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.
720
datasets available to search
ShareScore release 0.9.0
Dataset results
720 results for “Identification data”
Data and R script for publication: The LOKI underwater imaging system and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean
<p>This is a www.zenodo.org data and R script upload for publication:</p> <p> </p> <p>Schmid, M.S. et al., The LOKI underwater imaging system and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean. Methods in Oceanography (2016), http://dx.doi.org/10.1016/j.mio.2016.03.003</p> <p> </p> <p>Downloadable script: Script_Schmid_Mio_2016_data_upload.R</p> <p>Downloadable data: Schmid_2016_MIO_CGlac.csv</p>
MRI raw data to publication: Fuzzy ripple artifact in high resolution fMRI: identification, cause, and mitigation.
<p>These Data are refering to the maunscript "Fuzzy ripple artifact in high resolution fMRI: identification, cause, and mitigation" authored by </p> <p>Renzo Huber1, Rüdiger Stirnberg2, A Tyler Morgan1, David A Feinberg3,4-5, Samantha J Ma6, Philipp Ehses3, Omer Faruk Gulban2,7, Kenshu Koiso2, Isabel Gephart1, Stephanie Swegle1, Susan Wardle1, Emily Ma2, Andrew Persichetti1, Alexander JS Beckett4-5, Tony Stöcker3, Nicolas Boulant8, Benedikt A Poser2, Peter Bandettini1</p> <p><strong> </strong></p> <p>1 NIMH, NIH, Bethesda, United States,</p> <p>2 German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany,</p> <p>3 Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States, </p> <p>4 Advanced MRI Technologies, Sebastopol, CA, United States,</p> <p>5 CN, FPN, University of Maastricht, The Netherlands,</p> <p>6 Siemens Medical Solutions USA, Inc., Berkeley, CA, USA,</p> <p>7 Brain Innovation, Maastricht, The Netherlands,</p> <p>8 CEA, NeuroSpin, University Paris Saclay, France.</p> <p> </p> <h1>Abstract</h1> <p><strong>Purpose:</strong> High resolution fMRI is an emerging research field focused on capturing functional signal changes across cortical layers. However, the data acquisition is limited by low spatial frequency EPI artifacts; termed as Fuzzy Ripples. These artifacts limit the practical applicability of acquisition protocols with higher spatial resolution, faster acquisition speed, and they challenge imaging in lower brain areas. </p> <p><strong>Methods: </strong>We characterize Fuzzy Ripple artifacts across commonly used sequences and distinguish them from conventional EPI Nyquist ghosts, off-resonance effects, and GRAPPA artifacts. To investigate their origin, we employ dual polarity readouts.</p> <p><strong>Results: </strong>Our findings indicate that Fuzzy Ripples are primarily caused by kx-specific imperfections in gradient trajectories, which can be exacerbated by inductive coupling between third-order shims and readout gradients. We also find that these artifacts can be mitigated through complex-valued averaging of dual polarity EPI or by disconnecting the third-order shim.</p> <p><strong>Conclusion:</strong> The proposed mitigation strategies allow for overcoming current limitations in layer-fMRI protocols: </p> <p>(1) Achieving resolutions beyond 0.8mm is feasible, and even at 3T, we achieved 0.53mm voxel functional connectivity mapping. </p> <p>(2) Temporal acquisition speed can be increased to GRAPPA 8. </p> <p>(3) Sub-millimeter fMRI is achievable in lower brain areas, including the cerebellum.</p> <p> </p>
Figure 2 from: Boroni NL, Lobo LS, Romano PSR, Lessa G (2017) Taxonomic identification using geometric morphometric approach and limited data: an example using the upper molars of two sympatric species of Calomys (Cricetidae: Rodentia). Zoologia 34: 1-11. https://doi.org/10.3897/zoologia.34.e19864
Figure 2 - Landmarks of the molar used in this study. For landmarks description, see text.
Figure 1 from: Boroni NL, Lobo LS, Romano PSR, Lessa G (2017) Taxonomic identification using geometric morphometric approach and limited data: an example using the upper molars of two sympatric species of Calomys (Cricetidae: Rodentia). Zoologia 34: 1-11. https://doi.org/10.3897/zoologia.34.e19864
Figure 1 - Map of Minas Gerais (Brazil), with the study location, municipality of Cordisburgo.
FIGURE 3 in An analysis of fossil identification guides to improve data reporting in citizen science programs
FIGURE 3. The setup used to photograph bromalites for the identification guides.
FIGURE 2 in An analysis of fossil identification guides to improve data reporting in citizen science programs
FIGURE 2. Stratigraphic column of FMMS.
Figure 1 from: Tibpromma S, Hyde K, Bhat J, Mortimer P, Xu J, Promputtha I, Doilom M, Yang J, Tang A, Karunarathna S (2018) Identification of endophytic fungi from leaves of Pandanaceae based on their morphotypes and DNA sequence data from southern Thailand. MycoKeys 33: 25-67. https://doi.org/10.3897/mycokeys.33.23670
Figure 1 Habitats of the host plants: a, b Pandanus spp. c, d Freycinetia spp.
Figure 1 from: Tibpromma S, Hyde K, Bhat J, Mortimer P, Xu J, Promputtha I, Doilom M, Yang J, Tang A, Karunarathna S (2018) Identification of endophytic fungi from leaves of Pandanaceae based on their morphotypes and DNA sequence data from southern Thailand. MycoKeys 33: 25-67. https://doi.org/10.3897/mycokeys.33.23670
Figure 1 - Habitats of the host plants: a, b Pandanus spp. c, d Freycinetia spp.
The raw data about the research of identification of biomarkers related to systemic sclerosis with or without pulmonary hypertension via co-expression analysis
<p>This file includes some of necessary raw data and code about the research of identification of biomarkers related to systemic sclerosis with or without pulmonary hypertension via co-expression analysis.</p> <p> </p>
Identification data for discrimination between species from genus Apis
<p>The data in dw.xml file can be used for identification of 9 species from genus <em>Apis</em>:<em> A. andreniformis, A. cerana, A. dorsata, A. florea, A. koschevnikovi, A. laboriosa, A. mellifera, A. nigrocincta, A. nuluensis</em>. The discrimination is based on 19 landmarks of a forewing. The data were obtained from:</p> <p>Bustamante, T., Fuchs, S., Grünewald, B., & Ellis, J. D. (2021). A geometric morphometric method and web application for identifying honey bee species (<em>Apis</em> spp.) using only forewings. Apidologie, 52(3), 697-706. https://doi.org/10.1007/s13592-021-00857-7</p> <p>Please cite Bustamante et al. (2021) if you find this data useful. </p> <p>The landmark order was change to be compatible with IdentiFly. Position of landmarks 7 and 14 differs from Ruttner (1988) and configuration used in IdentiFly. </p> <p>The dw.xml file can be used in IdentiFly software http://drawwing.org/identifly or in R package IdentiFlyR https://github.com/DrawWing/IdentiFlyR. </p> <p> </p>
Data from: Camouflage, detection and identification of moving targets
Nearly all research on camouflage has investigated its effectiveness for concealing stationary objects. However, animals have to move, and patterns that only work when the subject is static will heavily constrain behaviour. We investigated the effects of different camouflages on the three stages of predation—detection, identification and capture—in a computer-based task with humans. An initial experiment tested seven camouflage strategies on static stimuli. In line with previous literature, background-matching and disruptive patterns were found to be most successful. Experiment 2 showed that if stimuli move, an isolated moving object on a stationary background cannot avoid detection or capture regardless of the type of camouflage. Experiment 3 used an identification task and showed that while camouflage is unable to slow detection or capture, camouflaged targets are harder to identify than uncamouflaged targets when similar background objects are present. The specific details of the camouflage patterns have little impact on this effect. If one has to move, camouflage cannot impede detection; but if one is surrounded by similar targets (e.g. other animals in a herd, or moving background distractors), then camouflage can slow identification. Despite previous assumptions, motion does not entirely 'break' camouflage.
Data from: DNA barcoding gap: reliable species identification over morphological and geographical scales
The philosophical basis, and utility of DNA barcoding has been a subject of numerous debates. While most literature embraces it, some studies continue to question its use in dipterans, butterflies, and marine gastropods. Here, we explore the utility of DNA barcoding in identifying spider species that vary in taxonomic affiliation, morphological diagnosibility and geographic distribution. Our first test searched for a "barcoding gap" by comparing intra- and interspecific means, medians and overlap in more than 75,000 computed Kimura 2 parameter (K2P) genetic distances in three families. Our second test compared K2P distances of congeneric species with high versus low morphological distinctness in 20 genera of 11 families. Our third test explored the effect of enlarging geographical sampling area at a continental scale on genetic variability in DNA barcodes within 20 species of nine families. Our results generally point towards a high utility of DNA barcodes in identifying spider species. However, the size of the barcoding gap strongly depends on taxonomic groups and practices. It is becoming critical to define the barcoding gap statistically more consistently, and to document its variation over taxonomic scales. Our results support models of independent patterns of morphological and molecular evolution by showing that DNA barcodes are effective in species identification regardless of their morphological diagnosibility. We also show that DNA barcodes represent an effective tool for identifying spider species over geographic scales, yet their variation contains useful biogeographic information.
Anonymized data for paper "Cross-Project Defect Identification via Path-Based Semantic Feature Representation" submitted to ICSE 2022
<p>The project includes the dataset and code used in the submitted ICSE 2022 paper titled "# 971 Cross-Project Defect Identification via Path-Based Semantic Feature Representation"</p>
Data from: Rapid CRISPR-Cas13a genetic identification enables new opportunities for listed Chinook salmon management
<p>Figures and intermediate data files for https://github.com/MacCampbell/chinook-sherlock</p>
Supporting data to "Sediment source and pathway identification using Sentinel-2 imagery and (kayak-based) lagrangian river profiles on the Vjosa river"
<p>This release contains the supporting data files that are part of the manuscript "Sediment source and pathway identification using Sentinel-2 imagery and (kayak-based) lagrangian river profiles on the Vjosa river."</p>
Supplementary Website, Data, and Scripts for the Paper "Automatic Core-Developer Identification on GitHub: A Validation Study"
<p>Supplementary website containing result plots, pseudonymized input data, resulting pseudonymized classification data, analysis scripts, and Dockerfile used to produce the results of the paper "Automatic Core-Developer Identification on GitHub: A Validation Study".</p> <p>The pseudonymized input data is also availble separately here: https://zenodo.org/record/7775078</p> <p>The resulting pseudonymized classification data is also available separately here: https://zenodo.org/record/7775385</p>
Data for: Novel Quantitative Methods to Enable Multispectral Identification of High-Purity Water Ice Exposures on Mars from High Resolution Imaging Science Experiment (HiRISE) Images
<p>This repository contains all the HiRISE UNFILTERED products used in the paper by Rangarajan et al. (2023) titled "Novel quantitative methods to enable multispectral identification of high-purity water ice exposures using High Resolution Imaging Science Experiment (HiRISE) images" published in Icarus. </p>
Identification of Outcome Relevant Indicators in Routine Data
ClinicalTrials.gov study NCT04670744. IPD Sharing: NO. Countries: 1. Publications: 0.
Identification of Neuroinflammation and Neuroimaging Biomarkers Through Data Driven Artificial Intelligence Techniques for Unraveling the Heterogeneity of Aged Subjects at Risk of Dementia and to Bett
ClinicalTrials.gov study NCT06746909. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Data Collection and Identification of Infection-responsible Bacterial Resistances in Cirrhotic Patients
ClinicalTrials.gov study NCT03965260. IPD Sharing: NO. 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.