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400 results for “fingerprints”
Data from: Proteomic fingerprinting enables quantitative biodiversity assessments of species and ontogenetic stages in Calanus congeners (Copepoda, Crustacea) from the Arctic Ocean
<p><span>Species identification is pivotal in biodiversity assessments, and proteomic fingerprinting by MALDI-TOF mass spectrometry has already been shown to reliably identify calanoid copepods to species level. However, MALDI-TOF data may contain more information beyond mere species identification. In this study, we investigated different ontogenetic stages (copepodids C1-C6 females) of three co-occurring <em>Calanus</em> species from the Arctic Fram Strait, which cannot be identified to species level based on morphological characters alone. Differentiation of the three species based on mass spectrometry data was without any error. In addition, a clear stage-specific signal was detected in all species, supported by clustering approaches as well as machine learning using Random Forest. More complex mass spectra in later ontogenetic stages as well as relative intensities of certain mass peaks were found as the main drivers of stage distinction in these species. Through a dilution series, we were able to show that this did not result from the higher amount of biomass that was used in tissue processing of the larger stages. Finally, the data were tested in a simulation for application in a real biodiversity assessment by using Random Forest for stage classification of specimens absent from the training data. This resulted in a successful stage-identification rate of almost 90%, making proteomic fingerprinting a promising tool to investigate polewards shifts of Atlantic <em>Calanus</em> species and, in general, to assess stage compositions in biodiversity assessments of Calanoida, which can be notoriously difficult using conventional identification methods.</span></p>
Data from: Evaluating species richness using proteomic fingerprinting and DNA-barcoding – a case study on meiobenthic copepods from the Clarion Clipperton Fracture Zone
<p><span>The Clarion Clipperton Fracture Zone (CCZ) is a vast deep-sea region harboring a highly diverse benthic fauna, which will be affected by potential future deep-sea mining of metal-rich polymetallic nodules. Despite the need for conservation plans and monitoring strategies in this context, the majority of taxonomic groups remains scientifically undescribed. However, molecular rapid assessment methods such as DNA-barcoding and Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) provide the potential to accelerate specimen identification and biodiversity assessment significantly in the deep-sea areas. In this study, we successfully applied both methods to investigate the diversity of meiobenthic copepods in the eastern CCZ, including the first application of MALDI-TOF MS for the identification of these deep-sea organisms. Comparing several different species delimitation tools for both datasets, we found that biodiversity values were very similar, with Pielou's Evenness varying between 0.97 and 0.99 in all datasets. Still, direct comparisons of species clusters revealed differences between all techniques and methods, which are likely caused by the high number of rare species being represented by only one specimen, despite our extensive dataset of more than 2000 specimens. Hence, we regard our study as a first approach toward setting up a reference library for mass spectrometry data of the CCZ in combination with DNA-barcodes. We conclude that proteome fingerprinting, as well as the more established DNA-barcoding, can be seen as a valuable tool for rapid biodiversity assessments in the future, even when no reference information is available.</span></p>
Data to reproduce the results presented in Lake et al. 2022. Hydrological processes, https://doi.org/10.1002/hyp.14726 ("Using particle size distributions to fingerprint suspended sediment sources – evaluation at laboratory and catchment scales")
<p>This repository contains data on particle size distribution data obtained from the laboratory and field experiments as described in Lake et al., 2022. </p> <p>The data contains the input files as needed for the modelling:</p> <p>- In the excel files the particle size distribution data for the target SS</p> <p>- In the text file the particle size distribution data from the sources.</p> <p> </p> <p>Furthermore, the data contains the resulting output files.</p> <p> </p>
Unveiling the autoreactome: Proteome-wide immunological fingerprints reveal the promise of plasma cell depleting therapy
<p>The prevalence and burden of autoimmune and autoantibody mediated disease continues to rise, yet the etiologies of many of these diseases remain unclear. Despite numerous new targeted immunomodulatory therapies, comprehensive approaches to apply and evaluate the effects of these treatments longitudinally are lacking. Here, we leverage advances in PhIPseq methodology to explore the modulation, or lack thereof, for autoreactive antibodies proteome-wide in both health and disease. We demonstrate that each individual, regardless of disease state, possesses a distinct set of autoreactivities constituting a unique immunological fingerprint, or "autoreactome", that is remarkably stable over years. In addition to uncovering important new biology, the autoreactome can be used to better evaluate the relative effectiveness of various therapies in altering autoantibody repertoires. We find that therapies targeting B-Cell Maturation Antigen (BCMA) profoundly alter an individual's autoreactome, while anti-CD19 and CD-20 therapies have minimal effects, strongly suggesting a rationale for BCMA or other plasma cell targeted therapies in autoantibody mediated diseases.</p>
Figure 5. Algorithm A5pseudocode-Efficient Filtering of Noisy Fingerprint Images
<p>A5&apply thresholds globally across the image (Figure 5.);</p>
Antiox properties and mass fingerprint data (DLI-ESI and LTP MS) for elite maize (Zea mays) hybrids in different agroecologies
<p>Study of the nutraceutical value of elite maize (Zea mays) hybrids, using mass fingerprinting by direct liquid injection (DLI) electrospray ionization (ESI) and low-temperature plasma (LTP) ionization.</p>
data set related to article Magnetic resonance fingerprinting with dictionary-based fat and water separation (DBFW MRF): A multi-component approach
<p>This record contains raw data related to article Magnetic resonance fingerprinting with dictionary-based fat and water separation (DBFW MRF): A multi-component approach</p>
Dataset Using TLS Fingerprints for OS Identification in Encrypted Traffic
<p>The dataset consists of data from three different sources; flow records collected from the university backbone network, log entries from the two university DHCP (Dynamic Host Configuration Protocol) servers and a single RADIUS (Remote Authentication Dial In User Service) accounting server. The data was collected from 2019-07-12 00:00 to 2019-07-16 23:59 with a few hours overhead on both sides of the interval for the log entries to cover long connection sessions overlapping to and from the time frame.</p> <p>We measured the flow data from the university uplink to the Internet. In the dataset, we kept only flows with source IP addresses from university wireless networks (Eduroam). The flow data was then enriched with information from DHCP and RADIUS servers to contain ID of the RADIUS session and operating system od the transmitting device as derived from DHCP logs.</p> <p>The dataset is in the form of CSV file with the following information fields important for OS identification:</p> <ul> <li>Basic flow features <ul> <li>Date flow start - timestamp of flow start</li> <li>Date flow end - timestamp of flow end</li> <li>Src IPv4 - source IPv4 address</li> <li>sPort - source L4 port</li> <li>Dst IPv4 - destination IPv4 address</li> <li>dPort - destination L4 port</li> </ul> </li> <li>Extended TCP/IP parameters <ul> <li>SYN size - the size of the initial SYN packet of a TCP connection (in bytes)</li> <li>TCP win - value of TCP Window size parameter</li> <li>TCP SYN TTL - observed TTL value</li> </ul> </li> <li>HTTP parameters <ul> <li>HTTP Host - hostname from the HTTP request</li> <li>HTTP UA OS - OS identification based on user-agent</li> <li>HTTP UA OS MAJ - OS identification based on user-agent</li> <li>HTTP UA OS MIN - OS identification based on user-agent</li> <li>HTTP UA OS BLD - OS identification based on user-agent</li> </ul> </li> <li>TLS parameters <ul> <li>TLS SNI - Server Name Indication field</li> <li>TLS SNI length - length of SNI in bytes</li> <li>TLS Client Version - TLS client hello Version field</li> <li>Client Cipher Suites - list of supported cipher suites</li> <li>TLS Extension Types - list of extension IDs</li> <li>TLS Extension Lengths - list of extension lengths</li> <li>TLS Elliptic Curves - list of supported curves (or supported groups in TLS1.3)</li> <li>TLS EC Point Formats - list of EC formats</li> </ul> </li> <li>Log based extensions <ul> <li>Session ID - ID of the session to match flows from one device</li> <li>Ground Truth OS - OS name derived from log data</li> </ul> </li> </ul> <p>The observed network traffic contains privacy-sensitive information. Hereby, we declare that the monitored data used for our research were processed in accordance with the EU General Data Protection Regulation 2016/679. The published dataset was anonymized with cryptographic means using Crypto-PAn algorithm to preserve both the scientific value and user privacy.</p> <p>When using this dataset, please cite the original work as follows:</p> <pre><code>@inproceedings{lastovicka2020using, title={Using TLS Fingerprints for OS Identification in Encrypted Traffic}, author={La{\v{s}}tovi{\v{c}}ka, Martin and {\v{S}}pa{\v{c}}ek, Stanislav and Velan, Petr and {\v{C}}eleda, Pavel}, booktitle = {2020 IEEE/IFIP Network Operations and Management Symposium (NOMS 2020)}, doi = {http://dx.doi.org/10.1109/NOMS47738.2020.9110319}, keywords = {OS fingerprinting;passive monitoring;IPFIX;TLS}, isbn = {978-1-7281-4973-8}, pages = {1-6}, publisher = {IEEE Xplore Digital Library}, year = {2020} }</code></pre> <p> </p>
Metabolic fingerprints for suboptimal mycorrhizal colonization in wild-type and the jasmonic acid deficient spr2 tomato mutant
<p>Raw data for metabolic fingerprinting of tomato roots by DLI-ESI-MS and GC-MS to examine the effect of mycorrhizal colonization on the global metabolic profile of WT and <em>spr2</em> mutant plants.</p>
System Fingerprint Recognition for Deepfake Audio (SFR) - Compressed Set
<div>The rapid progress of deep speech synthesis models has posed significant threats to society such as malicious manip</div> <div>ulation of content. This has led to an increase in studies aimed at detecting so-called “deepfake audio”. However, existing works</div> <div>focus on the binary detection of real audio and fake audio. In real-world scenarios such as model copyright protection and</div> <div>digital evidence forensics, it is needed to know what tool or model generated the deepfake audio to explain the decision. This</div> <div>motivates us to ask: ‘Can we recognize the system fingerprints of deepfake audio?’ In this paper, we present the first deepfake</div> <div>audio dataset for System Fingerprint Recognition (SFR) and conduct an initial investigation. We collected the dataset from</div> <div>the speech synthesis systems of seven Chinese vendors that use the latest state-of-the-art deep learning technologies, including</div> <div>both clean and compressed sets. In addition, we provide extensive benchmarks and research findings to facilitate the further development of system fingerprint recognition methods. The dataset is publicly available. </div> <div> </div> <div>The subsets 01, 02, and 03 represent the training set, development set, and test set, respectively.</div> <div> </div> <div> <div>This data set is licensed with a CC BY-NC-ND 4.0 license.</div> </div>
System Fingerprint Recognition for Deepfake Audio (SFR) - Clean Set
<div>The rapid progress of deep speech synthesis models has posed significant threats to society such as malicious manip</div> <div>ulation of content. This has led to an increase in studies aimed at detecting so-called “deepfake audio”. However, existing works</div> <div>focus on the binary detection of real audio and fake audio. In real-world scenarios such as model copyright protection and</div> <div>digital evidence forensics, it is needed to know what tool or model generated the deepfake audio to explain the decision. This</div> <div>motivates us to ask: ‘Can we recognize the system fingerprints of deepfake audio?’ In this paper, we present the first deepfake</div> <div>audio dataset for System Fingerprint Recognition (SFR) and conduct an initial investigation. We collected the dataset from</div> <div>the speech synthesis systems of seven Chinese vendors that use the latest state-of-the-art deep learning technologies, including</div> <div>both clean and compressed sets. In addition, we provide extensive benchmarks and research findings to facilitate the further development of system fingerprint recognition methods. The dataset is publicly available. </div> <div> </div> <div>The subsets 01, 02, and 03 represent the training set, development set, and test set, respectively.</div> <div> </div> <div> <div>This data set is licensed with a CC BY-NC-ND 4.0 license.</div> </div>
Ecoacoustic Study Design Variation: Impact on Acoustic Indices and AudioSet Fingerprints
<b>Description: </b><p>Acoustic Index and AudioSet Fingerprint data quantified derived from audio recorded between the 26th of February and the 2nd March 2019.<br><br>The original raw audio was compressed, shortened and temporally subset to replicate common inconsistencies in ecoacoustic studies. This data frame show how this experimental variation affects how soundscapes are quantified by Analytical Indices and the AudioSet Fingerprint</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="http://127.0.0.1:8000/projects/project_view/200"><b>3D Acoustics for Audio Monitoring of Rainforest Biodiversity </b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (NERC QMEE CDT Studentship, NE/P012345/1, <a href="http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A">http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A</a>)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="http://127.0.0.1:8000/datasets/xml_metadata?id=5153193">here</a></p><p><b>Files: </b>This consists of 1 file: Ecoacoustic_Method_Comparison.xlsx</p><p><b>Ecoacoustic_Method_Comparison.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Analytical index values under differing experimental conditions</b> (described in worksheet Analytical_Index_Data)</p><p>Description: This dataset contains all the analytical indices derived from audio under different experimenatal conditions</p><p>Number of fields: 16</p><p>Number of data rows: 87211</p><p>Fields: </p><ul><li><b>id.no</b>: Sample ID (Field type: id)</li><li><b>file.size</b>: File size as a % of uncompressed (Field type: numeric)</li><li><b>compression</b>: Compression level (Mp3) (Field type: ordered categorical)</li><li><b>frame.size</b>: Frame size (recording length) (Field type: ordered categorical)</li><li><b>site</b>: Field Site Location (Field type: location)</li><li><b>req.freq</b>: Recording Frequency (Field type: numeric)</li><li><b>date</b>: Date (Field type: date)</li><li><b>time</b>: Time ID (including subsamples) (Field type: id)</li><li><b>max.freq</b>: Nyquist (Maximum) frequency (Field type: numeric)</li><li><b>ACI</b>: Acoustic Complexity Index (Field type: numeric)</li><li><b>ADI</b>: Acoustic Diversity Index (Field type: numeric)</li><li><b>Aeev</b>: Acoustic Eveness (Field type: numeric)</li><li><b>Bio</b>: Biodiversity Index (Field type: numeric)</li><li><b>H</b>: Acoustic Entropy (Field type: numeric)</li><li><b>M</b>: Median of Acoustic Envelope (Field type: numeric)</li><li><b>NDSI</b>: Normalised Difference Soundscape Index (Field type: numeric)</li></ul></li><li><p><b>AudioSet Fingerprint values under differing experimental conditions</b> (described in worksheet AudioSet_Fingerprint_Data)</p><p>Description: This dataset contains all the audioset fingerprint values derived from audio under different experimenatal conditions</p><p>Number of fields: 137</p><p>Number of data rows: 87329</p><p>Fields: </p><ul><li><b>id.no</b>: Sample ID (Field type: id)</li><li><b>file.size</b>: File size as a % of uncompressed (Field type: numeric)</li><li><b>frame.size</b>: Frame size (recording length) (Field type: ordered categorical)</li><li><b>compression</b>: Compression level (Mp3) (Field type: ordered categorical)</li><li><b>site</b>: Field Site Location (Field type: location)</li><li><b>req.freq</b>: Recording Frequency (Field type: numeric)</li><li><b>date</b>: Date (Field type: date)</li><li><b>time</b>: Time ID (including subsamples) (Field type: id)</li><li><b>max.freq</b>: Nyquist (Maximum) frequency (Field type: numeric)</li><li><b>feat1</b>: Feature 1 (Field type: numeric)</li><li><b>feat2</b>: Feature 2 (Field type: numeric)</li><li><b>feat3</b>: Feature 3 (Field type: numeric)</li><li><b>feat4</b>: Feature 4 (Field type: numeric)</li><li><b>feat5</b>: Feature 5 (Field type: numeric)</li><li><b>feat6</b>: Feature 6 (Field type: numeric)</li><li><b>feat7</b>: Feature 7 (Field type: numeric)</li><li><b>feat8</b>: Feature 8 (Field type: numeric)</li><li><b>feat9</b>: Feature 9 (Field type: numeric)</li><li><b>feat10</b>: Feature 10 (Field type: numeric)</li><li><b>feat11</b>: Feature 11 (Field type: numeric)</li><li><b>feat12</b>: Feature 12 (Field type: numeric)</li><li><b>feat13</b>: Feature 13 (Field type: numeric)</li><li><b>feat14</b>: Feature 14 (Field type: numeric)</li><li><b>feat15</b>: Feature 15 (Field type: numeric)</li><li><b>feat16</b>: Feature 16 (Field type: numeric)</li><li><b>feat17</b>: Feature 17 (Field type: numeric)</li><li><b>feat18</b>: Feature 18 (Field type: numeric)</li><li><b>feat19</b>: Feature 19 (Field type: numeric)</li><li><b>feat20</b>: Feature 20 (Field type: numeric)</li><li><b>feat21</b>: Feature 21 (Field type: numeric)</li><li><b>feat22</b>: Feature 22 (Field type: numeric)</li><li><b>feat23</b>: Feature 23 (Field type: numeric)</li><li><b>feat24</b>: Feature 24 (Field type: numeric)</li><li><b>feat25</b>: Feature 25 (Field type: numeric)</li><li><b>feat26</b>: Feature 26 (Field type: numeric)</li><li><b>feat27</b>: Feature 27 (Field type: numeric)</li><li><b>feat28</b>: Feature 28 (Field type: numeric)</li><li><b>feat29</b>: Feature 29 (Field type: numeric)</li><li><b>feat30</b>: Feature 30 (Field type: numeric)</li><li><b>feat31</b>: Feature 31 (Field type: numeric)</li><li><b>feat32</b>: Feature 32 (Field type: numeric)</li><li><b>feat33</b>: Feature 33 (Field type: numeric)</li><li><b>feat34</b>: Feature 34 (Field type: numeric)</li><li><b>feat35</b>: Feature 35 (Field type: numeric)</li><li><b>feat36</b>: Feature 36 (Field type: numeric)</li><li><b>feat37</b>: Feature 37 (Field type: numeric)</li><li><b>feat38</b>: Feature 38 (Field type: numeric)</li><li><b>feat39</b>: Feature 39 (Field type: numeric)</li><li><b>feat40</b>: Feature 40 (Field type: numeric)</li><li><b>feat41</b>: Feature 41 (Field type: numeric)</li><li><b>feat42</b>: Feature 42 (Field type: numeric)</li><li><b>feat43</b>: Feature 43 (Field type: numeric)</li><li><b>feat44</b>: Feature 44 (Field type: numeric)</li><li><b>feat45</b>: Feature 45 (Field type: numeric)</li><li><b>feat46</b>: Feature 46 (Field type: numeric)</li><li><b>feat47</b>: Feature 47 (Field type: numeric)</li><li><b>feat48</b>: Feature 48 (Field type: numeric)</li><li><b>feat49</b>: Feature 49 (Field type: numeric)</li><li><b>feat50</b>: Feature 50 (Field type: numeric)</li><li><b>feat51</b>: Feature 51 (Field type: numeric)</li><li><b>feat52</b>: Feature 52 (Field type: numeric)</li><li><b>feat53</b>: Feature 53 (Field type: numeric)</li><li><b>feat54</b>: Feature 54 (Field type: numeric)</li><li><b>feat55</b>: Feature 55 (Field type: numeric)</li><li><b>feat56</b>: Feature 56 (Field type: numeric)</li><li><b>feat57</b>: Feature 57 (Field type: numeric)</li><li><b>feat58</b>: Feature 58 (Field type: numeric)</li><li><b>feat59</b>: Feature 59 (Field type: numeric)</li><li><b>feat60</b>: Feature 60 (Field type: numeric)</li><li><b>feat61</b>: Feature 61 (Field type: numeric)</li><li><b>feat62</b>: Feature 62 (Field type: numeric)</li><li><b>feat63</b>: Feature 63 (Field type: numeric)</li><li><b>feat64</b>: Feature 64 (Field type: numeric)</li><li><b>feat65</b>: Feature 65 (Field type: numeric)</li><li><b>feat66</b>: Feature 66 (Field type: numeric)</li><li><b>feat67</b>: Feature 67 (Field type: numeric)</li><li><b>feat68</b>: Feature 68 (Field type: numeric)</li><li><b>feat69</b>: Feature 69 (Field type: numeric)</li><li><b>feat70</b>: Feature 70 (Field type: numeric)</li><li><b>feat71</b>: Feature 71 (Field type: numeric)</li><li><b>feat72</b>: Feature 72 (Field type: numeric)</li><li><b>feat73</b>: Feature 73 (Field type: numeric)</li><li><b>feat74</b>: Feature 74 (Field type: numeric)</li><li><b>feat75</b>: Feature 75 (Field type: numeric)</li><li><b>feat76</b>: Feature 76 (Field type: numeric)</li><li><b>feat77</b>: Feature 77 (Field type: numeric)</li><li><b>feat78</b>: Feature 78 (Field type: numeric)</li><li><b>feat79</b>: Feature 79 (Field type: numeric)</li><li><b>feat80</b>: Feature 80 (Field type: numeric)</li><li><b>feat81</b>: Feature 81 (Field type: numeric)</li><li><b>feat82</b>: Feature 82 (Field type: numeric)</li><li><b>feat83</b>: Feature 83 (Field type: numeric)</li><li><b>feat84</b>: Feature 84 (Field type: numeric)</li><li><b>feat85</b>: Feature 85 (Field type: numeric)</li><li><b>feat86</b>: Feature 86 (Field type: numeric)</li><li><b>feat87</b>: Feature 87 (Field type: numeric)</li><li><b>feat88</b>: Feature 88 (Field type: numeric)</li><li><b>feat89</b>: Feature 89 (Field type: numeric)</li><li><b>feat90</b>: Feature 90 (Field type: numeric)</li><li><b>feat91</b>: Feature 91 (Field type: numeric)</li><li><b>feat92</b>: Feature 92 (Field type: numeric)</li><li><b>feat93</b>: Feature 93 (Field type: numeric)</li><li><b>feat94</b>: Feature 94 (Field type: numeric)</li><li><b>feat95</b>: Feature 95 (Field type: numeric)</li><li><b>feat96</b>: Feature 96 (Field type: numeric)</li><li><b>feat97</b>: Feature 97 (Field type: numeric)</li><li><b>feat98</b>: Feature 98 (Field type: numeric)</li><li><b>feat99</b>: Feature 99 (Field type: numeric)</li><li><b>feat100</b>: Feature 100 (Field type: numeric)</li><li><b>feat101</b>: Feature 101 (Field type: numeric)</li><li><b>feat102</b>: Feature 102 (Field type: numeric)</li><li><b>feat103</b>: Feature 103 (Field type: numeric)</li><li><b>feat104</b>: Feature 104 (Field type: numeric)</li><li><b>feat105</b>: Feature 105 (Field type: numeric)</li><li><b>feat106</b>: Feature 106 (Field type: numeric)</li><li><b>feat107</b>: Feature 107 (Field type: numeric)</li><li><b>feat108</b>: Feature 108 (Field type: numeric)</li><li><b>feat109</b>: Feature 109 (Field type: numeric)</li><li><b>feat110</b>: Feature 110 (Field type: numeric)</li><li><b>feat111</b>: Feature 111 (Field type: numeric)</li><li><b>feat112</b>: Feature 112 (Field type: numeric)</li><li><b>feat113</b>: Feature 113 (Field type: numeric)</li><li><b>feat114</b>: Feature 114 (Field type: numeric)</li><li><b>feat115</b>: Feature 115 (Field type: numeric)</li><li><b>feat116</b>: Feature 116 (Field type: numeric)</li><li><b>feat117</b>: Feature 117 (Field type: numeric)</li><li><b>feat118</b>: Feature 118 (Field type: numeric)</li><li><b>feat119</b>: Feature 119 (Field type: numeric)</li><li><b>feat120</b>: Feature 120 (Field type: numeric)</li><li><b>feat121</b>: Feature 121 (Field type: numeric)</li><li><b>feat122</b>: Feature 122 (Field type: numeric)</li><li><b>feat123</b>: Feature 123 (Field type: numeric)</li><li><b>feat124</b>: Feature 124 (Field type: numeric)</li><li><b>feat125</b>: Feature 125 (Field type: numeric)</li><li><b>feat126</b>: Feature 126 (Field type: numeric)</li><li><b>feat127</b>: Feature 127 (Field type: numeric)</li><li><b>feat128</b>: Feature 128 (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2019-02-26 to 2019-06-02</p><p><b>Latitudinal extent: </b>4.6644 to 4.7027</p><p><b>Longitudinal extent: </b>117.5351 to 117.5914</p>
Figure 21 in Environmental control versus phylogenic fingerprint in ontogeny: The example of the development of the stalk in the genus Guillecrinus (stalked crinoids, Echinodermata)
Figure 21. Comparison of the filtration potential of the crown in several extant stalked crinoids.
DataSet Literature review on cyberlaw and fingerprints
<p>Merupakan sebuah keamanan cyberlaw dan fingerprint</p>
A reductionist paradigm for high-throughput behavioural fingerprinting in Drosophila melanogaster - DATASET 2 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 2022 </p> <p>See http://lab.gilest.ro/coccinella for more information</p> <p>This is archive 2 of 2</p>
Sr-Nd isotopic fingerprints of Red River sediments and its implication for provenance discrimination in the South China Sea
<p>Supplementary Data of the Manuscript titled 'Revisit Sr-Nd isotopic fingerprints of Red River sediments and its implication for provenance discrimination in the South China Sea'</p> <p>Table A1. elemental compositions of Red River estuarine sediment samples</p> <p>Table A2. Literature data on sediment Sr-Nd isotopic ratios, Rb/Sr and K<sub>2</sub>O/(Na<sub>2</sub>O+CaO) in the Red River catchment</p> <p>Table A3. Compiled data on Sr-Nd isotopic compositions of rocks in the Thao River, Song Da and Song Lo basin</p> <p>Table A4. Compiled data of sediment Sr-Nd isotopic composition in the Pearl River, Red River and Mekong River</p>
Influence of sedimentary environment evolution on fingerprint characteristics of methane isotopes: A case study from Hangzhou Bay
<p><span>To better understand the depositional constraints on the fingerprint characteristics of methane isotopes, we present a set of carbon/hydrogen isotopic data for CH<sub>4</sub>, CO<sub>2</sub>, pore water, carbonates, and total organic carbon (TOC) along a 70-m sedimentary core from Hangzhou Bay, China. The sedimentary facies (Units I, II, and III from upper to bottom) suggested depositional environments of the present estuary, shallow marine, and floodplain-estuary. The values of δD<sub>CH4</sub> displayed similar trends with those of δD<sub>H2O</sub> and Cl<sup>-</sup> concentrations along the depth profiles. The values of δ<sup>13</sup>C<sub>CH4</sub> generally synchronously changed with those of δ<sup>13</sup>C<sub>CO2</sub>. The variation trends of δ<sup>13</sup>C<sub>CH4</sub> and δ<sup>13</sup>C<sub>CO2</sub> were the same with δ<sup>13</sup>C<sub>carbonate</sub> from 10 m to 70 m depth but decoupled above 10 m. Calculations suggested that about 86% of methane was produced through the CO<sub>2</sub> reduction pathway. In this pathway, the hydrogen in CH<sub>4</sub> is from ambient water, while the carbon is from dissolved inorganic carbon. In our study, the low δD<sub>CH4</sub> below 44.5 m corresponded to low δD<sub>H2O</sub> and low salinity during the cold and low-sea-level period. The values of δ<sup>13</sup>C<sub>CH4</sub> in Units II and III were correlated with the δ<sup>13</sup>C<sub>carbonates</sub>, which is related to the sedimentary processes. But decoupling of low values of δ<sup>13</sup>C<sub>CH4</sub> and δ<sup>13</sup>C<sub>CO2</sub> from δ<sup>13</sup>C<sub>carbonates</sub> in Unit I may be related to preferential microbial consumption of labile compounds with light carbon isotopic compositions, such as lipids. In short, the variations of the stable carbon and hydrogen isotopic compositions of CH<sub>4</sub> were largely related to sedimentary processes.</span></p>
Passive Operating System Fingerprinting Revisited - Network Flows Dataset
<p>For the evaluation of OS fingerprinting methods, we need a dataset with the following requirements:</p> <ul> <li>First, the dataset needs to be big enough to capture the variability of the data. In this case, we need many connections from different operating systems.</li> <li>Second, the dataset needs to be annotated, which means that the corresponding operating system needs to be known for each network connection captured in the dataset. Therefore, we cannot just capture any network traffic for our dataset; we need to be able to determine the OS reliably.</li> </ul> <p>To overcome these issues, we have decided to create the dataset from the traffic of several web servers at our university. This allows us to address the first issue by collecting traces from thousands of devices ranging from user computers and mobile phones to web crawlers and other servers. The ground truth values are obtained from the HTTP User-Agent, which resolves the second of the presented issues. Even though most traffic is encrypted, the User-Agent can be recovered from the web server logs that record every connection’s details. By correlating the IP address and timestamp of each log record to the captured traffic, we can add the ground truth to the dataset.</p> <p>For this dataset, we have selected a cluster of five web servers that host 475 unique university domains for public websites. The monitoring point recording the traffic was placed at the backbone network connecting the university to the Internet.</p> <p>The dataset used in this paper was collected from approximately 8 hours of university web traffic throughout a single workday. The logs were collected from Microsoft IIS web servers and converted from W3C extended logging format to JSON. The logs are referred to as <em>web logs </em>and are used to annotate the records generated from packet capture obtained by using a network probe tapped into the link to the Internet.</p> <p>The entire dataset creation process consists of seven steps:</p> <ol> <li>The packet capture was processed by the Flowmon flow exporter (<a href="https://www.flowmon.com">https://www.flowmon.com</a>) to obtain primary flow data containing information from TLS and HTTP protocols.</li> <li>Additional statistical features were extracted using GoFlows flow exporter (<a href="https://github.com/CN-TU/go-flows">https://github.com/CN-TU/go-flows</a>).</li> <li>The primary flows were filtered to remove incomplete records and network scans.</li> <li>The flows from both exporters were merged together into records containing fields from both sources.</li> <li><em>Web logs </em>were filtered to cover the same time frame as the flow records.</li> <li><em>Web logs</em> were paired with the flow records based on shared properties (IP address, port, time).</li> <li>The last step was to convert the User-Agent values into the operating system using a Python version of the open-source tool <em>ua-parser</em> (<a href="https://github.com/ua-parser/uap-python">https://github.com/ua-parser/uap-python</a>). We replaced the unstructured User-Agent string in the records with the resulting OS.</li> </ol> <p>The collected and enriched flows contain 111 data fields that can be used as features for OS fingerprinting or any other data analyses. The fields grouped by their area are listed below:</p> <ul> <li>basic flow properties - flow_ID;start;end;L3 PROTO;L4 PROTO;BYTES A;PACKETS A;SRC IP;DST IP;TCP flags A;SRC port;DST port;packetTotalCountforward;packetTotalCountbackward;flowDirection;flowEndReason;</li> <li>IP parameters - IP ToS;maximumTTLforward;maximumTTLbackward;IPv4DontFragmentforward;IPv4DontFragmentbackward;</li> <li>TCP parameters - TCP SYN Size;TCP Win Size;TCP SYN TTL;tcpTimestampFirstPacketbackward;tcpOptionWindowScaleforward;tcpOptionWindowScalebackward;tcpOptionSelectiveAckPermittedforward;tcpOptionSelectiveAckPermittedbackward;tcpOptionMaximumSegmentSizeforward;tcpOptionMaximumSegmentSizebackward;tcpOptionNoOperationforward;tcpOptionNoOperationbackward;synAckFlag;tcpTimestampFirstPacketforward;</li> <li>HTTP - HTTP Request Host;URL;</li> <li>User-agent - UA OS family;UA OS major;UA OS minor;UA OS patch;UA OS patch minor;</li> <li>TLS - TLS_CONTENT_TYPE;TLS_HANDSHAKE_TYPE;TLS_SETUP_TIME;TLS_SERVER_VERSION;TLS_SERVER_RANDOM;TLS_SERVER_SESSION_ID;TLS_CIPHER_SUITE;TLS_ALPN;TLS_SNI;TLS_SNI_LENGTH;TLS_CLIENT_VERSION;TLS_CIPHER_SUITES;TLS_CLIENT_RANDOM;TLS_CLIENT_SESSION_ID;TLS_EXTENSION_TYPES;TLS_EXTENSION_LENGTHS;TLS_ELLIPTIC_CURVES;TLS_EC_POINT_FORMATS;TLS_CLIENT_KEY_LENGTH;TLS_ISSUER_CN;TLS_SUBJECT_CN;TLS_SUBJECT_ON;TLS_VALIDITY_NOT_BEFORE;TLS_VALIDITY_NOT_AFTER;TLS_SIGNATURE_ALG;TLS_PUBLIC_KEY_ALG;TLS_PUBLIC_KEY_LENGTH;TLS_JA3_FINGERPRINT;</li> <li>Packet timings - NPM_CLIENT_NETWORK_TIME;NPM_SERVER_NETWORK_TIME;NPM_SERVER_RESPONSE_TIME;NPM_ROUND_TRIP_TIME;NPM_RESPONSE_TIMEOUTS_A;NPM_RESPONSE_TIMEOUTS_B;NPM_TCP_RETRANSMISSION_A;NPM_TCP_RETRANSMISSION_B;NPM_TCP_OUT_OF_ORDER_A;NPM_TCP_OUT_OF_ORDER_B;NPM_JITTER_DEV_A;NPM_JITTER_AVG_A;NPM_JITTER_MIN_A;NPM_JITTER_MAX_A;NPM_DELAY_DEV_A;NPM_DELAY_AVG_A;NPM_DELAY_MIN_A;NPM_DELAY_MAX_A;NPM_DELAY_HISTOGRAM_1_A;NPM_DELAY_HISTOGRAM_2_A;NPM_DELAY_HISTOGRAM_3_A;NPM_DELAY_HISTOGRAM_4_A;NPM_DELAY_HISTOGRAM_5_A;NPM_DELAY_HISTOGRAM_6_A;NPM_DELAY_HISTOGRAM_7_A;NPM_JITTER_DEV_B;NPM_JITTER_AVG_B;NPM_JITTER_MIN_B;NPM_JITTER_MAX_B;NPM_DELAY_DEV_B;NPM_DELAY_AVG_B;NPM_DELAY_MIN_B;NPM_DELAY_MAX_B;NPM_DELAY_HISTOGRAM_1_B;NPM_DELAY_HISTOGRAM_2_B;NPM_DELAY_HISTOGRAM_3_B;NPM_DELAY_HISTOGRAM_4_B;NPM_DELAY_HISTOGRAM_5_B;NPM_DELAY_HISTOGRAM_6_B;NPM_DELAY_HISTOGRAM_7_B;</li> <li>ICMP - ICMP TYPE;</li> </ul> <p>The details of OS distribution grouped by the OS family are summarized in the table below. The <em>Other </em>OS family contains records generated by web crawling bots that do not include OS information in the User-Agent.</p> <table> <thead> <tr> <th scope="col">OS Family</th> <th scope="col">Number of flows</th> </tr> </thead> <tbody> <tr> <td>Other</td> <td>42474</td> </tr> <tr> <td>Windows</td> <td>40349</td> </tr> <tr> <td>Android</td> <td>10290</td> </tr> <tr> <td>iOS</td> <td>8840</td> </tr> <tr> <td>Mac OS X</td> <td>5324</td> </tr> <tr> <td>Linux</td> <td>1589</td> </tr> <tr> <td>Ubuntu</td> <td>653</td> </tr> <tr> <td>Fedora</td> <td>88</td> </tr> <tr> <td>Chrome OS</td> <td>53</td> </tr> <tr> <td>Symbian OS</td> <td>1</td> </tr> <tr> <td>Slackware</td> <td>1</td> </tr> <tr> <td>Linux Mint</td> <td>1</td> </tr> </tbody> </table> <p> </p>
Calis-p 2.1 a software for protein-based stable isotope fingerprinting (Protein-SIF) and probing (Protein-SIP)
<p>Calis-p (The CALgary approach to ISotopes in proteomics) is a java application to estimate isotopic composition (e.g. delta13C or delta15N) of individual species in a microbial community from a proteomic dataset. Calis-p 2.1 handles both natural isotope abundances and data from labelling experiments such as stable isotope probing (SIP). It requires a mzIdent (or target spectrum match) and mzML files as the input and requires about 1 min per mzML file with 10 threads and needs <10 Gb of RAM. It has been tested with data from various nano liquid chromatography/Orbitrap platforms. For successful SIP, it is extremely sensitive, but requires the 13C fraction to remain below 10%.</p> <p>Detailed documentation can be found in the Wiki on https://sourceforge.net/p/calis-p/wiki/Home/ and the research article.</p>
Chemical fingerprints indicate group membership in a highly social communal breeding bird
<p>Gregarious species must distinguish group members from non-group members. Olfaction is important for group recognition in mammals but rarely studied in birds, despite birds using olfaction in social contexts from species discrimination to kin recognition. Olfactory-based recognition requires that groups have a signature odour, so we tested for preen oil and feather chemical similarity among group-living smooth-billed anis (<em>Crotophaga</em> <em>ani</em>). Physiology affects body chemistry, so we also tested for an effect of egg-laying competition, as a proxy for reproductive status, on female chemical similarity. Finally, the fermentation hypothesis for chemical recognition posits that host-associated microbes drive host odour, so we tested for covariation between ani chemicals and microbiota. Group members were more chemically similar than non-group members, regardless of body region, demonstrating that gregarious bird species can have group chemical signatures. Females in groups with less egg-laying competition had more similar preen oil, but not feather, chemicals, suggesting preen oil conveys information about reproductive status. There was no overall covariation between chemicals and microbes; instead, subsets of microbes could mediate olfactory cues in birds. Preen oil and feather chemicals showed little overlap, suggesting they convey different information. These findings will guide experimental work on olfaction in gregarious birds.</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.