Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
400
datasets available to search
ShareScore release 0.7.1
Dataset results
400 results for “fingerprints”
A Transcriptome Fingerprinting Assay for Clinical Immune Monitoring (MS Datasets)
GEO Series GSE100162. Homo sapiens. 56 samples. Type: Expression profiling by array.
Slice-selective extended phase graphs in gradient-crushed, transient-state free precession sequences: an application to magnetic resonance fingerprinting
<p>Input data for github repo jostenson/mrm_ssepg associated with "Slice-selective extended phase graphs in gradient-crushed, transient-state free precession sequences: an application to magnetic resonance fingerprinting," as published in Magnetic Resonance in Medicine.</p>
PSD-based fingerprinting - dataset
<p>Signals and algorithm results for the article "A PSD-based fingerprint approach for IoT devices" submitted for PRDC2020. All the data inside this dataset is anonymised.</p>
Virus Gene Ontology Fingerprint Data
<p>Gene Ongtology Fingerprint (GOF) is a intellect knowledge-empowered gene and ontology network based on integrating valuable, publicly available literature. It consists of a set of genes and Gene Ontology terms automatically extracted from the literature by the natural language processing (NLP) technology.</p> <p>In the GOF, a smaller adjusted p-value stands for higher enrichment significance between a gene and a GO term. The gene-gene similarity score (GGSS) reflects the functional similarity between two genes, indicating their similar ontology fingerprints in a certain research topic.</p>
Data from: Mapping polyclonal HIV-1 antibody responses via next-generation neutralization fingerprinting
Computational neutralization fingerprinting, NFP, is an efficient and accurate method for predicting the epitope specificities of polyclonal antibody responses to HIV-1 infection. Here, we present next-generation NFP algorithms that substantially improve prediction accuracy for individual donors and enable serologic analysis for entire cohorts. Specifically, we developed algorithms for: (a) selection of optimized virus neutralization panels for NFP analysis, (b) estimation of NFP prediction confidence for each serum sample, and (c) identification of sera with potentially novel epitope specificities. At the individual donor level, the next-generation NFP algorithms particularly improved the ability to detect multiple epitope specificities in a sample, as confirmed both for computationally simulated polyclonal sera and for samples from HIV-infected donors. Specifically, the next-generation NFP algorithms detected multiple specificities in twice as many samples of simulated sera. Further, unlike the first-generation NFP, the new algorithms were able to detect both of the previously confirmed antibody specificities, VRC01-like and PG9-like, in donor CHAVI 0219. At the cohort level, analysis of ~150 broadly neutralizing HIV-infected donor samples suggested a potential connection between clade of infection and types of elicited epitope specificities. Most notably, while 10E8-like antibodies were observed in infections from different clades, an enrichment of such antibodies was predicted for clade B samples. Ultimately, such large-scale analyses of antibody responses to HIV-1 infection can help guide the design of epitope-specific vaccines that are tailored to take into account the prevalence of infecting clades within a specific geographic region. Overall, the next-generation NFP technology will be an important tool for the analysis of broadly neutralizing polyclonal antibody responses against HIV-1.
Decoded fingerprints of hyperresponsive, expanding product space from polyether cascade cyclizations as tools to elucidate supramolecular catalysis
<p>Original data</p>
Mistletoe chronobiology: 27 years of daily metabolomic fingerprinting (dataset 1)
<p>Chronobiological aspects of mistletoe have been studied for decades <span>(Fyfe, 1969, Scheer et al., 1992, Flückiger and Baumgartner, 2002, Dorka et al., 2007, Urech et al., 2009, Derbidge et al., 2013, Derbidge et al., 2016)</span>.<span> One of the pioneers of such studies was Agnes Fyfe, a researcher at Hiscia Research Institute (Society for Cancer Research, Arlesheim, Switzerland). Already in the 1950s, with the aim of identifying optimal harvesting times, she started to build up a dataset of chromatograms using a form of metabolomic fingerprinting based on pattern formation peculiar in terms of ease, rapidity and affordability. She conducted daily chromatograms of <em><span>Viscum album </span></em>ssp. <em><span>album</span></em> L. for nearly 30 years (from November 1958 to October 1985) creating a unique heritage.</span></p> <p><span>In 2020, half of it, namely the “Gold Fyfe dataset”, was digitized. A total of 27 979 chromatograms showing recurring variations of different patterns compose the Gold Fyfe dataset. </span><span>Part of the chromatograms were made with 50% mistletoe extract concentration (n= 19 109; here referred to as "dataset 1"). </span></p> <p><span>The following information was always recorded on each chromatography paper: sample name and concentration, harvest date and a numerical identification code. Additional information can be reported concerning the sample, as extract rising time, humidity and temperature. But also reagent information such as name and concentration, reagent rising time, humidity and temperature. Moreover, additional handwritten notes can appear on the right upper corner of the chromatographic paper.</span></p> <p><span>Chromatograms were scanned in reflective mode using an Epson Perfection V600 scanner (Epson, Kloten, Switzerland). Since the chromatograms are colour pictures,<span> </span>colours were calibrated using: IT 8.7/2 RF target (Wolf Faust, Frankfurt, Germany) and SilverFast Ai Studio 8.8 (LaserSoft Imaging AG, Kiel, Germany) as calibration program. Images were saved in TIFF format, 300 DPI, RGB, 16 cm x 17 cm.</span></p> <p>Each TIFF file was named in the following way YYYYMMDD_50_dx. In particular:</p> <ul> <li>YYYYMMDD: refers to sample harvest date.</li> <li>50: refers to sample extract concentration (50%).</li> <li>dx: refers to duplicate number (from 1 up to 8). Chromatograms are considered duplicates if the harvesting date and hour is the same. The standard harvest time is 8 am and it may/not be specified on the chromatograms. Harvesting time is specified in the TIFF file name only if it is different than the standard (8 am). In that case, the name of the TIFF file is: YYYYMMDD_50/10_HHHH_dx (where HHHH stands for harvesting time).</li> </ul> <p> </p>
Electronic structure fingerprints of nickel-cobalt-manganese oxide from x-ray spectroscopy and high-throughput ab initio calculations
<p>AiiDA archives of the calculations presented in the paper "Electronic structure fingerprints of nickel-cobalt-manganese oxide from x-ray spectroscopy and high-throughput <em>ab initio</em> calculations".</p> <p> </p> <ul> <li>structures_and_general_info.aiida: The "EnumlibCalcJob" to generate the structural candidates, the resulting initial structures and AiiDA "Dict" nodes containing the mapping of the different steps (via their corresponding <em>uuid</em>) to each structure.</li> <li>pre_optimization.aiida: The relevant calculations to perform the pre-relaxation.</li> <li>optimization.aiida: The relevant calculations to perform the final structural optimization.</li> <li>electronic_structure.aiida: The bandstructure and DOS/PDOS calculations. All preliminary calculations such as SCF and NSCF calculations to obtain the eigenvalues are included as well.</li> </ul> <p>Finally, the Pandas DataFrame containing the PDOS for each site of all the structures (resolved into orbital and spin contributions) which builds the foundation for the presented analysis is stored in the `pickle` format in `pdos_all.pckl`.</p>
Raw I/Q measurement data and software for GNSS RFF fingerprinting
<p>This is a dataset of raw IQ GNSS measurements, with and without spoofing. Each scenario contains two sub-scenarios: a clean data (with only GNSS PRNs from the shy satellites) and a spoofed data (with a mixture of shy PRNs and two spoofing PRNs) <br>The raw IQ samples for the GNSS signal analysis were collected over two days: October 18 and October 19, in 2022. </p> <p><br>The samples are stored in the /data/ directory. There is a subset of 1000 samples, randomly selected from the original dataset for each scenario to keep the open-access data to a reasonable size.</p> <p><br>How to cite this Zenodo data: W. Wang, J. Sankari, E.S. Lohan, and M. Valkama, "Raw I/Q measurement data and software for GNSS RFF fingerprinting", open-access data set, 10.5281/zenodo.13846381, Sep 2024.</p> <p>More details available in the readme file.</p>
Dataset Literature Review on Cyberlaw and Fingerprints
<p>Ini merupakan dataset dari literature review on cyberlaw and fingerprints</p>
Dataset of the paper "Who Touched My Browser Fingerprint?"
<p>Simplified dataset of the IMC paper. Separated by Tabs. </p>
Implementation of New Multimodal MRI Sequences by Fingerprint to Improve Ischemic Stroke Examination.
ClinicalTrials.gov study NCT07199920. IPD Sharing: NO. Countries: 1. Publications: 0.
Fingerprint Characterization of Advanced HCC
ClinicalTrials.gov study NCT02372162. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Observational Small Intestine and Blood Fingerprint (SmIle) Study in Parkinson's Disease
ClinicalTrials.gov study NCT06003608. IPD Sharing: NO. Countries: 1. Publications: 0.
Metabolomic and Proteomic Fingerprinting in Peri-implant Diseases
ClinicalTrials.gov study NCT04283903. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Defining a PD-specific Breath Fingerprint of Underlying Inflammatory and Neurodegenerative Processes
ClinicalTrials.gov study NCT02749214. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Longitudinal Neural Fingerprinting of Opioid-use Trajectories
ClinicalTrials.gov study NCT06207162. IPD Sharing: YES. Countries: 1. Publications: 0.
Leucoaraiosis and Multimodal MRI With Fingerprinting Technique
ClinicalTrials.gov study NCT06181981. IPD Sharing: NO. Countries: 1. Publications: 0.
Fingerprinting of Impulsivity
ClinicalTrials.gov study NCT02722174. IPD Sharing: Not stated. Countries: 1. Publications: 0.
HFPEF-project: Heart Failure Phenotyping - Exploring the Fingerprints
ClinicalTrials.gov study NCT06465043. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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.