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7,195 results for “SET”
Understanding unconventional magnetic order in a candidate axion insulator by resonant elastic x-ray scattering - Data set
<p>This data set includes the resonant elastic xray scattering (REXS) data collected at the I16 (Diamond Light Source, United Kingdom) and P09 (DESY, Germany) beamline, along with the magnetization data.</p> <p>I16 Data<br> - Temperature dependence of the L=15 reflection<br> - Temperature dependence of the L=14.333 reflection<br> - 00L dependence (6K - 20K)<br> - Azimuthal dependence of L=15 reflection<br> - Azimuthal dependence of L=13.667 reflection</p> <p>P09 Data<br> - Field dependence of L=15 reflection (pi-pi channel)<br> - Field dependence of L=15 reflection (pi-sigma channel)<br> - Field dependence of L=14.333 reflection (pi-pi channel)<br> - Field dependence of L=14.333 reflection (pi-sigma channel)</p> <p>Magnetization Data<br> - Field Dependence of the Magnetization<br> </p>
Neural net training and test data set
<p>In this zip file you will find several folders as well as a readme file explaining the dataset. The neural net file can be directly used in cellpose to segment <strong>widefield</strong> <strong>20x objective</strong> imaging data of neutrophils. The neural net could be used for other types of data, but might perform below expectations.</p>
Short communication: Synchrotron-based elemental mapping of single grains to investigate variable infrared-radiofluorescence emissions [Data set]
<p>This dataset accompanies a research paper in the journal Geochronology (<span><a href="https://doi.org/10.5194/gchron-6-77-2024"><span>https://doi.org/10.5194/gchron-6-77-2024</span></a></span>). It contains the raw output of micro-XRF measurements carried out at the 5-ID SRX beamline at the National Synchrotron Light Source II (NSLS-II) at Brookhaven National Laboratory, USA, on coarse K-feldspar grains. Additionally, the XRF intensity attributed to each element after spectral fitting is provided for elemental mapping. The dataset also includes the output from scanning electron microscope energy-dispersive X-ray spectroscopy (SEM-EDS) measurements on coarse K-feldspar grains of two samples taken at Archéosciences Bordeaux, France.</p>
Figure 5. BEAST chronogram from a data set corresponding with Table 1 in Verifying Australian Nilotanypus Kieffer (Chironomidae) In A Global Perspective: Molecular Phylogenetic And Temporal Analyses, New Species And Emended Generic Diagnoses
Figure 5. BEAST chronogram from a data set corresponding with Table 1. Values at nodes are time to most recent common ancestor (tmrca) with HPD (95% Highest Posterior Density) intervals in parentheses. The time scale is in millions of years before present.
Methylation-free E.coli nanopore sequencing (ONT R9.4.1) data set
<p>The data set consists of fast5 files divided into 5 zip files (fast5_[1-5].zip), a genome record (Ecoli_K12_MG1655.fasta), an Illumina assembly genome (illumina_contigs.fasta) and a fastq file from Guppy 5 (guppy_basecalled.fastq.gz). We sequenced the Ecoli non-methylated genomic DNA (D5016, Zymo Research) with an ONT MinION device. The sequencing libraries were prepared by fragmenting the genomic DNA using Covaris g-TUBE and a Ligation sequencing kit (SQK-LSK109, Oxford Nanopore) with Flow Cell chemistry R9.4.1. We also performed short-read Illumina sequencing on the same sample using the TruSeq PCR-free library preparation on the MiSeq sequencing platform (Illumina, USA), and constructed a draft assembly from the Illumina sequencing results using SPAdes v3.6.0. We also upload a reference genome directly obtained from the E.coli sample producer website. </p> <p>In addition, the data set contains two fastq files that produced by the Lokatt basecaller (lokatt_basecalled.fasta.gz) and local-trained Bonito basecaller (bonito_local_basecalled.fastq.gz ), respectively, which are used for benchmarking in the Lokatt basecaller paper.</p>
Data set used in "Effect of turbulence and viscosity models on wall shear stress derived biomarkers for aorta simulations"
<p>Data set used in "Effect of turbulence and viscosity models on wall shear stress derived biomarkers for aorta simulations"</p> <p>Includes the data for 20 heartbeats. Divided into external and internal walls regions. </p>
Pyrolysis Model Data Set Contribution for the MaCFP-3 Workshop October 2023
<p>Pyrolysis model data set contribution for the <a href="https://iafss.org/macfp/">MaCFP-3 workshop October 2023</a></p> <p>The contributions are also available via the materials database <a href="https://github.com/MaCFP/matl-db">MaCFP git repository.</a></p> <p>For the inverse modelling in general, please see the manuscript <a href="https://doi.org/10.48550/arXiv.2303.17446">"PMMA Pyrolysis Simulation -- from Micro- to Real-Scale"</a></p> <p> </p> <p>There are three different ZIP archives provided:</p> <ul> <li>"MaCFP_Gasification" contains the simulations reproducing the NIST Gasification Apparatus for validation of the parameter sets, as requested per MaCFP. There are three different runs conducted, two for the originally submitted parameter sets (BUW-FZJ Approach A/B). No changes to the parameter sets are conducted. A new parameter set is provided to the MaCFP repository (BUW-FZJ PMMA01) and its validation simulations are provided as well. Furthermore, Jupyter notebooks are provided to process the simulation data.</li> <li>"PyrolysisKinetics_PMMA01" contains the inverse modelling run to determine the pyrolysis reaction kinetics.</li> <li>"Thermophysical_PMMA01" contains the inverse modelling run to determine the thermophysical parameters.</li> </ul>
Evaluating a poroelastic model via pore pressure signals in seafloor sediments [data set]
<p>The csv files consist of pressure data collected off the coast of Camp Pendleton between 10 February 2021 and 25 February 2021, and include both pore pressure data from two instrumented surrogates and pressure data from a Nortek Signature. Timestamps are in posix time; pressure is in kPa. The time series for Surrogate A are prefixed "surrA"; those for Surrogate B are prefixed "surrB". The Nortek Signature time series is prefixed "Sig1000".</p>
MOSAIKS Feature Sets for Zambia
<p>These two features sets cover the Survey Enumeration Areas and the entire country of Zambia. They were created using Sentinel 2 imagrey and their temporal bounds are from 2015 -2023. They can be used for modeling of spacial data.</p>
Data set for "Membrane potential dynamics of excitatory and inhibitory neurons in mouse barrel cortex during active whisker sensing"
<p>Data set for: Kiritani T, Pala A, Gasselin C, Crochet S, Petersen CCH (2023) Membrane potential dynamics of excitatory and inhibitory neurons in mouse barrel cortex during active whisker sensing. PLOS ONE 18: e0287174. doi: 10.1371/journal.pone.0287174</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2023_Kiritani_PLOSONE.pdf" is the Open Access pdf of the online publication in PLOS ONE.</p> <p>2. The file named "Kiritani_data_code.zip" (~5 GB) is a zipped version of a folder "Kiritani_data_code" (~5 GB), which contains the data analysed in the study along with the Matlab codes used to generate the published figures. To access the data and codes, first unzip the file. You need to install the Matlab 'Signal Processing' and 'Curve Fitting' Toolboxes. In Matlab, add the path of the folder 'Kiritani_data_code' and all subfolders. Directly from this folder, you should first run the codes in the folder 'Data_Analysis_Codes', sequentially executing 'Analysis_1.m' through to 'Analysis_9.m'. Note, execution of 'Analysis_9.m' can take a long time (~1 hour on a good desktop PC). You can then run the codes in the folder 'Figure_Plotting_Codes' to generate the figures published in the journal article. In the folder 'Data', you can also find a DataViewer to visualise the data sets, which you can run by executing 'DataViewer.m' directly from the subfolder ‘Data’.</p> <p> </p>
Rock magnetic data sets for Coupled detachment faulting and hydrothermal circulation at 49.7°E Southwest Indian Ridge revealed by seafloor magnetism
<p>The rock magnetic data sets in "Coupled detachment faulting and hydrothermal circulation at 49.7°E Southwest Indian Ridge revealed by seafloor magnetism" was studied, including the density, magnetic susceptibility, NRM, Q ratio and other parameters of rock samples , as well as the thermomagnetic curves, hysteresis loops, FORCs, AF and TD demagnetization.</p>
Comprehensive Set of Structural Comparisons of Transport Domains from Elevator Transporters
<p>This upload is a supplementary dataset for the following preprint: <a href="https://doi.org/10.1101/2023.06.14.544989">N. Trebesch and E. Tajkhorshid. "Structure Reveals Homology in Elevator Transporters." <em>bioRxiv</em>. (2023). DOI: 10.1101/2023.06.14.544989.</a> Please see the preprint for the methods, analysis, and discussion associated with this dataset.</p>
Data set from long-term wave, wind and response monitoring of the Bergsøysund Bridge
<p>Wind, wave, displacement and acceleration data have been collected in a measurement campaign on the Bergsøysund Bridge between the years 2014 and 2018. The data set is now available in this open-access research entry, for free access and download. The data is collected in two h5-files (hierachical data format), with sampling rates 2 Hz and 10 Hz, downsampled from the raw sampling rate of 200 Hz. Note that the data has undergone some minimal signal processing and adjustment, in line with that applied to the Hardanger Bridge data described in Fenerci et al. (2021). Tools and examples for import, data visualization and initial analysis are given in the opyndata Python package available on GitHub (Kvåle, 2022). Furthermore, a document briefly describing the hierarchy and structure of the data, is given. For more details on the measurement system and the bridge, it is referred to Kvåle and Øiseth (2017).</p> <p>The updated, copyrighted version of the appended preprint is published by ASCE with the following DOI: <a href="https://doi.org/10.1061/JSENDH.STENG-12095">10.1061/JSENDH.STENG-12095</a></p>
Simulated brainweb PET/MR data sets for denoising and deblurring
<p>The data set consists of 20 subjects based on the normal anatomical models from the brainweb phantom. See <a href="https://brainweb.bic.mni.mcgill.ca/">https://brainweb.bic.mni.mcgill.ca/</a>.</p> <p>For each subjectXX the data is organized as follows:</p> <ol> <li>image_0.nii.gz -> (1st simulated "random" PET contrast)</li> <li>image_1.nii.gz -> (2nd simulated "random" PET contrast)</li> <li>image_2.nii.gz -> (3rd simulated "random" PET contrast)</li> <li>attenuation_image.nii.gz -> (attenuation image)</li> <li>t1.nii.gz -> (high resolution T1 MR)</li> </ol> <p>All data sets have a shape of (220,220,184) and a voxel size of 1mm x 1mm x 1mm and are provided in nifti format.<br> The T1 MR scans and antomical models used to create the ground truth PET images are taken from the BrainWeb: Simulated Brain Database.</p> <p>See:</p> <p> http://www.bic.mni.mcgill.ca/brainweb/<br> C.A. Cocosco, V. Kollokian, R.K.-S. Kwan, A.C. Evans : "BrainWeb: Online Interface to a 3D MRI Simulated Brain Database"<br> NeuroImage, vol.5, no.4, part 2/4, S425, 1997 -- Proceedings of 3-rd International Conference on Functional Mapping of the Human Brain, Copenhagen, May 1997.<br> R.K.-S. Kwan, A.C. Evans, G.B. Pike : "MRI simulation-based evaluation of image-processing and classification methods"<br> IEEE Transactions on Medical Imaging. 18(11):1085-97, Nov 1999.<br> R.K.-S. Kwan, A.C. Evans, G.B. Pike : "An Extensible MRI Simulator for Post-Processing Evaluation"<br> Visualization in Biomedical Computing (VBC'96). Lecture Notes in Computer Science, vol. 1131. Springer-Verlag, 1996. 135-140.<br> D.L. Collins, A.P. Zijdenbos, V. Kollokian, J.G. Sled, N.J. Kabani, C.J. Holmes, A.C. Evans : "Design and Construction of a Realistic Digital Brain Phantom"<br> IEEE Transactions on Medical Imaging, vol.17, No.3, p.463--468, June 1998</p>
Data set for the journal article: Social life cycle assessment of green methanol and benchmarking against conventional fossil methanol
<p>Single File containing:</p> <ul> <li>Green Methanol Inventories: numerical data as displayed in Figure 4, Main social life cycle inventory data of the green methanol system. </li> <li>Conventional Methanol Inventories: numerical data as displayed in Figure 5, Main social life cycle inventory data of the conventional methanol system. </li> <li>Supplementary information: Diagrams and tables describing teh flowsheet of the simulations used in this work: <ul> <li> <p>Green methanol production process (flowsheet and stream table)</p> </li> <li> <p>Syngas production through Steam Methane Reforming (flowsheet and stream table)</p> </li> <li> <p>Conventional methanol production process (flowsheet and stream table)</p> </li> </ul> </li> </ul>
ProdgerLab-StarDist-HIV Target Cell Training Set
<p>40 annotated immunofluorescence microscopy images (600 microns x 600 microns) of foreskin tissue stained for CD3/CD4/CCR5/Nuclei. These images were used to train StarDist models used for the identification of HIV Target Cells in foreskin tissue section scans. </p>
Multiparameter Water Quality Monitoring System for Continuous Monitoring of Fresh Waters Calibration and Measurement Data Set
<p>This data set contains calibration data for all sensors incorporated in the sensor node. It provides comparison measurements of TPL fluorescence taken by the node and reference spectrofluorimeter. Initial test measurements, as well as site measurements, are also provided. Finally, data from a heuristic method of TPL detection in the presence of algae and mud are also given.</p>
Evaluating the implementation of adult smoking cessation programs in community settings. Protocol for a scoping review
<p>Tables and figures from <em>Evaluating the implementation of adult smoking cessation programs in community settings. Protocol for a scoping review</em><br> </p>
Data set - Measured in a context : making sense of open access book data
<p>For more than a decade, open access book platforms have been distributing titles in order to maximise their impact. Each platform offers some form of usage data, showcasing the success of their offering. However, the numbers alone are not sufficient to convey how well a book is actually performing.</p> <p>Our data set is consists of 18,014 books and chapters. The selected titles have been added to the OAPEN Library collection before 1 January 2022, and the usage data of twelve months (January to December 2022) has been captured. During that period, this collection of books and chapters has been downloaded more than 10 million times. Each title has been linked to one broad subject and the title’s language has been coded as either English, German or other languages.</p> <p>The titles are rated using the TOANI score.</p> <p>The acronym stands for Transparent Open Access Normalised Index. The transparency is based on the application of clear regulations, and by making all data used visible. The data is normalised, by using a common scale for the complete collection of an open access book platform. Additionally, there are only three possible values to score the titles: average, less than average and more than average. This index is set up to provide a clear and simple answer to the question whether an open access book has made an impact. It is not meant to give a sense of false accuracy; the complexities surrounding this issue cannot be measured in several decimal places.</p> <p>The TOANI score is based on the following principles:</p> <ul> <li>Select only titles that have been available for at least 12 months;</li> <li>Use the usage data of the same 12 months period for the whole collection;</li> <li>Each title is assigned one – high level – subject;</li> <li>Each title is assigned one language;</li> <li>All titles are grouped based on subject and language;</li> <li>The groups should consists of at least 100 titles;</li> <li>The following data must be made available for each title: <ul> <li>Platform</li> <li>Total number of titles in the group</li> <li>Subject</li> <li>Language</li> <li>Period used for the measurement</li> <li>Minimum value, maximum value, median, first and third quartile of the platform’s usage data</li> </ul> </li> <li>Based on the previous, titles are classified as: <ul> <li>“Less than average” – First quartile; 25 % of the titles</li> <li>“Average” – Second and third quartile; 50% of the titles</li> <li>“More than average” – Fourth quartile; 25 % of the titles</li> </ul> </li> </ul>
Data platform (genotyping data set) related to ERDF postdoctoral project No. 1.1.1.2/VIAA/4/20/718 "The role of vitamin D gene polymorphisms and its receptors in the modulation of intestinal inflammation in patients with relapsing and progressive forms of multiple sclerosis".
<p><strong>Data platform </strong><strong>(genotyping dataset)</strong> <strong>related to the ERDF postdoctoral project No. </strong><strong>1.1.1.2/VIAA/4/20/718</strong><strong> “</strong><strong>The role of vitamin D and its receptor gene polymorphisms in the modulation of intestinal inflammation in patients with relapsing and progressive forms of multiple sclerosis</strong><strong>”.</strong></p> <p><strong>About the project and gathered data:</strong></p> <p>The dataset contains genotyping data on 289 sex-balanced samples (approximately 60% women / 40% men)) were created at the the multiple sclerosis (MS) Clinic of the Latvian Maritime Medical Center (LMMC) in 2011 (disease duration of 1-51 years); the collection was updated within the framework of the ERDF MS project (2017-2020) and replenished during the ERDF postdoctoral project No. 1.1.1.2/VIAA/4/20/718 “The role of vitamin D and its receptor gene polymorphisms in the modulation of intestinal inflammation in patients with relapsing and progressive forms of multiple sclerosis” (2021-2023).</p> <p>For the <strong>Genotyping dataset </strong>relevant information for each patient from the MS disease cohort, referring to proteasomal gene genetic variations (microsatellites and SNPs): (HSMS006 <em>(PSMA6),</em> HSMS602 <em>(FAM177A1),</em> HSMS701 <em>(KIAA0391)</em>, HSMS702 <em>(KIAA0391)</em> HSMS801 <em>(KIAA0391)</em>, rs11543947<em>(PSMB5), </em>rs2277460 (mi110), rs1048990 (mi8)<em> (PSMA6),</em> rs1048990 (mi8)<em> (PSMA6),</em> rs2295826/rs2295827<em>(PSMC6),</em> rs2348071 <em>(PSMA3),</em> rs2071543, rs9357155 <em>(PSMB8),</em> rs17587<em>(PSMB9),</em> rs74421874 <em>(PSMD9); </em>rs9275596 from HLA region; vitamin D-related genes (VDR and GC) polymorphisms: rs2228570, rs1544410, rs7975232, rs731236 (<em>VDR</em>) and rs7041, rs4588 <em>(GC).</em></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.