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400 results for “fingerprints”
Temperature and Climate Attribution estimates supporting "Human Fingerprints on Daily Temperatures in 2022" (2x2 degrees, 2022)
<p>These data support the publication of "Human Fingerprints on Daily Temperatures in 2022" published in the <a href="https://www.ametsoc.org/index.cfm/ams/publications/bulletin-of-the-american-meteorological-society-bams/explaining-extreme-events-from-a-climate-perspective/">BAMS-EEE special issue</a> in 2024 (DOI: <a href="https://doi.org/10.1175/BAMS-D-23-0264.1">10.1175/BAMS-D-23-0264.1</a>). Included are:</p> <ul> <li>Temperatures: <strong>Gilfordetal2024_BAMS-EEE_T2022.nc</strong></li> <li>Attributions estimates (Climate Shift Index and Change in Information due to Perspective): <strong>Gilfordetal2024_BAMS-EEE_ChIP2022.nc</strong></li> </ul> <p>And an accompanying land-sea mask from ERA5 (<strong>Gilfordetal2024_BAMS-EEE_LandSeaMask.nc</strong>). All data values valid for the 2022 calendar year and interpolated to a 2x2 degrees spatial grid to support the study's analysis.</p> <p>For more information on this dataset or to follow up, please contact Daniel Gilford (<a href="mailto:dgilford@climatecentral.org" target="_blank" rel="noopener">dgilford@climatecentral.org</a>).<br><br><em>Funding for this work was provided by the Bezos Earth Fund, The Schmidt Family Foundation, High Meadows Foundation, and the William and Flora Hewlett Foundation.</em></p>
A unified template for sediment source fingerprinting databases
<p>Over the last few years, the sediment source fingerprinting community has been engaged in promoting best practices to improve the design and the implementation of sediment fingerprinting techniques (<a href="https://doi.org/10.1007/s11368-022-03203-1">Evrard et al., 2022</a>). Data sharing is a key part of open science making research more reliable and accessible to the community. To move forward and improve data sharing, we propose these templates for databases and metadata.</p> <p>These templates include: common metadata for samples (soil, river flood deposit, sediment core...) description (name, IGSN, location, sampling date...), list and description of common properties (elemental geochemistry, organic matter, radionuclides…) used in sediment source fingerprinting studies. These templates are intended to evolve thanks to the participation of the community, as part of a collaborative project.</p> <p>In addition, the <strong>collectionneur </strong>R package was designed to help researchers and data managers maintain an up-to-date and well-organized database. is avalaible on <a href="https://github.com/tchalauxclergue/collectionneur"><strong>GitHub</strong> (https://github.com/tchalauxclergue/collectionneur)</a> and <a href="https://doi.org/10.5281/zenodo.15146958"><strong>Zenodo</strong> (https://doi.org/10.5281/zenodo.15146958)</a>. It facilitates the comparison and integration of new data entries into an existing database while keeping a detailed report of all modifications. All database formats are allowed, although it was initially designed for sediment source fingerprinting databases.</p> <p>Published databases following these templates are listed in the References section below. </p>
Organic Matter, Geochemical, Visible Spectrocolorimetric Properties, Radiocesium Properties, and Grain Size of Potential Source Material, Target Sediment Core Layers and Laboratory Mixtures for Conducting Sediment Fingerprinting Approaches in the Mano Dam Reservoir (Hayama Lake) Catchment, Fukushima Prefecture, Japan
<p>The current dataset was compiled to study sediment fingerprintings practices, i.e tracer selection and contribution modelling. Organic matter, elemental geochemistry, visible difuse spectrocolorimetric properties, radiocesium properties, and grain size were analysed were analysed in potential source material that may supply sediment to coastal rivers, here the upper part of the Mano river, draining the main Fukushima radioactive pollution plume (Japan). Four potential soil source materials (<em>n</em> = 68) were considered: undecontaminated cropland (<em>n</em> = 24), as non-decontaminated soil before the application of local decontamination policies, remediated cropland (<em>n</em> = 10), as decontaminated soil after the application of local decontamination policies, forest soils (n = 24) and subsurface material originating from channel bank collapse or landslides (<em>n</em> = 10; referred to as subsoil). A sediment core was collected in the Mano Dam lake (Hayama lake) on the 6th June 2021 and was sectionned into 1-cm layers (<em>n</em> = 38). Laboratory mixtures (<em>n</em> = 27) were made to assess different contribution levels from the sources.</p> <p>The current dataset comprises four .csv files including data and metadata information and their respective descriptions of variables. The data set is composed of soil samples, sediment core layer and laboratory mixtures. Laboratory mixtures were prepared to provide a dataset to calibrate/validate un-mixing models implemented to address this research question and analysed in the same conditions and using the same equipment as the source/target material.</p> <p>Recommended encoding format: <strong>latin1</strong></p>
Geochemistry of soils and eroded suspended sediments from two large rural catchments in southern Brazil for studies on Suspended Sediment Fingerprinting
<p> <strong>1. Introduction</strong></p> <p>This dataset comes from a research project entitled "Water and pollutants, from cropfields to cities: evaluation and improved of soil management technologies in a catchment network " supported by the Foundation for Research Support of the State of Rio Grande do Sul (FAPERGS) and National Council for Scientific and Technological Development (CNPq) (process n°10/0034-0). The project was carried out between 2010 and 2014 under the coordination of José Miguel Reichert and Danilo Rheinheimer dos Santos, professors at the Federal University of Santa Maria. One of the aims of this project was to understand the main pollutant transfer process from hillslopes to fluvial systems in large rural catchments representative of the agricultural production system in Southern Brazil. In this context, the Suspended Sediment Fingerprinting (SSF) was extremely useful for quantifying the origin of the sediment yield monitored at the outlet of these catchments. Among the various works carried out in this project, we highlight Tales Tiecher's doctoral thesis (Tiecher, 2015) that explored the SSF in many catchments, including the Conceição and Guaporé river basins.</p> <p><strong>2. Material and Methods</strong></p> <p>The catchments represent the magnitude of erosive and hydrological processes representative of Southern Brazil. The Conceição catchment has a drainage area of 804 km<sup>2</sup> (28°27′22″S and 53°58′24″ W). According to Köppen, the climate is Cfa type, with an annual rainfall between 1,750 and 2,000 mm. Geology is riodacithe basalt, with a formation of deep and highly weathered soils (Oxisols, Ultisols, and Alfisols). The relief is characterized by gentle slopes (6–9 %) on top and hillside slopes and higher steepness (10–14%) near the drainage channels. Farming based on the production of soybeans (<em>Glycine max</em>) in summer and wheat (<em>Triticumspp.</em>), oats (<em>Avena strigosa</em>), and ryegrass (<em>Lolium multiflorum</em>) in winter. The Guaporé catchment has a drainage area of 1,980 km<sup>2</sup> (28°54′41″S and 51°57′10″W), it covers part of the meridional plateau border. The climate is classified as Cfa, with annual rainfall varies between 1,400 and 2,000 mm. Geology is characterized by volcanic lava flows, and topography is undulating to hilly. Due to variations in landscape, several classes of soils (Entisols, Luvisol, Cambisol, Oxisol, Ultisol, and Chernosol). The land use is highly heterogeneous. In the upper third of the catchment, there is a predominance of soybean cultivated under no-tillage soil management. In the other two-thirds (middle and lower parts), land use and soil management are very heterogeneous. The main land uses are tobacco (<em>Nicotiana tabacum</em>) and maize (<em>Zea mays</em>) crops, Eucalyptus (<em>Eucalyptus</em> spp.), as well as pastures for dairy cattle. The contribution of unpaved roads is relevant to the sediment yield in both catchments (Didoné et al., 2014). Composite samples of potential sediment sources (cropland, unpaved roads, and stream channel banks) were collected. Sediment source samples were taken from the surface soil layer (0–0.05 m) of cropland and unpaved roads and on exposed sites located along the river channel network. Each sample was composed of at least 10 subsamples. To obtain representative samples of suspended sediment transported in the catchment’s outlet were used three strategies: (1) to collect flood suspended sediments (FSS) through the manual sampling (USDH-48) at different periods during the rising and falling stages of floods; (2) to deploy time-integrated suspended sediment samplers (TISS), by installing the device developed by Phillips et al. (2000) at different sites within the catchments; to collect fine-bed sediment (FBS) with a suction stainless sampler limiting the loss of fine material at the bed river. Source and sediment samples were oven‐dried at 50 °C, gently disaggregated using a pestle and mortar, and then sieved to 62,5 μm. The geochemical tracers evaluated were total organic carbon estimated by wet oxidation (K<sub>2</sub>Cr<sub>2</sub>O<sub>7</sub> + H<sub>2</sub>SO<sub>4</sub>) and the total concentration of Al, Ba, Be, Ca, Co, Cr, Cu, Fe, K, La, Li, Mg, Mn, Na, Ni, P, Pb, Sr, Ti, V, and Zn using inductively coupled plasma optical emission spectrometry after microwave‐assisted digestion with concentrated HCl and HNO<sub>3</sub> (ratio 3:1) for 9.5 min at 182 °C (Tiecher, 2015; Tiecher et al. 2017, 2018).</p> <p> <strong>3. Final remarks</strong></p> <p> The SSF results provided by this dataset (Tiecher, 2015) combined with sediment yield monitoring were very important for the assessment and modeling studies in these two catchments that took place after that (Didoné et al., 2015; 2017). In addition, other studies have explored the same sample bank, expanding upon the array of tracer properties and increasing our understanding about the mechanisms of sediment and pollutant transfer in these catchments (Le Gall et al. 2017; Zafar et al., 2017; Ramon et al., 2020).</p> <p> <strong>4. References</strong></p> <p> Didoné, E. J., Minella, J. P. G., Reichert, J. M., Merten G. H., Dalbianco, L., Barros, C. A. P., Ramon, R. (2014) Impact of no-tillage agricultural systems on sediment yield in two large catchments in southern Brazil. J Soils Sediments 14:1287–1297.</p> <p>Didoné, E.J., Minella, J.P.G., Evrard, O. (2017). Measuring and modelling soil erosion and sediment yields in a large cultivated catchment under no-till of Southern Brazil. Soil Tillage Res. 174, 24-33. https://doi.org/10.1016/j.still.2017.05.011</p> <p>Didoné, E. J.; Minela, J. P. G.; Merten, G. H. (2015). Quantifying soil erosion and sediment yield in a catchment in southern Brazil and implications for land conservation. J. Soils Sediments 11, 2334-2346. https://doi.org/10.1007/s11368-015-1160-0</p> <p>le Gall, M., Evrard, O., Dapoigny, A., Tiecher, T., Zafar, M., Minella, J. P. G., Laceby, J. P., & Ayrault, S. (2017). Tracing sediment sources in a subtropical agricultural catchment of southern Brazil cultivated with conventional and conservation farming practices. Land Degradation and Development, 28(4). https://doi.org/10.1002/ldr.2662</p> <p>Ramon, R., Evrard, O., Laceby, J. P., Caner, L., Inda, A. v., Barros, C. A. P., Minella, J. P. G., & Tiecher, T. (2020). Combining spectroscopy and magnetism with geochemical tracers to improve the discrimination of sediment sources in a homogeneous subtropical catchment. Catena, 195, 104800. https://doi.org/10.1016/j.catena.2020.104800</p> <p>Tiecher, T. (2015). Fingerprinting sediment sources in agricultural catchments in Southern Brazil. Doctoral Dissertation in Soil Science. Universidade Federal de Santa Maria, Santa Maria, RS.</p> <p>Tiecher, T., Minella, J. P. G., Caner, L., Evrard, O., Zafar, M., Capoane, V., le Gall, M., & Santos, D. R. D. (2017). Quantifying land use contributions to suspended sediment in a large cultivated catchment of Southern Brazil (Guaporé River, Rio Grande do Sul). Agriculture, Ecosystems and Environment, 237. https://doi.org/10.1016/j.agee.2016.12.004</p> <p>Tiecher, T., Minella, J. P. G., Evrard, O., Caner, L., Merten, G. H., Capoane, V., Didoné, E. J., & dos Santos, D. R. (2018). Fingerprinting sediment sources in a large agricultural catchment under no-tillage in Southern Brazil (Conceição River). Land Degradation and Development, 29(4). https://doi.org/10.1002/ldr.2917.</p> <p>Zafar, M., Tiecher, T., Capoane, V., Troian, A., dos Santos, D.R. (2017). Characteristics, lability and distribution of phosphorus in suspended sediment from a subtropical catchment under diverse anthropic pressure in Southern Brazil. Ecol. Eng. 100, 28–45.</p>
Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models Using Similarity to Training Data
<p>The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed “similarity-based merger models” which combined the output of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints) and the structural and morphological similarities of the test compounds to training compounds. We applied these similarity-based merger models using logistic equations to weigh individual features and predicted assay hit calls of 177 assays from ChEMBL, PubChem and the Broad Institute, where the required Cell Painting annotations were available. We found that the similarity-based merger models outperformed other models with an additional 20% assays (79 out of 177 assays) with an AUC>0.70 compared with 65 out of 177 assays using structural models and 50 out of 177 assays using Cell Painting models. Our results demonstrate that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and expand the applicability domain by better extrapolating to new structural and morphology spaces.</p>
Topographic fingerprint of deep mantle subduction
<p>This dataset contains the extracted velocities (Vt, Vconv), the time, as well as the surface topographic signals (Hsurf, Hdyn) of the models presented in the paper.</p>
Additional TAU datasets for Wi-Fi fingerprinting-based positioning
<p><strong>1. Contents</strong></p> <p>This document describes two datasets collected at Tampere University facilities with samples taken from a Wi-Fi network interface for experiments with indoor positioning based on Wi-Fi fingerprinting.</p> <p>To reference this dataset, please use</p> <p>E.S. Lohan et al. “Additional TAU datasets for Wi-Fi fingerprinting-based positioning” 10.5281/zenodo.3819917</p> <p>Additional reference using these datasets</p> <p><em>Torres-Sospedra, J.; Quezada-Gaibor, D.; Mendoza-Silva, G. M.; Nurmi, J.; Koucheryavy, Y. and Huerta, J. New Cluster Selection and Fine-grained Search for k-Means Clustering and Wi-Fi Fingerprinting Proceedings of the Tenth International Conference on Localization and GNSS (ICL-GNSS), 2020.</em></p> <p><strong>Dataset format</strong></p> <p>Two independent datasets are provided, they are in different folders, namely “Database_Building01” and “Database_Building02” respectively. Each dataset includes two sets of samples:</p> <ul> <li>radio map – a set of Wi-Fi samples collected at a grid of points (reference points);</li> <li>evaluation – a set of Wi-Fi samples randomly collected in the evaluation area.</li> </ul> <p>Two files are provided for each set that include the rss vectors and the coordinates. For the radio map, the provided files have their names starting with “rm_”; for the evaluation, the evaluation files have their names starting with “eval_”. For instance, for the radio map they are:</p> <ul> <li>rm_crd.csv: holds coordinates (x,y)and floor identifier (z) where the samples were collected;</li> <li>rm_rss.csv: holds the measured RSSI values from each of the Access Points (AP) detected in each sample;</li> </ul> <p>All the file are described in the same format, and all files are CSV – Comma Separated Values plain text (UTF-8).</p> <p><strong>Coordinates:</strong> Each sample is associated to a pair of coordinates in a 2D Euclidean reference system. The origin of the reference system was chosen arbitrarily for convenience. The units are meters. Therefore, distances between points can be easy calculated. Moreover, the floor identifier is included to enable 3D positioning.</p> <p><strong>RSSI values:</strong> The RSSI values provided as read from the Wi-Fi network interface through the Android API. In each sample, a value of +100 was assigned to each AP not detected during a measurement. No information is provided about the MAC addresses of the APs. However, in the files, the same order is used for all samples, meaning that the values in each column are all associated to the same AP.</p> <p>Both datasets are independent and none of the provided files include an identifier for each sample. The values in the two provided files are associated by the line number, meaning that the coordinates and RSSI values in the same line, in each file, refer to the same sample.</p>
[Dataset] FP-Redemption: Measuring Browser Fingerprinting Adoption for the Sake of Web Security
<p>Full dataset for the paper "FP-Redemption: Measuring Browser Fingerprinting Adoption for the Sake of Web Security"</p> <p>5 files are provided:</p> <ul> <li>dataset.csv. The raw elements collected when browsing the web. Each entry corresponds to one attribute being accessed with one parameter combination by one script on one webpage. A single attribute with the same parameters can be accessed several times. It is represented with the key <em>nbTimes</em></li> <li>domainTags.csv: For each website, it provides its category and country tag.</li> <li>webpageTags.csv: For each webpage, it provides its type.</li> <li>fingerprinters.zip/<filenumber>.js: Our fingerprinters. Out of the 199 we requested, 7 are missing, leading in 192 js files.</li> <li>mapping.csv. 3 columns CSV file: <ul> <li>The first one lists the 199 fingerprinters detected by our algorithm.</li> <li>The second one gives the <filenumber> used to link a fingerprinter and its file in the directory.</li> <li>The third one gives the groups the fingerprinters belongs to. By default, each fingerprinter belongs to his own group. However, several fingerprinters are belonging to the same group as we evaluate there were duplicates. Thus, the number of distinct groups corresponds to the distinct fingerprinters we measured in our dataset: 169.</li> </ul> </li> </ul>
Geochemical sediment fingerprinting dataset from Oroua river catchment, New Zealand
<p>Geochemical dataset collected to determine key source contributions to overbank sediment deposition for specific particle size fractions as described in "Vale, S., Smith, H., Matthews, A., & Boyte, S. (2020). Determining sediment source contributions to overbank deposits within stopbanks in the Oroua River, New Zealand, using sediment fingerprinting. <i>Journal of Hydrology (New Zealand)</i>, <i>59</i>(2), 147-172." </p>
Artificial fingerprints engraved through block-copolymers as nanoscale physical unclonable functions for authentication and identification - Dataset
<p>This is the dataset of "Artificial fingerprints engraved through block-copolymers as nanoscale physical unclonable functions for authentication and identification" by Irdi Murataj, Chiara Magosso, Stefano Carignano, Matteo Fretto, Federico Ferrarese Lupi, and Gianluca Milano, Nature Communications (2024), DOI: 10.1038/s41467-024-54492-8</p> <p>Part of this was funded by the project MEMQuD, code 20FUN06. The project has received funding from the EMPIR program co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation program.</p> <p>Part of this work was supported by the European project OpMetBat, code 21GRD01. The project has received funding from the European Partnership on Metrology, cofinanced from the the European Union's Horizon Europe Research and Innovation Programme, and by Participating States.</p> <p>Part of this work was supported by the European Union - Next Generation EU under the National Recovery and Resilience Plan (NRRP), Mission 04 Component 2 Investment 3.1 | Project Code: IR0000027 - CUP: B33C22000710006 - iENTRANCE@ENL: Infrastructure for Energy TRAnsition aNd Circular Economy @EuroNanoLab.</p>
Data to reproduce the results presented in Lake et al. 2021. Journal of Soils and Sediments, https://doi.org/10.1007/s11368-021-03107-6 ("High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting")
<p>This repository contains data on (1) the absorbance data and (2) the measured concentrations, to reproduce computational results as presented in:<br> "High frequency un-mixing of soil samples using a submerged spectrophotometer in a laboratory setting – implications for sediment fingerprinting".</p> <p> <br> 1. Absorbance data (200-730 nm wavelengths):</p> <p> * Average absorbance compensated for measured concentrations (average absorbance value per concentration)<br> * Average absorbance compensated for theoretical concentrations (average absorbance value per concentration)<br> * Average raw absorbance measured (average absorbance value per concentration)<br> * Raw absorbance measured (all absorbance values for all concentrations)</p> <p> Data in all 3 files is indicated per soil sample / mixture, with corresponding fraction(s) of soil sample(s) and corresponding (theoretical) input concentration.<br> <br> 2. Measured concentration data:</p> <p> * Measured concentration (average concentrations, tested for all experiments and for all theoretical input concentrations)</p> <p> </p>
A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories
<p>Containes input data for MD simulations of 3 HSP90- small compound complexes from the paper</p> <p>A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories" from Daria B. Kokh, Bernd Doser , Stefan Richter , Fabian Ormersbach , Xingyi Cheng, Rebecca C. Wade, publishe in J. Chem. Phys. <strong>153</strong>, 125102 (2020); <a href="https://doi.org/10.1063/5.0019088">https://doi.org/10.1063/5.0019088</a></p> <ul> <li>ref.pdb - structure of the complex in PDB format</li> <li>ref.prmtop - topology file in AMBER</li> <li>ref-equal-NTP.pdb - structure after NTP equilibration </li> <li>ref-equal-NTP.rst7 - coordinates after NTP equilibration</li> <li>ref-equal-NTP.crd - coordinates after NTP equilibration </li> <li>gromacs.gro - coordinates in Gromacs format (after NTP equalibration)</li> <li>gromacs.top - Gromacs topology </li> </ul> <p> </p>
Fourier-transform Infrared (FT-IR) spectroscopy fingerprints subpopulations of extracellular vesicles of different sizes and cellular origin
<p>Atomic Force Microscopy images of Large (LEV), Medium (MEV) and Small (SEV) Extrzcellular vesicles (EVs) from murine cell line B16 (B16-F10, ATCC CRL-647; Mus musculus, mouse; tissue: melanoma skin). Image size 8.3 x 8.3 um. Analysis mode: Tapping mode in air as described in Paolini et al. https://doi.org/10.1080/20013078.2020.1741174</p>
Combined unsupervised and semi-automated supervised analysis of flow cytometry data reveals cellular fingerprint associated with newly diagnosed pediatric type 1 diabetes
<p>Type 1 diabetes is a chronic autoimmune disease resulting in an immune-mediated loss of pancreatic β-cells; however, an unbiased and reproducible profiling of type 1 diabetes-specific circulating immunome at disease onset has yet to be explored. In this study, fresh whole blood was collected from a pediatric cohort of 107 patients with new-onset type 1 diabetes, 85 relatives of patients with type 1 diabetes with 0-1 islet autoantibodies, 58 patients with celiac disease or autoimmune thyroiditis and 76 healthy controls. Up to 6 mL of blood was collected from each subject into a VACUETTE® TUBE 6 ml ACD-B (Greiner). Fresh whole blood underwent red blood cell lysis, was washed and stained with specific monoclonal antibodies. Fresh whole blood samples were stained with five panels of antibodies labelled as T cells, T&NK cells, B cells, Tregs and DCs/monos encompassing main subsets of T cells, NK cells, B cells, Tregs, DCs and monocytes detected using 26 surface markers and the intracellular marker forkhead box P3 (FoxP3); for the Treg panel, intracellular staining was performed after fixation and permeabilization. Cells were acquired on a BD FACSCanto-II flow cytometer equipped with FACSDiva software (Becton Dickinson, Franklin Lakes, NJ). </p>
Continuous Long-term Wi-Fi Fingerprinting Dataset for Indoor Positioning (full version)
<p>Database with Wi-Fi samples (RSSI measurements) collected from several Raspberry Pi (RPi) 3B+ devices continuously over 2+ years. The database includes the long-term dataset from the RPi devices (with 7,435,398 Wi-Fi samples), as well as 12 site-survey datasets (with 11,140 Wi-Fi samples) conducted in this period. The site-surveys were also conducted with a RPi 3B+.</p> <p>The measurements obtained from the RPi 3B+ Wi-Fi interface include the list of detected APs, their signal strength (RSSI) and transmission channel. The list has APs from the 2.4GHz and 5GHz bands because it supports IEEE 802.11.b/g/n/ac wireless LAN. </p> <p>These data were collected at a university building, between 19 Feb. 2019 and 25 Mar. 2021.</p> <p>The supporting material includes the Python scripts to parse and analyse the data by generating various plots. It also includes the locations of the monitoring devices and the list of reference points considered in the site-surveys.</p> <p> </p> <p>A detailed description of this dataset and the data collection process can be found here:</p> <p>Silva I, Pendão C, Moreira A. Collection of a Continuous Long-Term Dataset for the Evaluation of Wi-Fi-Fingerprinting-Based Indoor Positioning Systems. <em>Sensors</em>. <strong>2022</strong>; 22(22):8585. <a href="https://doi.org/10.3390/s22228585">https://doi.org/10.3390/s22228585</a></p> <p> </p> <p>When using this dataset, please add a citation to the paper above or this citation:</p> <p>Silva, I., Pendão, C., & Moreira, A. (2022). Continuous Long-term Wi-Fi Fingerprinting Dataset for Indoor Positioning (full version) (1.1.0) [Data set]. Zenodo. <a href="Silva, I., Pendão, C., & Moreira, A. (2022). Continuous Long-term Wi-Fi Fingerprinting Dataset for Indoor Positioning (full version) (1.1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6928554">https://doi.org/10.5281/zenodo.6928554</a> </p> <p> </p> <p>The following papers have used this dataset for quantifying radio map degradation and overcoming radio map degradation in Wi-Fi fingerprinting:</p> <ul> <li>I. Silva, C. Pendão, J. Torres-Sospedra and A. Moreira, "Quantifying the Degradation of Radio Maps in Wi-Fi Fingerprinting," <em>2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN)</em>, Lloret de Mar, Spain, 2021, pp. 1-8, doi: 10.1109/IPIN51156.2021.9662558.</li> <li>I. Silva, C. Pendão, J. Torres-Sospedra and A. Moreira, "Overcoming Radio Map Degradation in Wi-Fi-based Positioning Systems," <em>2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)</em>, Nuremberg, Germany, 2023, pp. 1-6, doi: 10.1109/IPIN57070.2023.10332545.</li> </ul>
WLAN (WiFi) RSS database for fingerprinting positioning
<p>This data set contains two WLAN Received Signal Strengths (RSS) databases suitable for fingerprinting positioning. One database contains training data (Training_rss.csv, Training_coordinates.csv), the radio map, the second database contains test data (Test_rss.csv, Test_coordinates.csv), RSS measurements on a path and the coordinates of that path. The data was collected in a three-floor building at Tampere University of Technology.</p> <p>The files Training_rss.csv and Test_rss.csv represent a matrix, with a reference point per row and an access point per column. The radio map consists of 446 reference points and 489 access points. Empty RSS values are set to 100. The files Training_coordinates.csv and Test_coordinates.csv contain the reference positions, 3D coordinates in a metric local reference frame. The data format allows to use previously published software (https://doi.org/10.5281/zenodo.889797) to analyze the data.<br> <br> The data is postprocessed: The reference positions of each floor are mapped onto a regular grid with 5 meter grid point spacing and the RSS values at each reference position are spatial averages of the RSS values in the resulting cells. The test data is mapped as well, but to a grid of 1 meter grid point spacing, from which only every third value was selected.</p> <p> </p>
Kinematically collected reference fingerprint map (RFM) with the high precision tracking system for feature-based indoor positioning
<p>The offline referencing phase, one of the core phases of the fingerprinting-based indoor positioning system (FIPS), is the key stage for deploying the positioning system. The reference fingerprint map (RFM) is acquired for representing the relationship between location-relevant features and the corresponding locations and used for inferring the user’s location at the online stage. The kinematically collecting the RFM using the mobile device with the help of high precision tracking system is contributed to the community for benchmarking comparison of the indoor positioning performance. The detailed description of the data is cooming soon.<br> </p>
A Reproducible Analysis of RSSI Fingerprinting for Outdoors Localization Using Sigfox: Preprocessing and Hyperparameter Tuning (datasets)
<p>The train/validation/test sets used in the study "<strong>A Reproducible Analysis of RSSI Fingerprinting for Outdoors Localization Using Sigfox: Preprocessing and Hyperparameter Tuning</strong>".</p> <p>Preprint:<a href="https://arxiv.org/abs/1908.06851"> https://arxiv.org/abs/1908.06851</a></p> <p>Published paper: <a href="https://ieeexplore.ieee.org/document/8911792">https://ieeexplore.ieee.org/document/8911792</a></p> <p> </p> <p>The dataset used to create these sets was published in:</p> <p><a href="http://www.mdpi.com/2306-5729/3/2/13">http://www.mdpi.com/2306-5729/3/2/13</a></p> <p>The full dataset is available here:</p> <pre><a href="https://doi.org/10.5281/zenodo.1212478">https://doi.org/10.5281/zenodo.1212478</a> </pre> <p>The credit for the creation of the dataset goes to Aernouts, Michiel; Berkvens, Rafael; Van Vlaenderen, Koen; and Weyn, Maarten.</p> <p> </p>
A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN (datasets)
<p>The train/validation/test sets used in the study "<strong>A Reproducible Comparison of RSSI Fingerprinting Localization Methods Using LoRaWAN</strong>".</p> <p>Preprint: <a href="https://arxiv.org/abs/1908.05085">https://arxiv.org/abs/1908.05085</a></p> <p>Published paper: <a href="https://ieeexplore.ieee.org/document/8970177">https://ieeexplore.ieee.org/document/8970177</a></p> <p> </p> <p>The dataset used to create these sets was published in:</p> <p><a href="http://www.mdpi.com/2306-5729/3/2/13">http://www.mdpi.com/2306-5729/3/2/13</a></p> <p>The full dataset is available here:</p> <pre><a href="https://doi.org/10.5281/zenodo.1212478">https://doi.org/10.5281/zenodo.1212478</a> </pre> <p>The credit for the creation of the dataset goes to Aernouts, Michiel; Berkvens, Rafael; Van Vlaenderen, Koen and Weyn, Maarten.</p>
Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning
<p>Dataset of Javascripts used for training and testing the fingerprinting algorithms described in </p> <p>Rizzo, Valentino, Stefano Traverso, and Marco Mellia. "Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning." <em>Proceedings on Privacy Enhancing Technologies</em> 2021.1 (2021): 43-63.</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.