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468 results for “Spikes”
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Spiking Session 1 - Phycocyanin
<p>Accompanying data to publication: <strong>Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging</strong></p> <p>Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.</p>
Hexaploid Wheat Spike Image Dataset (7 species and one amphidiploid; 190 images)
<p>The sample included plants of seven species of hexaploid wheat and one amphidiploid from the collection of Dr. N.P. Goncharov (Institute of Cytology and genetics, SB RAS, Novosibirsk, Russia). Plants were grown in a hydroponic greenhouse under individual isolation and standard conditions of humidity, temperature and light for several seasons. In total, the sample included 19 accessions and 190 plants (1 spike per plant).</p> <p>Spike images were obtained under laboratory conditions using a “table” protocol as described in previous work (Genaev et al. (2019) Morphometry of the Wheat Spike by Analyzing 2D Images. Agronomy 2019, 9, 390). </p> <p>This is supplementary material for the paper: Komyshev, E.G.; Genaev, M.A.; Kruchinina, Y.V.; Koval, V.S.; Goncharov, N.P.; Afonnikov, D.A. Evaluation of the Spike Diversity of Seven Hexaploid Wheat Species and an Artificial Amphidiploid Using a Quadrangle Model Obtained from 2D Images. Plants 2024, 13, 2736. Please cite this work when you publishing material related to this dataset.</p>
Replication Data For: Spiking patterns in the globus pallidus highlight convergent neural dynamics across diverse genetic dystonia syndromes
<div><strong>Human Globus Pallidum Single-Unit Activity Dataset in Genetic Dystonia Patients</strong></div> <div> </div> <div>This dataset consists of tabular data encompassing diverse neural features extracted from spiking trains of stable single-unit activity. These units were isolated from raw microelectrode recordings obtained from the globus pallidum of genetic dystonia patients who underwent globus pallidus internal (GPi) deep brain stimulation (DBS) surgery. The dataset includes anonymized patient IDs, details about the patient's genetic dystonia mutation, as well as information on the hemisphere and depth of microelectrode recordings (MER). Additionally, it features neural properties such as firing rate, spiking regularity, neural bursts, oscillations, and pause characteristics of isolated single-unit activities (SUAs).</div> <div> </div> <div>To process the raw MER, we applied a semi-parametric offline spike sorting algorithm to isolate SUAs. The SUAs were analyzed both in the temporal and frequency domains to derive a comprehensive set of features related to spiking patterns.</div> <div> </div> <div>For those interested in replicating or understanding the feature extraction process, the MATLAB source code is available in the <a href="github.com/ahmetofficial/Spike-Feature-Generator">Github repository</a>.</div>
Spiking Seizure Classification Dataset
<p><strong>Dataset for event encoded analog EEG signals for detection of Epileptic seizures </strong></p> <p>This dataset contains events that are encoded from the analog signals recorded during pre-surgical evaluations of patients at the Sleep-Wake-Epilepsy-Center (SWEC) of the University Department of Neurology at the Inselspital Bern. The analog signals are sourced from the <a href="http://ieeg-swez.ethz.ch/" target="_blank" rel="noopener">SWEC-ETHZ iEEG Database</a></p> <p>This database contains event streams for 10 seizures recorded from 5 patients and generated by the DYnamic Neuromorphic Asynchronous Processor (DYNAP-SE2) to demonstrate a proof-of-concept of encoding seizures with network synchronization. The pipeline consists of two parts (I) an Analog Front End (AFE) and (II) an SNN termed as"Non-Local Non-Global" (NLNG) network. </p> <p>In the first part of the pipeline, the digitally recorded signals from <a href="http://ieeg-swez.ethz.ch/" target="_blank" rel="noopener">SWEC-ETHZ iEEG Database</a> are converted to analog signals via an 18-bit Digital-to-Analog converter (DAC) and then amplified and encoded into events by an Asynchronous Delta Modulator (ADM). Then in the second part, the encoded event streams are fed into the SNN that extracts the features of the epileptic seizure by extracting the partial synchronous patterns intrinsic to the seizure dynamics. </p> <p>Details about the neuromorphic processing pipeline and the encoding process are included in a manuscript under review. The preprint is available in <a title="Encoding seizures with partial synchronization: A spiking neural network for biosignal monitoring on a mixed signal neuromorphic processor" href="https://doi.org/10.1101/2024.05.22.595225" target="_blank" rel="noopener">bioRxiv</a></p> <p><strong>Installation</strong><br>The installation requires Python>=3.x and conda (or py-venv) package. Users can then install the requirements inside a conda environment using </p> <pre><code>conda env create -f requirements.txt -n sez</code></pre> <p><br>Once created the conda environment can be activated with <strong><em>conda activate sez</em></strong></p> <div> <div>The main files in the database are described in the hierarchy below.</div> <div> </div> <div>EventSezDataset/</div> <div>├─ data/</div> <div>│ ├─ P x S x</div> <div>│ │ ├─ Pat x _Sz_ x _CH x .csv</div> <div>├─ LSVM_Params/</div> <div>│ ├─ opt_svm_params/</div> <div>│ ├─ pat_x_features_SYNCH/</div> <div>├─ fig_gen.py</div> <div>├─ sync_mat_gen.py</div> <div>├─ SeizDetection_FR.py</div> <div>├─ SeizDetection_SYNCH.py</div> <div>├─ support.py</div> <div>├─ run.sh</div> <div>├─ requirements.txt</div> <div> </div> <div>where <strong>x</strong> represents the Patient ID and the Seizure ID respectively.</div> <ul> <li><strong>requirements.txt</strong>: This file lists the requirements for the execution of the Python code.</li> <li><strong>fig_gen.py</strong>: This file plots the analog signals and the associated AFE and NLNG event streams. The execution of the code happens with `<em>python fig_gen.py 1 1 13', </em>where patient 2, seizure 1, and channel 13 of the recording are plotted.</li> <li><strong>sync_mat_gen.py: </strong>This file describes the function for plotting the synchronization matrices emerging from the ADM and the NLNG spikes with either linear or log colorbar. The execution of the code happens with `<em>python sync_mat_gen.py 1 1' or `python sync_mat_gen.py 1 1 log'</em>. This execution generated four figures for pre-seizure, First Half of seizure, Second Half of seizure, and post-seizure time periods, where patient 1 and seizure 1. The third option can either be left blank or input as `lin` or `log`, for respective color bar scales. The time is the signal-time as mentioned in the table below.</li> <li><strong>run.sh:</strong> A simple Linux script to run the above code for all patients and seizures.</li> <li><strong>SeizDetection_FR.py:</strong> This file runs the LSVM on the ADM and NLNG spikes, using the firing rate (FR) as a feature. The code is currently set up with plotting with pre-computed features (in the LSVM_Params/opt_svm_params/ folder). Users can use the code for training the LSVM with different parameters as well.</li> <li><strong>SeizDetection_SYNCH.py:</strong> This file runs the LSVM on the kernelized ADM and NLNG spikes, using the flattened SYNC matrices as a feature. The code is currently set up with plotting with pre-computed features (in the LSVM_Params/pat_x_features_SYNCH/ folder). Users can use the code for training the LSVM with different parameters as well.</li> <li><strong>LSVM_Params:</strong> Folder containing LSVM features with different parameter combinations.</li> <li><strong>support.py:</strong> This file contains the necessary functions.</li> <li><strong>data/P1S1/</strong>: This folder, for example, contains the event streams for all channels for seizure 1 of patient 1.</p> <ul> <li><strong>Pat1_Sz_1_CH1.csv</strong>: This file contains the spikes of the AFE and the NLNG layers with the following tabular format (which can be extracted by the <em>fig_gen.py</em>)</li> </ul> </li> </ul> <div> <h3>## Comments</h3> <p># SStart: 180 <em>//Start of the Seizure in signal time</em><br># SEnd: 276.0 <em>//Start of the Seizure in signal time</em><br># Pid: 2 <em>// The patient ID as per the <a href="http://ieeg-swez.ethz.ch/" target="_blank" rel="noopener">SWEC-ETHZ iEEG Database</a> </em><br># Sid: 1 <em> // The Seizure ID as per the <a href="http://ieeg-swez.ethz.ch/" target="_blank" rel="noopener">SWEC-ETHZ iEEG Database</a> </em> <br># Channel_No: 1 <em>// The channel number</em></p> <div> <table> <tbody> <tr> <td><strong>SYS_time</strong></td> <td><strong>signal_time</strong></td> <td><strong>dac_value</strong></td> <td><strong>ADMspikes</strong></td> <td><strong>NLNGspikes</strong></td> </tr> <tr> <td>The time from the interface FPGA</td> <td>The time of the signal as per the SWEC ETHZ Database</td> <td>The value of the analog signals as recorded in the SWEC ETHZ Database</td> <td>The event-steam is the output of the AFE in boolean format. True represents a spike</td> <td>The spike-steam is the output of the SNN in boolean format. True represents a spike</td> </tr> </tbody> </table> </div> </div> </div>
Figure 14. A, B in giant, spike-toothed salmon, Oncorhynchus rastrosus and the "Proto-Tuolumne River" (early Pliocene) of Central California
Figure 14. A, B. Measurements of O. rastrosus premaxillary teeth (31 specimens total), freshwater (pink/red) vs. coastal marine (grey/black). A. Average lengths of osseous bases. B. Average lengths of tooth cusps. Freshwater specimens are from the Mehrten Formation (pink) and Pinole Formation (red). Coastal marine specimens are from the Monterey, Santa Margarita, and Capistrano formations. How measurements were taken is shown in Fig. 10).
Figure 13 in giant, spike-toothed salmon, Oncorhynchus rastrosus and the "Proto-Tuolumne River" (early Pliocene) of Central California
Figure 13. Coastal marine specimens of O. rastrosus premaxillary teeth from the Santa Margarita Formation, Monterey Formation, and Capistrano Formation. A. LACM 135697. B. LACM 58915. C. LACM 158730. D. LACM 147601. E. LACM 147597. Freshwater specimens from O. rastrosus from the Pinole Tuff Formation. F. UCMP 61550. G. UCMP 61554. H. UCMP 65630. All are lateral views.
Figure 10. A in giant, spike-toothed salmon, Oncorhynchus rastrosus and the "Proto-Tuolumne River" (early Pliocene) of Central California
Figure 10. A. Photograph of fossil salmon locality, T-6, showing beach and overlying cliff. Specimens were collected from the surface of the beach area from ~1957–1964. B. Photograph taken at salmon locality, T-6, showing exposed water-lain tuff within the sands. C. Photograph of sharp unconformity underlying salmon locality. Below the unconformity is pink/tan tuffaceous silt. D. Photograph of T-5, a nearby site to T-6, with the sharp unconformity and large, rounded cobbles.
Figure 8 in giant, spike-toothed salmon, Oncorhynchus rastrosus and the "Proto-Tuolumne River" (early Pliocene) of Central California
Figure 8. Map showing locations of all California specimens examined/measured in this study. Blue circles indicate marine deposits while orange indicates freshwater.
Figure 12 in giant, spike-toothed salmon, Oncorhynchus rastrosus and the "Proto-Tuolumne River" (early Pliocene) of Central California
Figure 12. Freshwater specimens of O. rastrosus premaxillary teeth from the Mehrten Formation (Turlock Lake, CA; UCMP V5405). All are lateral views. A. Left tooth (UCMP 93181). B. Left tooth (UCMP 93183). C. Right tooth (UCMP 136029). D. Right tooth (UCMP 93179). E. Right tooth (UCMP 93184). F. Left tooth (UCMP 61951).
Figure 9. A–F in giant, spike-toothed salmon, Oncorhynchus rastrosus and the "Proto-Tuolumne River" (early Pliocene) of Central California
Figure 9. A–F. Measurements taken of O. rastrosus premaxillary teeth. A. Greatest length of the osseous base. B. Greatest length of the tooth cusp. C. Greatest height of the tooth cusp and osseous base. D. Greatest height of the osseous base. E. Greatest width of the osseous base. F. Greatest width of the tooth cusp.
Figure 5 in giant, spike-toothed salmon, Oncorhynchus rastrosus and the "Proto-Tuolumne River" (early Pliocene) of Central California
Figure 5. Generalized stratigraphy of the Mehrten Formation highlighted in grey. Modified from Wagner 1981.
Figure 3 in giant, spike-toothed salmon, Oncorhynchus rastrosus and the "Proto-Tuolumne River" (early Pliocene) of Central California
Figure 3. Map of California showing the location of Turlock Lake in relation to nearby cities, towns, and landmarks.
Figure 4 in giant, spike-toothed salmon, Oncorhynchus rastrosus and the "Proto-Tuolumne River" (early Pliocene) of Central California
Figure 4. Early Pliocene (~5 Ma) reconstruction of California showing the modern locations of the Tuolumne River, Turlock Lake, latite flows of the Stanislaus Group, the Little Walker Caldera, the path of the "proto-Tuolumne River", and the shallow marine embayment near Bakersfield. Based on Blakey and Ranney 2008.
Figure 11. Stratigraphic section through the T-6 in giant, spike-toothed salmon, Oncorhynchus rastrosus and the "Proto-Tuolumne River" (early Pliocene) of Central California
Figure 11. Stratigraphic section through the T-6 fossil locality (measured/described by Francisco Palacios and Jacob Biewer in October, 2015).
Figure 7. A–K in giant, spike-toothed salmon, Oncorhynchus rastrosus and the "Proto-Tuolumne River" (early Pliocene) of Central California
Figure 7. A–K. "Proto-Tuolumne River" deposits exposed at Turlock Lake indicated by orange on inset map. Arrows indicate observed flow directions. A. Unconformity exposed northwest of T-24. B. Channel cross-section visible at T-21. C. Unconformity exposed far north of salmon locality. D. Unconformity exposed on 'unmapped' island. E. Cobbles above unconformity at T-5 showing clear imbrication toward the left (southwest direction). F. Sharp, wavy unconformity on T-3/T-4 island. G. Cross-bedded sand of cliff face overlying salmon locality. H. Steep cross-beds and silt rip-up clasts exposed at cliff overlooking T-8. I. Unconformity exposed between T-15 and T-14. J. Cross-beds located southwest of T-24. K. Beach composed of cross-bedded sand and gravel at T-23.
Distribution and occurrence frequency of dB/dt spikes during magnetic storms 1980 - 2019
<p>Dataset and files linked to the study "Distribution and occurrence frequency of dB/dt spikes during magnetic storms 1980 - 2019" by A. Schillings et al., in Space Weather.</p> <p> </p>
R1A-R1AB-SPIKE-ACE2_Dataset
<p>SARS-Cov-2 proteins: a comparison with ACE2 protein</p>
Images and results from a visual inspection of AIA spikes
<p>This upload contains images and results used in a manuscript submitted by P.R. Young et al. to the Solar Physics journal. The preprint is available at: <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210802624Y/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv210802624Y/abstract</a>.</p> <p>The files are:</p> <p>RESULTS.txt - The results of the spike analysis.<br> make_spike_images.pro - An IDL routine for generating the pdf files<br> (requires software in the Solarsoft IDL<br> distribution to run)<br> spike_images_*_*.pdf - A set of pdf files containing AIA images. For<br> each spike there is a pair of images in a<br> row. Each image is 30" x 30" in size and<br> centered on the spike. The left panel shows<br> the original image, and the right panel shows<br> the despiked image. There are 45 pdf files in<br> all. </p> <p> </p>
RD-Connect GPAP synthetic data spiked-in variant data
<p>This data is a subset of Rare Disease Synthetic Dataset (<a href="https://ega-archive.org/datasets/EGAD00001008392">EGAD00001008392</a>) dataset. The subset only contains the chromosomal regions with the spiked-in causative variants. The associated study is the Human genomic and phenotypic synthetic data for the study of rare diseases (<a href="https://ega-archive.org/studies/EGAS00001005702">EGAS00001005702</a>) study. For more info go to <a href="https://ega-archive.org/">https://ega-archive.org/</a>.</p> <p>All this data was created with the support of the RD-Connect GPAP (<a href="https://platform.rd-connect.eu/">https://platform.rd-connect.eu/</a>), EC H2020 project EJP-RD (grant # 825575), EC H2020 project B1MG (grant # 951724) and Generalitat de Catalunya VEIS project (grant # 001-P-001647).</p>
Shotgun metagenomic sequencing dataset of a synthetic mock community containing 20 genomes spiked-in at even and staggered concentrations.
<p>Shotgun metagenomics (SM) sequencing is a popular method used in microbial ecology to obtain insights on microbial community structure and function potential in a given biological system without the need to cultivate microorganisms. The dataset described in this article describes technical triplicates of shotgun metagenomic sequence libraries generated from two purified and titrated mixes of 20 distinct reference bacterial genomes for which key characteristics such as genome size, sequence and spiked-in concentrations are known. In one of the genomic DNA mix, each genome is spiked-in at similar concentrations (representing an even microbial community) and in the other, genomes are spiked-in at different concentrations with some genomes highly abundant and other in low quantity, mimicking an uneven microbial community DNA extract. In order to be interpretable, SM sequencing data needs to be properly analyzed by complex analytical bioinformatic pipelines. Environments investigated with this method can range from simple to very complex. Typically, microbial communities contain microbes that are ubiquitous and some others much rarer. Analysis of rare microbes in a complex microbial community are challenging to perform as their sequencing signals get submerged by the microbial genomes that are more abundant. In this context, it is critical to have access to sequencing data of simple mock communities of mixes of well characterized genomes in order to develop and validate bioinformatic methods that aim to accurately analyze microbial communities.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.