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Dataset results
400 results for “fingerprints”
Mass Spectral Fingerprinting in Obstructive Sleep Apnoea
ClinicalTrials.gov study NCT02810158. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Salivary Raman COVID-19 Fingerprint
ClinicalTrials.gov study NCT04583306. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Mass Spectral Fingerprinting in Lung Cancer
ClinicalTrials.gov study NCT02781857. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Novel, continuous monitoring of fine-scale movement using fixed-position radiotelemetry arrays and random forest location fingerprinting
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Data from: Egg morphology fails to identify nests parasitized by conspecifics in common pochard: a test based on protein fingerprinting and including female relatedness
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MSAP and AFLP fingerprints
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Supplemental Tables for Heal et al: Marine community metabolomes carry fingerprints of phytoplankton community composition
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Mathematical chromatography deciphers the molecular fingerprints of dissolved organic matter
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A fingerprint of climate change across pine forests of Sweden
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Data from: Rapid species level identification of fish eggs by proteome fingerprinting using MALDI-TOF MS
<p>Quantifying spawning biomass of commercially relevant fish species is important to generate fishing quotas. This will mostly rely on the annual or daily production of fish eggs. However, these have to be identified precisely to species level to obtain a reliable estimate of offspring production of the different species. Because morphological identification can be very difficult, recent developments are heading towards application of molecular tools. Methods such as COI barcoding have long handling times and cause high costs for single specimen identifications. In order to test MALDI-TOF MS, a rapid and cost-effective alternative for species identification, we identified fish eggs using COI barcoding and used the same specimens to set up a MALDI-TOF MS reference library. This library, constructed from two different MALDI-TOF MS instruments, was then used to identify unknown eggs from a different sampling occasion. By using a line of evidence from hierarchical clustering and different supervised identification approaches we obtained concordant species identifications for 97.5% of the unknown fish eggs, proving MALDI-TOF MS a good tool for rapid species level identification of fish eggs. At the same time we point out the necessity of adjusting identification scores of supervised methods for identification to optimize identification success.</p>
Data from: Phylogenetic and comparative genomics of the family Leptotrichiaceae and introduction of a novel fingerprinting MLVA for Streptobacillus moniliformis
Background: The Leptotrichiaceae are a family of fairly unnoticed bacteria containing both microbiota on mucous membranes as well as significant pathogens such as Streptobacillus moniliformis, the causative organism of streptobacillary rat bite fever. Comprehensive genomic studies in members of this family have so far not been carried out. We aimed to analyze 47 genomes from 20 different member species to illuminate phylogenetic aspects, as well as genomic and discriminatory properties. Results: Our data provide a novel and reliable basis of support for previously established phylogeny from this group and give a deeper insight into characteristics of genome structure and gene functions. Full genome analyses revealed that most S. moniliformis strains under study form a heterogeneous population without any significant clustering. Analysis of infra-species variability for this highly pathogenic rat bite fever organism led to the detection of three specific variable number tandem analysis loci with high discriminatory power. Conclusions: This highly useful and economical tool can be directly employed in clinical samples without laborious prior cultivation. Our and prospective case-specific data can now easily be compared by using a newly established MLVA database in order to gain a better insight into the epidemiology of this presumably under-reported zoonosis.
Dataset for Personalized microbial fingerprint associated with differential glycemic effects of a whole grain rye intervention on Chinese adults
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Fig. 76. Normal Q-Q in Indoor Radio Map localization WiFi fingerprint datasets
Fig. 76. Normal Q-Q plot of standardized body length (SBL) for selected Texas Selenophorus species.
2D and 3D biometrics fingerprint dataset in contacless and labeled conditions
<p>2D and 3D Fingerrpint dataset. It was created by former Ph.D. stduent Ms. Wei Zhou under the supervision of Prof. J. Hu who acts as the communication contact.</p>
Hydrogen Isotope fingerprinting of lipid biomarkers in the Chinese Marginal Seas
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Limitless per Customer: Understanding and Breaking User & Device Fingerprinting in Android
<p>Dataset and artifacts for the research work "Limitless per Customer: Understanding and Breaking User & Device Fingerprinting in Android"</p>
A deep dive into fat: Investigating blubber lipidomics fingerprint of killer whales and humpback whales in northern Norway
<p class="xl24">In cetaceans, blubber is the primary and largest lipid body reservoir. Our current understanding about lipid stores and uses in cetaceans is still limited and most studies only focused on a single narrow snapshot of the lipidome. We documented an extended lipidomics fingerprint in two cetacean species present in northern Norway during wintertime. We were able to detect 817 molecular lipid species in blubber of killer whales (<i>Orcinus orca</i>) and humpback whales (<i>Megaptera novaeangliae</i>). The profiles were largely dominated by triradylglycerols in both species and to a lesser extent, by other constituents including glycerophosphocholines, phosphosphingolipids, glycerophosphoethanolamines and diradylglycerols. Through a unique combination of traditional statistical approaches, together with a novel bioinformatics tool (LION/web), we showed contrasting fingerprints composition between species. The higher content of triradylglycerols in humpback whales is necessary to fuel their upcoming half a year fasting and energy-demanding migration between feeding and breeding grounds. In adipocytes, we assume that the intense feeding rate of humpback whales prior to migration translates into an important accumulation of triacylglycerols content in lipid droplets. Upstream, the endoplasmic reticulum is operating at full capacity to supply acute lipid storage, consistent with the reported enrichment of glycerophosphocholines in humpback whales, major components of the endoplasmic reticulum. There was also an enrichment of membrane components which translates into higher sphingolipids content in the lipidome of killer whales, potentially as a structural adaptation for their higher hydrodynamic performance. Finally, the presence of both lipid-enriched and lipid-depleted individuals within the killer whale population in Norway suggests dietary-specialization, consistent with significant differences of δ<sup>15</sup>N and δ<sup>13</sup>C isotopic ratios in skin between the two groups, with higher values and a wider niche for the lipid-enriched individuals. Results suggest the lipid-depleted killer whales were herring-specialists, while the lipid-enriched individuals might feed on both herrings and seals.</p>
QCSD: QUIC Client-Side Website-Fingerprinting Defence Dataset
<p>Contains live-defended, defended under simulation, and undefended website-fingerprinting traces associated with the paper "QCSD: QUIC Client-Side Website-Fingerprinting Defence Framework" (USENIX Security '22).</p>
Source and result datasets for ""Oh SSH-it, what's my fingerprint? A Large-Scale Analysis of SSH Host Key Fingerprint Verification Records in the DNS"
<p>These files are the LRZip [0] compressed datasets used in the research paper 'Oh SSH-it, what's my fingerprint? A Large-Scale Analysis of SSH Host Key Fingerprint Verification Records in the DNS' [1]. The code can be found on Github [2].</p> <p>ls 2021-12-22-10\:49\:40<br> total 21M<br> drwxr-xr-x 2 sneef sneef 4.0K Aug 15 15:38 .<br> drwxr-xr-x 62 sneef sneef 4.0K Aug 15 15:51 ..<br> -rw-r--r-- 1 sneef sneef 7.7M May 25 12:10 domainfile.log.new.gz<br> -rw-r--r-- 1 sneef sneef 17K May 24 20:53 parser.log.new.gz<br> -rw-r--r-- 1 sneef sneef 13M May 24 20:52 query.log.new.gz<br> -rw-r--r-- 1 sneef sneef 125 Dec 22 2021 README<br> -rw-r--r-- 1 sneef sneef 27K May 24 20:52 server.log.new.gz</p> <p>ls 2021-12-22-15\:04\:21<br> total 11G<br> drwxr-xr-x 2 sneef sneef 4.0K Aug 15 15:43 .<br> drwxr-xr-x 62 sneef sneef 4.0K Aug 15 15:51 ..<br> -rw-r--r-- 1 sneef sneef 4.2G May 23 12:43 certstream.log.new.gz<br> -rw-r--r-- 1 sneef sneef 3.3M May 23 12:37 parser.log.new.gz<br> -rw-r--r-- 1 sneef sneef 6.0G May 23 12:52 query.log.new.gz<br> -rw-r--r-- 1 sneef sneef 104 Dec 22 2021 README<br> -rw-r--r-- 1 sneef sneef 6.3M May 23 12:37 server.log.new.gz</p> <p><br> ls results_certstream<br> total 17G<br> drwxr-xr-x 2 sneef sneef 4.0K Jun 8 19:26 .<br> drwxr-xr-x 11 sneef sneef 4.0K Aug 15 13:24 ..<br> -rw-r--r-- 1 sneef sneef 1.3K Jun 8 22:54 certstream_analysis_scanned_domains.txt<br> -rw-r--r-- 1 sneef sneef 1.2K Jun 8 22:39 certstream_analysis_skipped_domains.txt<br> -rw-r--r-- 1 sneef sneef 4.5G May 23 13:21 certstream_counted_unique_domains.json<br> -rw-r--r-- 1 sneef sneef 450 May 23 13:22 certstream_counted_unique_domains_otherlines.csv<br> -rw-r--r-- 1 sneef sneef 602M May 23 13:22 certstream_counted_unique_domains_skipped_domains.csv<br> -rw-r--r-- 1 sneef sneef 3.6G May 23 13:22 certstream_counted_unique_domains_unique_domains.csv<br> -rw-r--r-- 1 sneef sneef 6.1K Jun 3 16:48 parserlog_analysis_errors_and_sshfps.txt<br> -rw-r--r-- 1 sneef sneef 39K Jun 3 16:48 parserlog_analysis_errors_and_sshfps.txt_errors.txt<br> -rw-r--r-- 1 sneef sneef 3.1M Jun 8 19:20 parserlog_analysis_v6.json<br> -rw-r--r-- 1 sneef sneef 94 Jun 8 19:26 parserlog_analysis_v6_out.txt<br> -rw-r--r-- 1 sneef sneef 37K May 23 13:41 parserlog_structured_data_errors.csv<br> -rw-r--r-- 1 sneef sneef 45M May 23 13:41 parserlog_structured_data_structued_data.json<br> -rw-r--r-- 1 sneef sneef 22M May 23 13:41 parserlog_structured_data_timesorted_sshpfs.csv<br> -rw-r--r-- 1 sneef sneef 2.1K Jun 8 23:09 querylog_analysis_err_label.txt<br> -rw-r--r-- 1 sneef sneef 1.2K Jun 8 23:09 querylog_analysis_err_no_answer.txt<br> -rw-r--r-- 1 sneef sneef 1.4K Jun 8 23:09 querylog_analysis_err_no_queryname.txt<br> -rw-r--r-- 1 sneef sneef 1.3K Jun 8 23:08 querylog_analysis_err_no_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 1.1K Jun 8 23:09 querylog_analysis_err_timeout.txt<br> -rw-r--r-- 1 sneef sneef 1.1K Jun 8 23:09 querylog_analysis_found_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 2.6K May 23 13:41 querylog_counted_messages_err_label.csv<br> -rw-r--r-- 1 sneef sneef 76M May 23 13:41 querylog_counted_messages_err_no_answer.csv<br> -rw-r--r-- 1 sneef sneef 206M May 23 13:41 querylog_counted_messages_err_no_queryname.csv<br> -rw-r--r-- 1 sneef sneef 3.4G May 23 13:41 querylog_counted_messages_err_no_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 7.0M May 23 13:41 querylog_counted_messages_err_timeout.csv<br> -rw-r--r-- 1 sneef sneef 423K May 23 13:41 querylog_counted_messages_found_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 3.9G May 23 13:40 querylog_counted_messages.json<br> -rw-r--r-- 1 sneef sneef 6.0K Jun 9 15:57 serverlog_analysis_all.txt<br> -rw-r--r-- 1 sneef sneef 259 Jun 8 00:51 serverlog_analysis_ptr.txt<br> -rw-r--r-- 1 sneef sneef 305K Jun 8 00:24 serverlog_ptr_mapping.json<br> -rw-r--r-- 1 sneef sneef 1.4M May 23 22:41 serverlog_structured_data_dnssec.csv<br> -rw-r--r-- 1 sneef sneef 63K May 23 22:41 serverlog_structured_data_error_dns_no_a_record.csv<br> -rw-r--r-- 1 sneef sneef 74 May 23 22:41 serverlog_structured_data_error_dns_not_exist.csv<br> -rw-r--r-- 1 sneef sneef 9.1K May 23 22:41 serverlog_structured_data_error_dns_servfail.csv<br> -rw-r--r-- 1 sneef sneef 239 May 23 22:41 serverlog_structured_data_error_dns_timeout.csv<br> -rw-r--r-- 1 sneef sneef 1.7M May 23 22:41 serverlog_structured_data_error_server_no_fp.csv<br> -rw-r--r-- 1 sneef sneef 1.7K May 23 22:41 serverlog_structured_data_error_server_nxdomain.csv<br> -rw-r--r-- 1 sneef sneef 17K May 23 22:41 serverlog_structured_data_error_server_servfail.csv<br> -rw-r--r-- 1 sneef sneef 79 May 23 22:41 serverlog_structured_data_error_server_wrongresponse.csv<br> -rw-r--r-- 1 sneef sneef 70M May 23 22:41 serverlog_structured_data_structued_data.json</p> <p> </p> <p>ls results_tranco1m<br> total 79M<br> drwxr-xr-x 2 sneef sneef 4.0K Jun 8 17:03 .<br> drwxr-xr-x 11 sneef sneef 4.0K Aug 15 13:24 ..<br> -rw-r--r-- 1 sneef sneef 850 Jun 8 22:38 domainfile_analysis_scanned_domains.txt<br> -rw-r--r-- 1 sneef sneef 21M May 25 12:28 domainfile_counted_unique_domains.json<br> -rw-r--r-- 1 sneef sneef 19M May 25 12:28 domainfile_counted_unique_domains_unique_domains.csv<br> -rw-r--r-- 1 sneef sneef 4.0K May 27 03:03 parserlog_analysis_errors_and_sshfps.txt<br> -rw-r--r-- 1 sneef sneef 16K Jun 8 16:32 parserlog_analysis_v6.json<br> -rw-r--r-- 1 sneef sneef 83 Jun 8 17:04 parserlog_analysis_v6_out.txt<br> -rw-r--r-- 1 sneef sneef 226 May 25 12:28 parserlog_structured_data_errors.csv<br> -rw-r--r-- 1 sneef sneef 66K May 25 12:28 parserlog_structured_data_structued_data.json<br> -rw-r--r-- 1 sneef sneef 31K May 25 12:28 parserlog_structured_data_timesorted_sshpfs.csv<br> -rw-r--r-- 1 sneef sneef 0 Jun 8 22:38 querylog_analysis_err_label.txt<br> -rw-r--r-- 1 sneef sneef 900 Jun 8 22:38 querylog_analysis_err_no_answer.txt<br> -rw-r--r-- 1 sneef sneef 915 Jun 8 22:38 querylog_analysis_err_no_queryname.txt<br> -rw-r--r-- 1 sneef sneef 909 Jun 8 22:38 querylog_analysis_err_no_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 844 Jun 8 22:38 querylog_analysis_err_timeout.txt<br> -rw-r--r-- 1 sneef sneef 829 Jun 8 22:38 querylog_analysis_found_sshfp.txt<br> -rw-r--r-- 1 sneef sneef 27 May 25 12:28 querylog_counted_messages_err_label.csv<br> -rw-r--r-- 1 sneef sneef 589K May 25 12:28 querylog_counted_messages_err_no_answer.csv<br> -rw-r--r-- 1 sneef sneef 269K May 25 12:28 querylog_counted_messages_err_no_queryname.csv<br> -rw-r--r-- 1 sneef sneef 18M May 25 12:28 querylog_counted_messages_err_no_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 58K May 25 12:28 querylog_counted_messages_err_timeout.csv<br> -rw-r--r-- 1 sneef sneef 1.8K May 25 12:28 querylog_counted_messages_found_sshfp.csv<br> -rw-r--r-- 1 sneef sneef 21M May 25 12:28 querylog_counted_messages.json<br> -rw-r--r-- 1 sneef sneef 4.4K Jun 9 15:57 serverlog_analysis_all.txt<br> -rw-r--r-- 1 sneef sneef 241 Jun 8 11:45 serverlog_analysis_ptr.txt<br> -rw-r--r-- 1 sneef sneef 5.4K Jun 7 23:58 serverlog_ptr_mapping.json<br> -rw-r--r-- 1 sneef sneef 2.0K Jun 7 15:57 serverlog_structured_data_dnssec.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_dns_no_a_record.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_dns_not_exist.csv<br> -rw-r--r-- 1 sneef sneef 69 Jun 7 15:57 serverlog_structured_data_error_dns_servfail.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_dns_timeout.csv<br> -rw-r--r-- 1 sneef sneef 1.5K Jun 7 15:57 serverlog_structured_data_error_server_no_fp.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_server_nxdomain.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_server_servfail.csv<br> -rw-r--r-- 1 sneef sneef 22 Jun 7 15:57 serverlog_structured_data_error_server_wrongresponse.csv<br> -rw-r--r-- 1 sneef sneef 129K Jun 7 15:57 serverlog_structured_data_structued_data.json<code> </code></p> <p><br> <br> [0] https://github.com/ckolivas/lrzip<br> [1] TBD<br> [2] https://github.com/gehaxelt/sshfp-dns-measurement</p>
Magnetic Resonance Fingerprinting DICOM Validation Datasets for Real-Time Automated Quality Control for Quantitative MRI
<p>12 DICOM validation datasets (in DICOMDIR .zip format) from two Magnetic Resonance Fingerprinting sequences at two slice thickness across three test-retest sets per configuration. Also included are the resulting vial extraction reports and data for each dataset. </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.