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Figure 5 in Environmental control versus phylogenic fingerprint in ontogeny: The example of the development of the stalk in the genus Guillecrinus (stalked crinoids, Echinodermata)
Figure 5. Columnals of the stalk from the juvenile specimen N5, from the most proximal (a) to the distal extremity of the stalk (f). Scale bars: 0.5 mm.
Figure 7 in Environmental control versus phylogenic fingerprint in ontogeny: The example of the development of the stalk in the genus Guillecrinus (stalked crinoids, Echinodermata)
Figure 7. Modifications in the biometric profile of columnal height during stalk ontogeny in Guillecrinus neocaledonicus. Specimens of the type series.
Figure 10 in Environmental control versus phylogenic fingerprint in ontogeny: The example of the development of the stalk in the genus Guillecrinus (stalked crinoids, Echinodermata)
Figure 10. Distal columnals of the adult specimens of Guillecrinus neocaledonicus. (a–f) Increasingly distal columnals with an increasingly early development of subsidiary crests: (a–d) facets with a pentaradiate symmetry; (e) facet with a four-part symmetry inherited from a juvenile stage similar to that of Figure 5d; (f) facet with bilateral symmetry and with two generations of subsidiary crests, inherited from a juvenile stage similar to that of Figure 5f, note the growth in diameter of the axial canal resulting from resorption of the perilumen stereom of the initial fulcral ridge. Scale bars: 1 mm.
Figure 4 in Environmental control versus phylogenic fingerprint in ontogeny: The example of the development of the stalk in the genus Guillecrinus (stalked crinoids, Echinodermata)
Figure 4. Biometric profiles of the stalk of the juvenile specimens of Guillecrinus neocaledonicus. The numbers in circles refer to the symmetry of the articulations (for 5b, 5c, 39 see Figure 6). Dotted area with the single line defines transitional zone of mesistele; double vertical lines define the entire mesistele.
Figure 19 in Environmental control versus phylogenic fingerprint in ontogeny: The example of the development of the stalk in the genus Guillecrinus (stalked crinoids, Echinodermata)
Figure 19. Variations in the level of organization of the columnals during their ontogeny in relation to their position on the stalk at the juvenile stage in Guillecrinus neocaledonicus. Ontogenetic stages of Figure 16 (J1 and J2, juvenile stage; A1 and A2, adult stage). Position on the stalk at the juvenile stage (P, proxistele; M, mesistele; D, dististele). See text for explanations.
Figure 2 in Environmental control versus phylogenic fingerprint in ontogeny: The example of the development of the stalk in the genus Guillecrinus (stalked crinoids, Echinodermata)
Figure 2. Developmental stages of the stalk in the genus Guillecrinus. The shaded portion represents columnals that appear earliest during development.
Figure 3 in Environmental control versus phylogenic fingerprint in ontogeny: The example of the development of the stalk in the genus Guillecrinus (stalked crinoids, Echinodermata)
Figure 3. Morphological units versus functional units, and symmetries at two levels of integration (ossicle and stalk). (a) A columnal viewed along its proximal/distal axis; (b) symmetry produced by superimposing the proximal and distal fulcral ridges of the columnal in (a); (c) superposition of articular fulcra of all columnals along the stalk; (d) schematic diagram of morphological and functional stalk units and their interactions.
Figure 1 in Environmental control versus phylogenic fingerprint in ontogeny: The example of the development of the stalk in the genus Guillecrinus (stalked crinoids, Echinodermata)
Figure 1. External morphology of the proximal part of the arms of the stalk. (a–d) Guillecrinus neocaledonicus: (a) specimen N1, holotype; (b) specimen N2; (c, d) specimen N3, juvenile. (e–h) G. reunionensis: (e, f) specimen R2; (g, h) specimen R1, holotype. ba, basals; ib?, pseudo-infrabasals. Scale bars: 1 mm (a, b, e, g, h); 0.5 mm (c, f); 0.2 mm (d).
Model output for "Impact of intensifying nitrogen limitation of ocean net primary production is fingerprinted by nitrogen isotopes"
<p><strong>Description.</strong></p> <p>The data included in this repository is output of simulations performed with the NEMO-PISCESv2 global ocean-biogeochemical model. Simulations involved forcing the NEMO-PISCESv2 with global warming associated with historical and future emissions, as well as the historical and future trends in atmospheric nitrogen deposition. Future climate change was according to the Representative Concentration Pathway 8.5 scenario (Dufresne et al., 2013; Riahi et al., 2011), which sees rapid warming during the 21<sup>st</sup> century. Historical and future atmospheric nitrogen deposition fields were created via linear interpolation of fields produced by Hauglustaine et al. (2014) at years 1850, 2000, 2030, 2050 and 2100. To represent the amplification of deposition since 1950 (Galloway 2014), 60 % of the increase between 1850 and 2000 occurred from 1950 onwards.</p> <p>In this study, we quantified the effect anthropogenic climate change and anthropogenic increases in atmospheric nitrogen deposition on the marine nitrogen cycle. The response of the marine nitrogen cycle to these combined stressors is highly uncertain, and we therefore employed this complex model with a strong representation of nitrogen cycling in an attempt to constrain the global behaviour of this important cycle. In addition, through the addition of nitrogen isotopes to the ocean-biogeochemical model, we also explored and described how the isotopes responded to these anthropogenic forcings, and if the isotopes uniquely fingerprinted the response for potential monitoring/detection purposes.</p> <p>Our abstract reads:</p> <p>“The open ocean nitrogen cycle is being altered by increases in anthropogenic atmospheric nitrogen deposition and climate change. How the nitrogen cycle responds will determine long-term trends in net primary production (NPP) in the nitrogen-limited low latitude ocean, but is poorly constrained by uncertainty in how the source-sink balance will evolve. Here we show that intensifying nitrogen limitation of phytoplankton, associated with near-term reductions in NPP, causes detectable declines in nitrogen isotopes (δ<sup>15</sup>N) and constitutes the primary perturbation of the 21<sup>st</sup> century nitrogen cycle. Model experiments show that ~75% of the low latitude twilight zone develops anomalously low δ<sup>15</sup>N by 2060, predominantly due to the effects of climate change that alter ocean circulation, with implications for the nitrogen sources-sink balance. Our results highlight that δ<sup>15</sup>N changes in the low latitude twilight zone may provide a useful constraint on emerging changes to nitrogen limitation and NPP over the 21<sup>st</sup> century.”</p> <p> </p> <p><strong>Coordinates</strong></p> <p>Spatial resolution is global (90°S-90°N, 180°W-180°E, surface ocean to 5000 metres depth) and temporal resolution runs from years 1801 to 2100.</p> <p> </p> <p><strong>Citation.</strong></p> <p>Buchanan PJ, Aumont O, Bopp L, Mahaffey C, and Tagliabue A (2021): An isotopic fingerprint of increasingly nitrogen-limited phytoplankton in a changing oceanic nitrogen cycle. Nature Communications.</p> <p> </p> <p><strong>Files provided.</strong></p> <p>The data files provided are those that are required to create the figures for this study and/or perform key analyses (i.e. the time of emergence calculations). In the following, each figure or analysis has an associated python script and we list the data files needed to run that script.</p> <p>Python scripts can be found the lead authors GitHub at <a href="https://github.com/pearseb/PISCESiso_Ncycle_analysis">https://github.com/pearseb/PISCESiso_Ncycle_analysis</a>. </p> <p> </p> <p>Put δ<sup>15</sup>N<sub>NO3</sub> observations on model grid (<em>process-d15Nno3_observations_on_model_grid.py</em>):</p> <ul> <li>“RafterTuerena_watercolumn_d15N_no3.txt”</li> </ul> <p>Model assessment (<em>process-model_assessment.py</em>):</p> <ul> <li>“ETOPO_spinup_d15Nno3.nc”</li> <li>“ETOPO_ORCA2.0_Basins_float.nc”</li> <li>“ETOPO_ORCA2.0.full_grid.nc”</li> <li>“RafterTuerena_watercolumn_d15N_no3_gridded.npz”</li> </ul> <p>Time of emergence calculations (<em>process-compute_toe.py</em>):</p> <ul> <li>“ETOPO_picontrol_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_ndep_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_nst_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_d15n_no3_ez_utz_ltz.nc”</li> <li>“ETOPO_future_ndep_1y_d15n_pom_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_temp_ez_utz_ltz.nc”</li> <li>“ETOPO_future_1y_temp_ez_utz_ltz.nc”</li> <li>“ETOPO_picontrol_1y_npp.nc”</li> <li>“ETOPO_picontrol_ndep_1y_npp.nc”</li> <li>“ETOPO_future_1y_npp.nc”</li> <li>“ETOPO_future_ndep_1y_npp.nc”</li> <li>“ETOPO_picontrol_1y_nfix.nc”</li> <li>“ETOPO_picontrol_ndep_1y_nfix.nc”</li> <li>“ETOPO_future_1y_nfix.nc”</li> <li>“ETOPO_future_ndep_1y_nfix.nc”</li> </ul> <p>Figure 1 (<em>fig-main1.py</em>):</p> <ul> <li>“ncycle_changes.nc”</li> <li>“sources_and_sinks.nc”</li> </ul> <p>Figure 2 (<em>fig-main2.py</em>):</p> <ul> <li>“figure2D_ndep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_ndep_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15npom_signal_usingPAR.nc”</li> <li>“ETOPO_ToE_futndep_depthzones.nc”</li> <li>“ETOPO_ToE_fut_depthzones.nc”</li> <li>“ETOPO_ToE_picndep_depthzones.nc”</li> <li>“ToE_futndep_curves.txt”</li> <li>“ToE_fut_curves.txt”</li> <li>“ToE_picndep_curves.txt”</li> </ul> <p>Figure 3 (<em>fig-main3.py</em>):</p> <ul> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“ETOPO_fluxanalysis_results.nc”</li> <li>“figure2D_cc_din_e15n.nc”</li> </ul> <p>Figure 4 (<em>fig-main4.py</em>):</p> <ul> <li>“ETOPO_direct_indirect_effects.nc”</li> </ul> <p>Supp Figure 1 (<em>fig-supp1.py</em>):</p> <ul> <li>“figure_d15Nmaps.nc”</li> </ul> <p>Supp Figure 2 (<em>process-model_assessment.py</em>):</p> <ul> <li>Produced by <em>process-model_assessment.py </em>(see data above)</li> </ul> <p>Supp Figure 3 (<em>fig-supp3.py</em>):</p> <ul> <li>“d15nstats.txt”</li> </ul> <p>Supp Figure 4 (<em>fig-supp4.py</em>):</p> <ul> <li>“ndep_Tg_yr.nc”</li> </ul> <p>Supp Figure 5 (<em>fig-supp5.py</em>):y</p> <ul> <li>“ncycle_changes_climatechangeonly.nc”</li> </ul> <p>Supp Figure 6 (<em>fig-supp6.py</em>):</p> <ul> <li>“ncycle_changes_ndeponly.nc”</li> </ul> <p>Supp Figure 7 (<em>fig-supp7.py</em>):</p> <ul> <li>“figure_depthzones.nc”</li> </ul> <p>Supp Figure 8 (<em>fig-supp8.py</em>):</p> <ul> <li>“figure2D_ndep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_ndep_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_cc_d15npom_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15nno3_signal_usingPAR.nc”</li> <li>“figure2D_picdep_d15npom_signal_usingPAR.nc”</li> <li>“BGCP_ETOPO_merged_alt.nc”</li> <li>“ETOPO_ToE_futndep_depthzones.nc”</li> <li>“ETOPO_ToE_fut_depthzones.nc”</li> <li>“ETOPO_ToE_picndep_depthzones.nc”</li> <li>“BGCP_ETOPO_merged_alt.nc”</li> <li>“ToE_fut_curves.txt”</li> <li>“ToE_futndep_curves.txt”</li> <li>“ToE_picndep_curves.txt”</li> </ul> <p>Supp Figure 9 (<em>fig-supp9.py</em>):</p> <ul> <li>“figure2D_ndep_no3_utz.nc”</li> </ul> <p>Supp Figures 10 and 11 (<em>process-0D_model_phyto_frac.py</em>):</p> <ul> <li>Produced by <em>process-0D_model_phyto_frac.py</em> and no data required.</li> </ul> <p>Supp Figure 12 (<em>process-compute_toe.py</em>):</p> <ul> <li>Produced by <em>process-compute_toe.py </em>(see data above)</li> </ul> <p> </p> <p><strong>References.</strong></p> <p>Dufresne, J. L., Foujols, M. A., Denvil, S., Caubel, A., Marti, O., Aumont, O., et al. (2013). <em>Climate change projections using the IPSL-CM5 Earth System Model: From CMIP3 to CMIP5</em>. <em>Climate Dynamics</em> (Vol. 40). https://doi.org/10.1007/s00382-012-1636-1</p> <p>Galloway, J. N. (2014). The Global Nitrogen Cycle. In <em>Treatise on Geochemistry</em> (2nd ed., Vol. 10, pp. 475–498). Elsevier. https://doi.org/10.1016/B978-0-08-095975-7.00812-3</p> <p>Hauglustaine, D. A., Balkanski, Y., & Schulz, M. (2014). A global model simulation of present and future nitrate aerosols and their direct radiative forcing of climate. <em>Atmospheric Chemistry and Physics</em>, <em>14</em>(20), 11031–11063. https://doi.org/10.5194/acp-14-11031-2014</p> <p>Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., et al. (2011). RCP 8.5—A scenario of comparatively high greenhouse gas emissions. <em>Climatic Change</em>, <em>109</em>(1–2), 33–57. https://doi.org/10.1007/s10584-011-0149-y</p>
Multi-Fingerprint Dataset
<p>This dataset contains 24 molecular fingerprints calculated for a large set of structures taken from MCULE, SAVI, DUDE, LITPCBA, DEKOIS and MUV. Each zip file contains different fingerprints calculated for the molecules in the database. Each file is a CSV file starting with the molecule name/ID and followed by a string of 0s and 1s. </p>
Dataset for the paper "Website Fingerprinting: Attacking Popular Privacy Enhancing Technologies with the Multinomial Naïve-Bayes Classifier"
<p>This dataset contains website fingerprints of 775 websites analyzed in the paper "Website Fingerprinting: Attacking Popular Privacy Enhancing Technologies with the Multinomial Naïve-Bayes Classifier" published in the Proceedings of the 2009 ACM workshop on Cloud computing security (CCSW 2009, DOI: 10.1145/1655008.1655013).</p>
A reductionist paradigm for high-throughput behavioural fingerprinting in Drosophila melanogaster - DATASET 1 of 2
<p>Dataset associated with "A reductionist paradigm for high-throughput behavioural fingerprinting in <em>Drosophila </em><em>melanogaster" </em>by Jones et al "A reductionist paradigm for high-throughput behavioural fingerprinting in Drosophila melanogaster". </p> <p>See http://lab.gilest.ro/coccinella for more information</p> <p>This is archive 1 of 2</p> <p> </p> <p> </p>
Dataset for the paper: Plug and Power: Fingerprinting USB Powered Peripherals via Power Side-channel
<p>This repository contains data related to "Plug and Power: Fingerprinting USB Powered Peripherals via Power Side-channel," by Riccardo Spolaor, Hao Liu, Federico Turrin, Mauro Conti, Xiuzhen Cheng, to appear in Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), 17-20 May 2023.</p> <p>This dataset includes the labels and features extracted from the energy consumption of 82 USB peripherals under different states (i.e., Boot, On) and actions (e.g., Read, Write, Upload, Download). The dataset contains more than 175.000 segments extracted from around 20.000 power traces. We have collected the raw power traces with a National Instruments USB-6210 DAQ at a sampling rate of 10kHz. Each segment is one second long. Please, find more details about the data collection in the paper.<br> We identify a USB peripheral by its type (Device_Type), model (Device_Model), and physical device with such type and model (Device_Id). For each power trace's segment, we assign a unique identifier (Segment_Id), and we indicate the action performed (Action) and the activity/inactivity proportions (Activity_Ratio and Inactive_Ratio). The remaining columns (with the prefix "EC__") are the features extracted from segments using the tsfresh libraries for python V0.19.0 (https://tsfresh.readthedocs.io)</p> <p><strong>Please, support our work by citing our paper:</strong><br> Riccardo Spolaor, Hao Liu, Federico Turrin, Mauro Conti, Xiuzhen Cheng, "Plug and Power: Fingerprinting USB Powered Peripherals via Power Side-channel," In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), 2023.</p> <p><strong>Contact info:</strong> Riccardo Spolaor (rspolaor@sdu.edu.cn, Shandong University, Qingdao, China) and Federico Turrin (turrin@math.unipd.it, University of Padua, Padua, Italy).</p>
Database of RF fingerprinting on use case IoT devices
<p>This document is a dataset of radiofrequency signals. It is composed of 1000 signals coming emitted by 10 different devices. This dataset was developped for benchmarking machine learning methods on an Internet of Things classification task: recognizing which device emitted a signal.</p> <p>This dataset is in an adaptation of the dataset collected by Basak et al. in “Drone classification from RF fingerprints using deep residual nets” (IEEE COMSNETS conference, 2021).</p> <p>Basak et al. collected signals from six commercial drones, three drone radio-controllers and one WiFi router. The conducted the measurements in an anechoic chamber, using a universal software radio peripheral (USRP X310) placed seven meters apart from the devices . The signals were all in the 2.4 GHz ISM band and the whole 100 MHz band was received instantaneously using a receiving sampling rate of 100 MSps (i.e. the system down-converted the signal frequencies to the 0-100 MHz band to sample them correctly).</p> <p>While the original dataset by Basak et al. consisted in spectrograms of 256 frequency bins by 256 time frames, we have converted in into averaged spectra of 256 frequency bins. Furthermore, while Basak et al. have considered several noise levels, here we only consider the lowest noise level available (-60 dBm).</p> <p>The database is stored in an h5 file, a format adapted to databases. Inside the file there are two datasets: the signals (‘Signals’) and the targets (‘Targets’). The targets correspond to the ten different classes of signals: Parrot Disco (0), Q205 (1), Tello (2), MultiTx (3), Nine Eagles (4), Spektrum DX4e (5), Spectrum DX6i (6), Wltoys (7), S500 (8) and Linkys router (9).</p> <p>This dataset corresponds to the Deliverable D6.2 of the RadioSpin EU funded project.</p>
Speciation and Structures in Pt Surface Sites Stabilized by N-Heterocyclic Carbene Ligands Revealed by DNP Enhanced Indirect-ly Detected 195Pt NMR Spectroscopic Signatures and Fingerprint Analysis
<p>Raw NMR data for the paper published under DOI: 10.1021/jacs.2c08300</p>
Training data for the "Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints"
<p>There are two ZIP-files consisting of small histological image tiles that have been used to detect and quantify distinct tissue textures and lymphocyte proportions from H&E-stained clear cell renal cell carcinoma (KIRC) digital tissue sections of the Cancer Genome Atlas (TCGA) image archive and the Helsinki dataset.</p> <p>The <strong>tissue_classification </strong>file contains 300x300px tissue texture image tiles (n=52,713) representing renal cancer (“cancer”; n=13,057, 24.8%); normal renal (“normal”; n=8,652, 16.4%); stromal (“stroma”; n= 5,460, 10.4%) including smooth muscle, fibrous stroma and blood vessels; red blood cells (“blood”; n=996, 1.9%); empty background (“empty”; n=16,026, 30.4%); and other textures including necrotic, torn and adipose tissue (“other”; n=8,522, 16.2%). Image tiles have been randomly selected from the TCGA-KIRC WSI and the Helsinki datasets.</p> <p>The <strong>binary_lymphocytes </strong>file contains mostly 256x256px-sized but also smaller image tiles of Low (n=20,092, 80.1%) or High (n=5,003, 19.9%) lymphocyte density (n=25,095). Image tiles have been randomly selected from the TCGA-KIRC WSI dataset.</p> <p>All accuracy of all annotations have been double-checked. However, the classification between multiple tissue textures or lymphocyte density can be sometimes ambiguous.</p> <p>The deep learning model parameters trained with the ResNet-18 infrastructure for (1) lymphocyte and (2) texture classification are named as (1) <strong>resnet18_binary_lymphocytes.pth</strong> and (2) <strong>resnet18_tissue_classification.pth</strong>. Codes and instructions to use these are found in <a href="https://github.com/vahvero/RCC_textures_and_lymphocytes_publication_image_analysis">https://github.com/vahvero/RCC_textures_and_lymphocytes_publication_image_analysis</a>.</p> <p> </p> <p>If you use either work, please cite the publication by Brummer O et al (1) AND the TCGA Research Network (2):<br><strong>(1) </strong><strong>Brummer, O., Pölönen, P., Mustjoki, S. <em>et al.</em> Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints. <em>Br J Cancer</em> 129, 683–695 (2023). </strong><a href="https://doi.org/10.1038/s41416-023-02329-4">https://doi.org/10.1038/s41416-023-02329-4</a></p> <p><strong>(2) The results shown here are in whole or part based upon data generated by the TCGA Research Network: </strong><strong><a href="https://www.cancer.gov/tcga">https://www.cancer.gov/tcga</a></strong><strong>.</strong></p>
Data for: Prevalent fingerprint of marine macroalgae in Arctic surface sediments
<p>Macroalgal forests export much of their production, partly supporting food webs and carbon stocks beyond their habitat, but evidence of their contribution in sediment carbon stocks is poor. We test the hypothesis that macroalgae contribute to carbon stocks in arctic marine sediments. We used environmental DNA (eDNA) fingerprinting on a large-scale set of surface sediment samples from Greenland and Svalbard. We evaluated eDNA results by comparing with traditional survey and tracer methods. The eDNA-based survey identified macroalgae in 94% of the sediment samples covering shallow nearshore areas to 1,460 m depth and 350 km offshore, with highest sequence abundance nearshore and with dominance of brown macroalgae. Overall, the eDNA results reflected the potential source communities of macroalgae and eelgrass assessed by traditional surveys, with the most abundant orders being common among different methods. A stable isotope analysis showed a considerable contribution from macroalgae in sediments although with high uncertainty, highlighting eDNA as a great improvement and supplement for documenting macroalgae as a contributor to sediment carbon stocks. Conclusively, we provide evidence for a prevalent contribution of macroalgal forests in arctic surface sediments, nearshore as well as offshore, identifying brown algae as main contributors.</p>
Dataset for "Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting"
<p>Labelled dataset of Iridium “ring alert” downlink messages, including message headers captured at 25MS/s. Message metadata includes satellite and transmitter identifier, satellite position, timestamp, and estimated noise level. The dataset contains 1706556 messages.</p> <p>The dataset has been split into numpy files for each column, and further split into segments of 10000 entries each, with the format <code>{column}_{segment}.npy</code>.</p> <p>This data was originally collected for the paper “Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting”, and was 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>When using this dataset, 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>
Supplementary Datasets for "Genome-wide CRISPR off-target prediction and optimization using RNA-DNA interaction fingerprints"
<p>Supplementary Datasets for "Genome-wide CRISPR off-target prediction and optimization using RNA-DNA interaction fingerprints". The deposition contains training/testing datasets used in the article.</p>
Data for: Prevalent fingerprint of marine macroalgae in Arctic surface sediments
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.