the Critically Endangered cao vit gibbon (Nomascus nasutus) than previously thought", currently in review. </p><p>The acoustic measurements provided in the file were input to the clustering analyses detailed in the paper. Each row corresponds to a single male song phrase. The columns include:</p><ul><li>GroupID - the name of the gibbon group, based on manual identification of the song phrase</li><li>MFCC[1-88] - Mel-frequency cepstral coefficients as detailed in the paper</li><li>Delta[89-176] - Delta-cepstral coefficients as detailed in the paper</li><li>Duration - the length of the song phrase (in seconds)</li><li>Freq 5% (Hz) and Freq 95% (Hz) - 5th and 95th percentile frequencies, respectively</li><li>Cao and Vit - the number of "cao" and "vit" components, respectively, present in the song phrase</li><li>CutFileName - the file name of the extracted song phrase (which also acts as a unique identifier)</li><li>Representative - whether the given song phrase was 'representative' ("Yes" or "No") of a typical phrase for that male (as defined by the modal number of 'cao' and 'vit' components for males)</li></ul><p>All columns (except GroupID, CutFileName and Representative) have been standardised (i.e. centred to the mean and scaled according to the standard deviation).</p>
A User DNS Fingerprint Dataset
<p><span>Using a user DNS fingerprint allows one to identify a specific network user regardless of the knowledge of his IP address. This method is proper, for example, when examining the behavior of a monitored network user in more depth. In contrast to other studies, this work introduces a dataset for possible user identification based only on the knowledge of its DNS fingerprint created from the previously sent DNS queries.</span></p> <p><span>We created a large dataset from the real network traffic of a metropolitan Internet service provider. The dataset was created from 2.3 billion DNS queries representing 6.2 million different domain names. The data collection took place over three months from 12/2023 to 02/2024.</span></p> <p><span>The dataset contains a detailed user activity description in the sense of overall daily activity statistics and detailed 24-hour activity statistics. Each dataset record contains a list of 1137 classification attributes. The absolutely unique feature of this data set is the classification of user activity based on categories of content accessed by a user.</span></p> <p><span>The new dataset can be used for the creation of machine learning models, allowing the identification of a specific user without direct knowledge of their IP addresses or additional network location information. The dataset can also serve as a reference dataset for the creation of DNS fingerprints of users.</span></p>
Evaluation of underfloor accelerometers through fingerprinting for indoor localization
<div><strong>Fingerprinting</strong></div> <div>Code developed to test the effectiveness of an indoor positioning system where multiple accelerometers are placed under the floor and set up to collect data. This material complements the work done for the paper "Evaluation of Underfloor Accelerometers for Enabling Location-based Services in Intelligent Environments" </div> <div>and helps readers to reproduce and validate the results presented in that paper. </div> <div> </div> <p><strong>What the code does</strong><br>The execution of the main code performs the following:<br>1. generation of sensor maps through their absolute coordinates;<br>2. noise reduction on the raw data according to the average of the stress the accelerometers are subjected at quiet;<br>3. generation of the fingerprint maps per each data set;<br>4. generation of the clean ground truth files (deleting coordinates set to zero);<br>5. computation of the n-dimensional distance between observations at a given time step and the euclidean error between the minimum distance value coordinates and the respective temporally closest ground truth ones;<br>6. same as in 5 but with intra-user fingerprint maps;<br>7. same as in 5 but with inter-user fingerprint maps;<br>8. same as in 5 but with enhanced inter-user fingerprint maps.</p> <p><strong>To run the code please read the file README.md</strong></p>
Peptide Mass Fingerprint Library of Monoclonal Murine anti-SARS-CoV-2 Antibodies
<p>The dataset contains fingerprints of monoclonal antibodies that can be used to identify the antibodies by using the open-source software ABID 2.0 <a href="https://bam.de/ABID">https://bam.de/ABID</a><br> More information can be found in the publication where this data was used to rapidly distinguish between 35 monoclonal murine Anti-SARS-CoV-2 antibodies: <a href="https://doi.org/10.3390/antib11020027">https://doi.org/10.3390/antib11020027 </a></p>
Magnetoencephalographic spectral fingerprints differentiate evidence accumulation from saccadic motor preparation in perceptual decision-making
<p>This repository contains the dataset of the paper "Magnetoencephalographic spectral fingerprints differentiate evidence accumulation<br> from saccadic motor preparation in perceptual decision-making".</p> <p>The dataset contains the MEG power spectrum of 16 subjects in the source space of a perceptual decision making experiment.</p>
Construction of a SNP fingerprinting database and population genetic analysis of 329 cauliflower cultivars
<p>The VCF file contains the information of 1662 SNP sites of 820 cauliflower inbred lines that were filtered according to a series of stringent conditions.</p>
Fig. 3 in T H E I P B S T E C H N I Q U E A P P L I C At I O N F O R D N A Fingerprinting Of Perch
Fig. 3. PCR products of DNA samples extracted from blood (B) and muscle (T) tissues and amplified by the primer 2080.
Fig. 2 in T H E I P B S T E C H N I Q U E A P P L I C At I O N F O R D N A Fingerprinting Of Perch
Fig. 2. PCR products of four samples from lakes Babites (B) and Kāla (K) and negative control (-) grouped by two different retrotransposon-based primers.
Fig.1 in T H E I P B S T E C H N I Q U E A P P L I C At I O N F O R D N A Fingerprinting Of Perch
Fig.1. Quality testing of DNA using 1.7% agarose gel electrophoresis for 2 hours at 80 V. The high concentrations of genomic DNA was found in samples: 1, 2, 3, 4, 6, 7, 8; 5 and 9 - DNA in the sample is not sufficient for further use; 10 – DNA ladder.
Figure 17. Algorithm A9pseudocode-Efficient Filtering of Noisy Fingerprint Images
<p>The proposed algorithms refer to two directions: suppressing the noise resulted from acquisition of the fingerprints, the Gaussian noise in filters: A1 to A8 and to “salt and pepper” reduction noise in A9. The set of 9 filters make use of some methods like: &thresholds for segmentation of the digital images and adapted for filters A4, A5 and A6; &quartiles that dive ranked data set in four equal groups and they are applied on the filters: A1, A2, A4, A6, A7, A8, A9; &selections of parameters, like in: A7, A8 and the dimension of the local neighborhood (in our case 10x10 pixels);</p>
Figure 2. Algorithm A1pseudocode-Efficient Filtering of Noisy Fingerprint Images
<p>A2&uses the principle of quartiles over the entire image in conjunction with applying the quartiles calculated on small areas "locally"(Figure 2.);</p>
Figure 3. Algorithm A3pseudocode-Efficient Filtering of Noisy Fingerprint Images
<p>A3&is achieved by the extrapolation of the values that are "in the immediate" neighborhood of extremes (0 and 255), made in a "local" manner (Figure 3.);</p>
Figure 1.Algorithm A1pseudocode-Efficient Filtering of Noisy Fingerprint Images
<p>In the image processing field, the quartiles are used also for eliminating the outliers (aberrant values) and replacing them with a median value, but in this paper we would like to avoid the median value because often introduces blurring in the resulting image. The set of algorithms uses the quartiles to transform the values under or upper the quartiles Q1, Q3 (in the overall image) and the q1, q3 (in the 10X10 vicinity) in black or white, leaving the rest of pixels in greyscale or introducing other type of processing like increasing or decreasing the values of pixels in the vicinity of the quartiles or simply by locally adjust values according to the vicinity values. A1 & uses quartile (figure 1.) which applies "locally" in small areas, usually 10x10 pixels or estimated using a sample representing approximately 40% of all image data (columns) and transform;</p>
Figure 4. Algorithm A4pseudocode-Efficient Filtering of Noisy Fingerprint Images
<p>A4& uses quartiles principle applied globally, in conjunction with the thresholds referred to A3 (Figure 4.);</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.