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400
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Dataset results
400 results for “fingerprints”
Data from: Potential of MALDI−TOF MS-based proteomic fingerprinting for species identification of Cnidaria across classes, species, regions and developmental stages
<p><span>Morphological identification of cnidarian species can be difficult throughout all life stages due to the lack of distinct morphological characters. Moreover, in some cnidarian taxa genetic markers are not fully informative, and in these cases combinations of different markers or additional morphological verifications may be required. Proteomic fingerprinting based on MALDI-TOF mass spectra was previously shown to provide reliable species identification in different metazoans including some cnidarian taxa. For the first time, we tested the method across four cnidarian classes (Staurozoa, Scyphozoa, Anthozoa, Hydrozoa) and included different scyphozoan life-history stages (polyp, ephyra, medusa) into our dataset. Our results revealed reliable species identification based on MALDI-TOF mass spectra across all taxa with species-specific clusters for all 23 analyzed species. In addition, proteomic fingerprinting was successful for distinguishing developmental stages, still by retaining a species specific signal. Furthermore, we identified the impact of different salinities in different regions (North Sea and Baltic Sea) on proteomic fingerprints to be negligible. In conclusion, the effects of environmental factors and developmental stages on proteomic fingerprints seem to be low in cnidarians. This would allow using reference libraries built up entirely of adult or cultured cnidarian specimens for the identification of their juvenile stages or specimens from different geographic regions in future biodiversity assessment studies.</span></p>
Data and code from: Sub-surface deformation of individual fingerprint ridges during tactile interactions
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Data from: Proteomic fingerprinting enables quantitative biodiversity assessments of species and ontogenetic stages in Calanus congeners (Copepoda, Crustacea) from the Arctic Ocean
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Data from: A universal tool for marine metazoan species identification – Towards best practices in proteomic fingerprinting
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Spatial fingerprinting: horizontal fusion of multi-dimensional bio-tracers as solution to global food provenance problems
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Influence of sedimentary environment evolution on fingerprint characteristics of methane isotopes: A case study from Hangzhou Bay
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Data from: Potential of MALDI−TOF MS-based proteomic fingerprinting for species identification of Cnidaria across classes, species, regions and developmental stages
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Data from: Characterizing niche differentiation among marine consumers with amino acid δ13C fingerprinting
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Calis-p 2.1 a software for protein-based stable isotope fingerprinting (Protein-SIF) and probing (Protein-SIP)
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MALDI-TOF MS data: Species delimitation of Hexacorallia and Octocorallia around Iceland using nuclear and mitochondrial DNA and proteome fingerprinting
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Data from: Evaluating species richness using proteomic fingerprinting and DNA-barcoding – a case study on meiobenthic copepods from the Clarion Clipperton Fracture Zone
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Phytoplankton optical fingerprint libraries for development of phytoplankton ocean color satellite products
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Unveiling the autoreactome: Proteome-wide immunological fingerprints reveal the promise of plasma cell depleting therapy
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Data from: Gut microbiome critically impacts PCB-induced changes in metabolic fingerprints and the hepatic transcriptome in mice
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Chemical fingerprints indicate group membership in a highly social communal breeding bird
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Data to support manuscript "Fates and fingerprints of sulfur and carbon following wildfire in economically important croplands of California, U.S."
Abstract Sulfur (S) is widely used in agriculture, yet little is known about its fates within upland watersheds, particularly in combination with disturbances like wildfire. This dataset includes samples collected within the Napa River Watershed, California, U.S., where high S applications to vineyards are common, and ~20% of the watershed burned in October 2017. The data package includes soil, soil leachate, and stream chemistry data from sites representing a combination of land use (vineyard agriculture and grasslands) and burn (burned and unburned). Bulk soil chemical measurements include total sulfur and carbon concentrations and sulfur stable isotopes. We then used a laboratory rainfall experiment to simulate a wet season of precipitation in order to compare unburned and low severity burned vineyard and grassland soil leachate chemistry. Soil leachate measurements include total dissolved sulfur, sulfate, and dissolved organic carbon concentrations, sulfate-sulfur stable isotopes, and the specific ultraviolet absorbance at 254 nm (SUVA254), an index strongly correlated with DOC aromaticity. We compared soil leachate chemistry to stream samples draining sub-catchments with differing land use and degrees of burn and severity to understand combined effects at broader spatial scales. Soil and stream chemistry are provided in separate data tables, and data from the laboratory rainfall experiment is included in the leachingexpts (leaching experimental record), leachingexpchem (chemistry), and leachingexpisotopes (sulfur stable isotopes) data tables.
Protecting against Website Fingerprinting with Multihoming – dataset
<p>This folder contains the dataset gathered for the paper Protecting against Website Fingerprinting with Multihoming, published at PoPETS 2020.</p> <p>It consists in real Tor traces gathered with different single path and multipath algorithms. The files consist in timestamp and direction (incoming or outgoing) of the Tor cells. More details about the architecture can be found in the paper.</p> <p>More details about this dataset can be found in Section 7 of our paper.</p> <p> </p>
Wi-Fi RSSI fingerprint dataset from two malls with validation routes in a shop-level for indoor positioning
<p><strong>Th</strong>e dataset is composed of the RSSI fingerprinting calibration of two malls employing four different smartphones with the random walking survey method. Moreover, the dataset includes validation routes performed with ten smartphones from various brands and labeled with the code of the shops where they were registered. For evaluation purposes, together with the samples, information related to the shopping center like the Euclidean distance between shops is provided. The files are described as follows:</p> <ul> <li> <p>building.csv’: two rows with the id of mall 1 and mall 2.</p> </li> <li> <p>‘floor.csv’: there is a row for each of the floors of the two malls. Three floors for mall 1 and two floors for mall 2 </p> </li> <li> <p>‘zones.csv’: for each zone is indicated the id of the mall and the id floor. There are 69 zones from mall 1 and 124 for mall 2 </p> </li> <li> <p>‘training.csv’: in this file are registered the ids and the description of the five different calibrations performed (one per smartphone) in each of the malls. </p> </li> <li> <p>‘training_sample.csv’: each row of the file has the RSSI in dBm recorded during the off-line calibration phase together with the timestamp, channel, and MAC of the AP.</p> </li> <li> <p>‘route.csv’: the id’s and the description of each of the validation routes are recorded in this file. </p> </li> <li> <p>‘route_sample.csv’: with a similar structure than the training_sample.csv, the RSSI, timestamp, channel, and MAC acquired during the validation routes are stored in this file.</p> </li> <li> <p>‘euclidea_distance.csv”: to evaluate the error of the different algorithms, this file recorded the euclidean distance from the center of a shop to each other in meters.</p> </li> </ul> <p>The dataset is to be cited as follows:<br> J. A. López-Pastor, A.J. Ruiz-Ruiz, A.J. García-Sánchez, J.L. Gómez-Tornero. Wi-Fi RSSI fingerprint dataset from two malls with validation routes in a shop-level for indoor positioning. Zenodo repository. DOI: 10.5281/zenodo.3698238</p>
Long-Term Wi-Fi fingerprinting dataset and supporting material
<p>WiFi measurements database for UJI's library and supporting material.</p> <p>The measurements were collected by one person using mainly one Android smartphone during 25 months at two floor of the library building from Universitat Jaume I, in Spain. It contains 103,584 WiFi fingerprints, which are organized into datasets. Each dataset is the result of a collection campaign.</p> <p>The supporting material includes Matlab® scripts to load and filter the desired data, and provides examples on possible studies that the database may enable. The supporting material also includes the bookshelves local coordinates.</p> <p>Citation request:</p> <p>Mendoza-Silva, G.M.; Richter, P.; Torres-Sospedra, J.; Lohan, E.S.; Huerta, J. Long-Term WiFi Fingerprinting Dataset for Research on Robust Indoor Positioning. <em>Data</em> <strong>2018</strong>, <em>3</em>, 3.</p> <p>G.M. Mendoza-Silva, P. Richter, J. Torres-Sospedra, E.S. Lohan, J. Huerta, "Long-Term Wi-Fi fingerprinting dataset and supporting material", Zenodo repository, DOI 10.5281/zenodo.1066041.</p> <p> </p>
The DSI dataset for Wi-Fi fingerprinting using mobile devices
<p>The provided data were collected at the first floor of building 11 of the University of Minho, Portugal, Azurém Campus (Department of Information Systems – DSI), between 2 and 3 of May 2016.</p> <p>Data was collected by a single person, using a Nvidia Shield tablet running Android 6.0.</p> <p> </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.