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Fig. 5 in Determining Spatial Parameters Of The Ecological Niche Of Parus Major (Passeriformes, Paridae) On The Base Of Remote Sensing Data
Fig. 5. Distribution of pseudo absence cells: a — the distance to the presence cells is not less than 1000 meters; b — the distance to the presence cells is not less than 500 meters; c — the distance to the presence cells is not less than 250 meters; d — distance to the presence cells is not less than 100 meters.
Data from: Leme et al. (2022) New genera and a new species in the "Cryptanthoid Complex" (Bromeliaceae: Bromelioideae) based on the morphology of recently discovered species, seed anatomy, and improvements in molecular phylogeny. Phytotaxa (doi: 10.11646/phytotaxa.544.2.2)
<p>DNA sequence alignments as well as the input and output files which specify the different data partitioning schemes used for phylogenetic analyses in Leme et al. (2022) New genera and a new species in the “Cryptanthoid Complex” (Bromeliaceae: Bromelioideae) based on the morphology of recently discovered species, seed anatomy, and improvements in molecular phylogeny. Phytotaxa. (doi: 10.11646/phytotaxa.544.2.2)</p>
A historic global ground-based monthly seasonal aerosol climatology based in AERONET data: a database 1993-2013
<table class="ds-includeSet-table detailtable table table-striped table-hover"> <tbody> <tr class="ds-table-row odd "> <td class="metadata-key label-cell" title="dc.description.abstract"> </td> <td class="metadata-field word-break">We present an aerosol classification based upon AERONET level 2.0 almucantar retrieval products from the period 1993 to 2012. In the initial phase of this research we opto-physically identified five major types of Bulk Columnar Aerosol (BCA) - based solely upon intensive optical properties of spectral Single Scattering Albedo (SSA), spectral Indices of Refraction (real – RRI and imaginary - IRI), and two Angstrom Exponents (extinction – EAE and absorption - AAE). These BCA we classified as Maritime Aerosol, Dust Aerosol, Urban Industrial Aerosol, Biomass Burning Aerosol, and Mixed Aerosol. The classification of a particular observation as one of these aerosol types is determined by its five-dimensional Mahalanobis distance (MD) to the centroid of each reference cluster (itself a 5-D hyperellipsoid). To retain a greater number of AERONET sites in the study (200+), we kept the variable space to 5-D. To generate reference clusters, we only retained data points that lie within 2 MD from the data centroid. Our typology is based on AERONET retrieved quantities, which do not include low optical depth values (AOD=440nm < 0.4 as per AERONET criteria for almucantar scan inversion). The classifications obtained will be useful in interpreting aerosol retrievals from satellite borne instruments and as input for regional climate models. The result is a dataset describing the types of aerosol particles that are distinct from one another in optical properties, and a geographic distribution of those aerosol types. We used the typology scheme upon the qualifying AERONET data archive, and produced seasonal aerosol climatologies by aerosol type for each of the AERONET sites included in the study, regional aerosol climatology maps, and a time-integrated global aerosol climatology map based entirely upon ground-based photometric data. An internally hyperlinked compendium of the individual AERONET site aerosol climatologies was produced to contain the results of the first phase of this work [available at https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf]. Each of these five aerosol types can be further discriminated into specific sub-types by this same scheme. For example, optical discrimination into specific sub-types of Biomass Burning aerosol may provide insight into sources exhibiting spectrally distinct smoke properties. We then use the mathematical strategies to sort the global AERONET data retrievals into the aerosol type classified against the reference standards. We believe these strategies regarding aerosol differentiation using polarization data will be useful for analysis of the newer AERONET version 3 data retrievals, and data collected from the deployment of newer CIMEL sun-photometers (with enhanced polarization measurement capabilities) to the network. The resulting AERONET-based aerosol typology is useful for applications in aerosol optics, including forward modeling or radiative transfer for remote sensing algorithms, or evaluating radiative forcing calculations in atmospheric models.</td> </tr> </tbody> </table> <p>Necessary Reference Material:</p> <p><span><span><span><span><span><span><span><span><span><span>[1] Giordano, M. E.,<em> </em><em>On Interactions of Matter and Energy: Light and Particles in a Terrestrial Atmosphere Progress on Opto-Physical Recognition and Classification of Aerosols: </em>A PhD dissertation, University of Nevada, copyright M.E. Giordano, 294 pages, December 2019. URI: <a href="http://hdl.handle.net/11714/6686" title="http://hdl.handle.net/11714/6686">http://hdl.handle.net/11714/6686</a></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span> <a href="https://scholarworks.unr.edu/handle/11714/6686?show=full" title="https://scholarworks.unr.edu/handle/11714/6686?show=full">https://scholarworks.unr.edu/handle/11714/6686?show=full</a></span></span></span></span></span></span></span></span></span></span></p> <p>[2]<span><span><span><span><span><span><span><span><span><span> Giordano, M.E., Ward, C.S., and Hamill, P.: <em>A Compendium of Aerosol Types Based on Mahalanobis Distances and AERONET data. </em>[An internally hyperlinked compendium of seasonal aerosol and local aerosol compositions] Atmospheric Environment, 140, 213-233,2016. </span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><a href="https://doi.org/10.1016/j.atmosenv.2016.06.002" title="https://doi.org/10.1016/j.atmosenv.2016.06.002">https://doi.org/10.1016/j.atmosenv.2016.06.002</a></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span> <a href="https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf" title="https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf">https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf</a></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span>[3] </span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span>Hamill, P. J., Giordano, M. E., Ward, C.S., Giles, D., Holben, B.: <em>An AERONET - based aerosol</em></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><em> classification using the Mahalanobis distance,</em> Atmospheric Environment, Volume 140, September, pgs 213 -233, 2016. <a href="http://dx.doi.org/10.1016/j.atmosenv.2016.06.002">http://dx.doi.org/10.1016/j.atmosenv.2016.06.002</a>and also at</span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span> <a href="https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf">https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf</a></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span>[4] Hamill, Patrick, Piedra, Patricio G., Giordano, Marco, E., 2020: <em>Simulated Polarization as a Signature of Aerosol Type</em>. Atmospheric Environment, Volume 224, 117348 article ATMENVD- 19-01763, 2020. </span></span></span></span></span></span></span></span></span></span><a href="https://doi.org/10.1016/j.atmosenv.2020.117348" title="Persistent link using digital object identifier">https://doi.org/10.1016/j.atmosenv.2020.117348</a></p> <p><span><span><span><span><span><span><span><span><span><span> </span></span></span></span></span></span></span></span></span></span></p>
Data for Microkinetic modeling of the transient CO2 methanation with DFT-based uncertainties in a Berty reactor
<p>Dataset and scripts for the manuscript "Microkinetic modeling of the transient CO2 methanation with DFT-based uncertainties in a Berty reactor", which has been submitted for review. The file contains all the raw data and the evaluation of the experiments. Additionally, all scripts for the microkinetic model are provided to perform transient simulations with all 5000 methanation mechanisms investigated in the manuscript.</p>
i-SoMPE metadata of the data base of the inventory (A and B)
<p>i-SoMPE metadata of the data base of the inventories A and B: main questions and all variables</p>
Data set: On the porosity-dependent permeability and conductivity of triply periodic minimal surface based porous media
<p>This file contains all processed data from the simulations and calculations.</p>
Development and evaluation of a method to identify potential release areas of snow avalanches based on watershed delineation - source data
<p>Full data files for the paper:</p> <p>Duvillier, C., Eckert, N., Evin, G., and Deschâtres, M.: Development and evaluation of a method to identify potential release areas of snow avalanches based on watershed delineation, Nat. Hazards Earth Syst. Sci., 23, 1383–1408, https://doi.org/10.5194/nhess-23-1383-2023, 2023.</p> <p>Can be used to reproduce all the results of the paper and for further benchmarking of snow avalanche potential release area detection methods.</p>
Fig. 5 in Taxonomic status of genera of Buccininae (Neogastropoda, Buccinidae) updated based on molecular data with description of new species and corrections of nomenclature of Buccinum
Fig. 5. Buccinum bizikovi sp. nov. A–B. Holotype (ZIN 62781), SL 20.0 mm. C. Paratype (ZIN 62782), SL 20.0 mm, lateral view to show the penis. D. South-eastern Sakhalin, depth 78 m, SL 21.5 mm, ZIN 62783. E–F. Buccinum ovulum Dall, 1895 (ZIN 48103), Kurile Is, Iturup I., depth 605–620 m, SL 16.9 mm. G. Holotype (USNM 106997), Aleutian Is, Andreanof Is, Amukta Pass, depth 454 m, SL 25.6 mm. Photo G: courtesy of USNM.
Fig. 6 in Taxonomic status of genera of Buccininae (Neogastropoda, Buccinidae) updated based on molecular data with description of new species and corrections of nomenclature of Buccinum
Fig. 6. Buccinum hasegawai sp. nov. (A–C) and Buccinum bizikovi sp. nov. (D–F). A–B. Paratype (ZIN 62777), SL 16.5 mm. A. Radula. B. Operculum. C. Paratype (ZIN 62782), penis. D. Paratype (ZIN 62782), SL 16.5 mm, radula. E. Paratype (ZIN 62782), SL 19.1 mm, operculum. F. Paratype (ZIN 62782), SL 16.5 mm, penis. Scale bars: C, F = 1 mm.
Fig. 1 in Taxonomic status of genera of Buccininae (Neogastropoda, Buccinidae) updated based on molecular data with description of new species and corrections of nomenclature of Buccinum
Fig. 1. Part of the Bayesian phylogenetic tree of Buccinoidea Rafinesque, 1815 obtained with the cox- 1, 16S and 28S concatenated dataset representing family Buccinidae Rafinesque, 1815. The full tree is available as supplementary material (Supplementary file 1). Subfamily Siphonaliinae Finlay, 1928 is collapsed. Vertical numbered lines on the right mark the subfamilies of Buccinidae: 1 = Beringiinae Golikov & Starobogatov, 1975; 2 = Neptuneinae Stimpson, 1865; 3 = Volutopsinae Habe & Sato, 1973; 4 = Parancistrolepidinae Habe, 1972. Posterior probabilities and bootstrap values are shown for each medium supported node. Highly supported nodes are marked with black dots. The colors of the text refer to valid genus (orange = Volutharpa P. Fischer, 1856) and species referred to or morphologically attributable to formerly recognized (sub)genera of Buccininae: red = Thysanobuccinum Golikov & Gulbin in Golikov, 1980; blue = Ovulatibuccinum Golikov & Sirenko, 1988; green = Bathybuccinum Golikov & Sirenko, 1988.
Fig. 4 in Taxonomic status of genera of Buccininae (Neogastropoda, Buccinidae) updated based on molecular data with description of new species and corrections of nomenclature of Buccinum
Fig. 4. Buccinum hasegawai sp. nov. A–B. Holotype (ZIN 62775 (Buc274)), SL 18.5 mm. C. Paratype 1 (ZIN 62902 (Buc276)) from the type locality, SL 14.8 mm. D. Paratype 2 (MIMB 42300 (Buc273)), Urup I., 450–460 m, SL 14.7 mm. E. Paratype (ZIN 26228), Iturup I., 414 m, SL 16.1 mm. F. Paratype (ZIN 26228), Iturup I., 414 m, SL 16.2 mm, lateral view to show the penis. G. Buccinum bombycinum Dall, 1907 (ZIN 48100) Iturup I., 910–920 m, SL 15.3 mm. H–I. Syntype (USNM 1105311), Japan, Honshu I., Suruga Bay, 536 m, SL 23.9 mm. I = enlarged upper part of the shell to show the sculpture. Scale bars: A–H = same scale; I = 5 mm. Photos H–I: courtesy of USNM.
Fig. 2 in Taxonomic status of genera of Buccininae (Neogastropoda, Buccinidae) updated based on molecular data with description of new species and corrections of nomenclature of Buccinum
Fig. 2. Sequenced species of Buccinum Linnaeus, 1758 previously attributed to Thysanobuccinum Golikov & Gulbin in Golikov, 1980, Bathybuccinum Golikov & Sirenko, 1988 and Ovulatibuccinum Golikov & Sirenko, 1988. A–B. Buccinum tunicatum Golikov & Gulbin, 1977 (Buc265), Okhotsk Sea, off Urup I., depth 142 m, R/V Akademik Oparin, 56 cr., sta 7, SL 20.4 mm. C. Buccinum sp. 2 (Buc175), off Onagawa, Miyagi, Honshu I., Japan, depth 3302–3311 m, SL 20.3 mm. D–D'. Buccinum cf. tunicatum (Buc269), Okhotsk Sea coast of Urup I., 2 m, R/V Akademik Oparin, 56 cr., sta 26, SL 8.7 mm. D = at the same scale as others; D' = enlarged. E–E'. Buccinum bicordatum (Golikov & Sirenko, 1988) (Buc188), off Onagawa, Miyagi, Honshu I., Japan, depth 342–343 m, SL 8.4 mm. E = at the same scale as others; E' = enlarged. F. Buccinum unicordatum (Golikov & Sirenko, 1988) (Buc266), Kurile Is, Iturup I., Prostor Bay, depth 264–270 m, SL 15.9 mm. G. Buccinum chinoi nom. nov. (Buc190), off Onagawa, Miyagi, Honshu I., Japan, depth 342–343 m, SL 9.0 mm. H. Buccinum fimbriatum (Golikov & Sirenko, 1988) (Buc275), Kurile Is, Simushir I., depth 436 m, R/V Akademik Oparin, 56 cr., stn 19, SL 16.6 mm. I. Buccinum sp. 1 (Buc189), off Onagawa, Miyagi, Honshu I., Japan, depth 342–343 m, SL 22.4 mm. All shells (except D', E') at the same scale.
Fig. 3 in Taxonomic status of genera of Buccininae (Neogastropoda, Buccinidae) updated based on molecular data with description of new species and corrections of nomenclature of Buccinum
Fig. 3. Sequenced species of Buccinum Linnaeus, 1758, Volutharpa P. Fischer, 1856, and Plicibuccinum Golikov & Gulbin, 1977. A. Buccinum tenuisulcatum Golikov & Gulbin, 1977 (Buc264), Okhotsk Sea, Kurile Is, Onekotan I., depth 571–580 m, R/V Akademik Oparin, 56 cr., stn 68, SL 29.4 mm. B. Buccinum cyaneum Bruguière, 1789 (Buc281), Barents Sea, Teriberka Inlet, depth 10–17 m, SL 18.1 mm. C–D. Volutharpa ampullacea (Middendorff, 1848) (Buc277), Kurile Is, off Chirpoi I., depth 148–147 m, SL 14.1 mm. E. Buccinum nipponense Dall, 1907 (Buc187), off Otsuchi, Iwate, Honshu I., Japan, depth 479–484 m, SL 40.8 mm. F. Buccinum percrassum Dall, 1883 (Buc283), Kurile Is, Simushir I., 1–6 m, SL 33.5 mm. G–H. Buccinum cf. kobjakovae Golikov & Gulbin, 1977 (Buc278), Kurile Is, Simushir I., R/V Akademik Oparin, 56 cr., stn 19, 46°40.6′ N, 151°58.4′ E, depth 436 m, SL 12.1 mm. I. Plicibuccinum declivis (Habe & Ito, 1976) (Buc289), Japan Sea, Primorje, R/V Akademik Oparin, 64 cr., stn 73, 43°43.5′ N, 135°22.9′ E, depth 45–49 m, SL 36.2 mm. Shells not to scale.
Location-based augmented reality (LBAR) spatial data test
<p>This repository gathers video data (screen capture) collected on a field test conducted on the 11th of May 2022, at the HEIG-VD in Yverdon-les-Bains, Switzerland.<br> <br> The goal of the test was to submit LBAR interfaces to different sources of spatial data. The 5 conditions compared were:<br> <br> 1) ARCore interface (visual odometry) fed with position and orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU) and GNSS measurment unit.<br> 2) ARCore interface (visual odometry) fed with orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU), and with position data provided by an external REDcatch GNSS/RTK measurment unit.<br> 3) A-Frame + LBAR.js interface fed with position and orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU) and GNSS measurment unit.<br> 4) A-Frame + LBAR.js interface fed with orientation data provided by the mobile device’s embedded Inertial Measurment Unit (IMU), and with position data provided by an external REDcatch GNSS/RTK measurment unit.<br> 5) A-Frame + LBAR.js interface fed with position and orientation data provided by an external Inertial Navigation Station Xsens MTi-680g (IMU + GNSS/RTK).</p>
Data for Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments
<p>Input and output files from the manuscript analysis titled "Scan-Centric, Frequency-Based Method for Characterizing Peaks from Direct Injection Fourier transform Mass Spectrometry Experiments"</p>
Data set for the journal article Structural Analysis of Metal Coordination Sites in Single-Atom Catalysts Based on Carbon Nitrides
<p>The data is organized according to the figure in the manuscript. </p>
Code and data for: Interpolant-based demosaicing routines for dual-mode visible/near-infrared imaging systems
<p><span>Dual-mode visible/near-infrared imaging systems, including a bioinspired six-channel design and more conventional four-channel implementations, have transitioned from a niche in surveillance to general use in machine vision. However, the demosaicing routines that transform the raw images from these sensors into processed images that can be consumed by humans or computers rely on assumptions that may not be appropriate when the two portions of the spectrum contribute different information about a scene. A solution can be found in a family of demosaicing routines that utilize interpolating polynomials and splines of different dimensionalities and orders to process images with minimal assumptions.</span></p>
Galaxy Training Data for "Evaluating and ranking a set of pathways based on multiple metrics"
<pre>This dataset provides the inputs needed for the Galaxy Pathway Analysis workflow training tutorial (<a href="https://galaxy-synbiocad.org">https://galaxy-synbiocad.org</a>). This workflow asseses the performance of predicted pathways by computing 4 criteria (target product flux, thermodynamic feasibility, pathway length, and enzyme availability). A score inform the user about the best candidate pathways to produce a compound of interest. The generated output is a collection of scored and ranked heterologous pathways. The content of the dataset is as follows: - A set of pathways provided in the SBML format (Systems Biology Markup Language) to be ranked, modeling heterologous pathways such as those outputted by the RetroSynthesis workflow (<a href="https://galaxy-synbiocad.org">https://galaxy-synbiocad.org</a>). - The GEM (Genome-scale metabolic models) which is a formalized representation of the metabolism of the host organism (the model is E. coli iML1515), provided in the SBML format.</pre>
Data from: Robust Estimation of Field Inhomogeneity Map Following Magnitude-Based Water-Fat Separation with Resolved Ambiguity
<p>These data have been uploaded and shared as part of "Robust Estimation of Field Inhomogeneity Map Following Magnitude-Based Water-Fat Separation with Resolved Ambiguity". The data may be used for field inhomogeneity mapping and PDFF/R2* reconstruction. </p> <p>These data were acquired by Perspectum Ltd (https://perspectum.com/) on a healthy volunteer. Informed consent was obtained from the participant. The dataset includes a localizer series and a multi-slice series (magnitude and phase) acquired from a volunteer covering the dome of the liver, heart and lungs:</p> <ul> <li>1-localizer_haste_bh</li> <li>2-I_6_Echo_3D_32_Slice_IDEAL</li> <li>3-I_6_Echo_3D_32_Slice_IDEAL</li> </ul> <p>These data were gathered using a Siemens Prisma 3 Tesla scanner. The main dataset comprises an acquisition with thirty-two slices including the abdominal region, with slices placed away from the isocenter. The acquisition consisted of a 6‐echo (TE1=1.3 ms, ΔTE=1 ms) gradient-recalled echo (GRE) protocol designed to minimize T1 bias (3° flip angle), Pixel Bandwidth = 1565 Hz, and 232 x 256 reconstructed image size, with 5 mm slice thickness and 1.72 x 1.72 mm^2 in-plane resolution. </p>
Login Data Set for Risk-Based Authentication
<p><strong>Login Data Set for Risk-Based Authentication</strong></p> <blockquote> <p>Synthesized login feature data of >33M login attempts and >3.3M users on a large-scale online service in Norway. Original data collected between February 2020 and February 2021.</p> </blockquote> <p>This data sets aims to foster research and development for <a href="https://riskbasedauthentication.org">Risk-Based Authentication (RBA)</a> systems. The data was synthesized from the real-world login behavior of more than 3.3M users at a large-scale single sign-on (SSO) online service in Norway.</p> <p>The users used this SSO to access sensitive data provided by the online service, e.g., a cloud storage and billing information. We used this data set to study how the <a href="https://doi.org/10.14722/ndss.2016.23240">Freeman et al. (2016)</a> RBA model behaves on a large-scale online service in the real world (see <a href="#publication">Publication</a>). The synthesized data set can reproduce these results made on the original data set (see <a href="#study-reproduction">Study Reproduction</a>). Beyond that, you can use this data set to evaluate and improve RBA algorithms under real-world conditions.</p> <p><strong>WARNING:</strong> The feature values are plausible, but still <strong>totally</strong> <strong>artificial</strong>. Therefore, you should NOT use this data set in productive systems, e.g., intrusion detection systems.</p> <p><strong>Overview</strong></p> <p>The data set contains the following features related to each login attempt on the SSO:</p> <table> <thead> <tr> <th>Feature</th> <th>Data Type</th> <th>Description</th> <th>Range or Example</th> </tr> </thead> <tbody> <tr> <td>IP Address</td> <td>String</td> <td>IP address belonging to the login attempt</td> <td>0.0.0.0 - 255.255.255.255</td> </tr> <tr> <td>Country</td> <td>String</td> <td>Country derived from the IP address</td> <td>US</td> </tr> <tr> <td>Region</td> <td>String</td> <td>Region derived from the IP address</td> <td>New York</td> </tr> <tr> <td>City</td> <td>String</td> <td>City derived from the IP address</td> <td>Rochester</td> </tr> <tr> <td>ASN</td> <td>Integer</td> <td>Autonomous system number derived from the IP address</td> <td>0 - 600000</td> </tr> <tr> <td>User Agent String</td> <td>String</td> <td>User agent string submitted by the client</td> <td>Mozilla/5.0 (Windows NT 10.0; Win64; ...</td> </tr> <tr> <td>OS Name and Version</td> <td>String</td> <td>Operating system name and version derived from the user agent string</td> <td>Windows 10</td> </tr> <tr> <td>Browser Name and Version</td> <td>String</td> <td>Browser name and version derived from the user agent string</td> <td>Chrome 70.0.3538</td> </tr> <tr> <td>Device Type</td> <td>String</td> <td>Device type derived from the user agent string</td> <td>(<code>mobile</code>, <code>desktop</code>, <code>tablet</code>, <code>bot</code>, <code>unknown</code>)<a href="#fn1"><sup>1</sup></a></td> </tr> <tr> <td>User ID</td> <td>Integer</td> <td>Idenfication number related to the affected user account</td> <td>[Random pseudonym]</td> </tr> <tr> <td>Login Timestamp</td> <td>Integer</td> <td>Timestamp related to the login attempt</td> <td>[64 Bit timestamp]</td> </tr> <tr> <td>Round-Trip Time (RTT) [ms]</td> <td>Integer</td> <td>Server-side measured latency between client and server</td> <td>1 - 8600000</td> </tr> <tr> <td>Login Successful</td> <td>Boolean</td> <td><code>True</code>: Login was successful, <code>False</code>: Login failed</td> <td>(<code>true</code>, <code>false</code>)</td> </tr> <tr> <td>Is Attack IP</td> <td>Boolean</td> <td>IP address was found in known attacker data set</td> <td>(<code>true</code>, <code>false</code>)</td> </tr> <tr> <td>Is Account Takeover</td> <td>Boolean</td> <td>Login attempt was identified as account takeover by incident response team of the online service</td> <td>(<code>true</code>, <code>false</code>)</td> </tr> </tbody> </table> <p><strong>Data Creation</strong></p> <p>As the data set targets RBA systems, especially the <a href="https://doi.org/10.14722/ndss.2016.23240">Freeman et al. (2016)</a> model, the statistical feature probabilities between all users, globally and locally, are identical for the categorical data. All the other data was randomly generated while maintaining logical relations and timely order between the features.</p> <p>The timestamps, however, are not identical and contain randomness. The feature values related to IP address and user agent string were randomly generated by publicly available data, so they were very likely not present in the real data set. The RTTs resemble real values but were randomly assigned among users per geolocation. Therefore, the RTT entries were probably in other positions in the original data set.</p> <ul> <li> <p>The country was randomly assigned per unique feature value. Based on that, we randomly assigned an ASN related to the country, and generated the IP addresses for this ASN. The cities and regions were derived from the generated IP addresses for privacy reasons and do not reflect the real logical relations from the original data set.</p> </li> <li> <p>The device types are identical to the real data set. Based on that, we randomly assigned the OS, and based on the OS the browser information. From this information, we randomly generated the user agent string. Therefore, all the logical relations regarding the user agent are identical as in the real data set.</p> </li> <li> <p>The RTT was randomly drawn from the login success status and synthesized geolocation data. We did this to ensure that the RTTs are realistic ones.</p> </li> </ul> <p><strong>Regarding the Data Values</strong></p> <p>Due to unresolvable conflicts during the data creation, we had to assign some unrealistic IP addresses and ASNs that are not present in the real world. Nevertheless, these do not have any effects on the risk scores generated by the <a href="https://doi.org/10.14722/ndss.2016.23240">Freeman et al. (2016)</a> model.</p> <p>You can recognize them by the following values:</p> <ul> <li> <p>ASNs with values >= 500.000</p> </li> <li> <p>IP addresses in the range 10.0.0.0 - 10.255.255.255 (10.0.0.0/8 CIDR range)</p> </li> </ul> <p><strong>Study Reproduction</strong></p> <p>Based on our evaluation, this data set can reproduce our study results regarding the RBA behavior of an RBA model using the IP address (IP address, country, and ASN) and user agent string (Full string, OS name and version, browser name and version, device type) as features.</p> <p>The calculated RTT significances for countries and regions inside Norway are not identical using this data set, but have similar tendencies. The same is true for the Median RTTs per country. This is due to the fact that the available number of entries per country, region, and city changed with the data creation procedure. However, the RTTs still reflect the real-world distributions of different geolocations by city.</p> <p>See <a href="RESULTS.md">RESULTS.md</a> for more details.</p> <p><strong>Ethics</strong></p> <p>By using the SSO service, the users agreed in the data collection and evaluation for research purposes. For study reproduction and fostering RBA research, we agreed with the data owner to create a synthesized data set that does not allow re-identification of customers.</p> <p>The synthesized data set does not contain any sensitive data values, as the IP addresses, browser identifiers, login timestamps, and RTTs were randomly generated and assigned.</p> <p><strong>Publication</strong></p> <p>You can find more details on our conducted study in the following journal article:</p> <p><a href="https://doi.org/10.1145/3546069">Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service</a> (2022)<br> <em>Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono</em>.<br> <em>ACM Transactions on Privacy and Security</em></p> <p><strong>Bibtex</strong></p> <pre>@article{Wiefling_Pump_2022, author = {Wiefling, Stephan and Jørgensen, Paul René and Thunem, Sigurd and Lo Iacono, Luigi}, title = {Pump {Up} {Password} {Security}! {Evaluating} and {Enhancing} {Risk}-{Based} {Authentication} on a {Real}-{World} {Large}-{Scale} {Online} {Service}}, journal = {{ACM} {Transactions} on {Privacy} and {Security}}, doi = {10.1145/3546069}, publisher = {ACM}, year = {2022} }</pre> <p><strong>License</strong></p> <p>This data set and the contents of this repository are licensed under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International (CC BY 4.0)</a> license. See the <a href="LICENSE">LICENSE</a> file for details. If the data set is used within a publication, the following journal article has to be cited as the source of the data set:</p> <p>Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono: Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service. In: ACM Transactions on Privacy and Security (2022). doi: <a href="https://doi.org/10.1145/3546069">10.1145/3546069</a></p> <ol> <li> <p>Few (invalid) user agents strings from the original data set could not be parsed, so their device type is empty. Perhaps this parse error is useful information for your studies, so we kept these 1526 entries.<a href="#fnref1">↩︎</a></p> </li> </ol>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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