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409 results for “information use”
MAMEM Phase I Dataset - A dataset for multimodal human-computer interaction using biosignals and eye tracking information
<p>This dataset combines multimodal biosignals and eye tracking information gathered under a human-computer interaction framework. The dataset was developed in the vein of the MAMEM project that aims to endow people with motor disabilities with the ability to edit and author multimedia content through mental commands and gaze activity. The dataset includes EEG, eye-tracking, and physiological (GSR and Heart rate) signals along with demographic, clinical and behavioral data collected from 36 individuals (18 able-bodied and 18 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. Alongside these data we also include evaluation reports both from the subjects and the experimenters as far as the experimental procedure and collected dataset are concerned. We believe that the presented dataset will contribute towards the development and evaluation of modern human-computer interaction systems that would foster the integration of people with severe motor impairments back into society.</p>
Dataset from "Matthieu Delescluse and Christophe Pouzat (2006) Efficient spike-sorting of multi-state neurons using inter-spike intervals information Journal of Neuroscience Methods 150: 16-29."
<p>The dataset (in HDF5 format) used in Delescluse and Pouzat (2006) Efficient spike-sorting of multi-state neurons using inter-spike intervals information Journal of Neuroscience Methods 150: 16-29. arXiv:q-bio/0505053. See this reference for recording details. Data collected by Matthieu Delescluse. Briefly, 4 channels (data sets Channel_0,1,2,3, organized in a group called 'ExtracellularData'; extracellular recordings along the Purkinje cell layer of a young rat cerebellar cortex slice) of a linear 'Michigan' (now Neuronexus) probe and a loose cell-attached recording (data set Reference, in group 'CellAttached') from one of the Purkinje cells that is also extracellularly recorded: a 'ground truth' for spike sorting algorithms. Each group has three attributes: SamplingRate, HighPass and LowPass. The last two are the filter settings used prior to A/D conversion. These attributes have identical values for the 5 traces (2 groups): the data were sampled at 15 kHz, high-passed at 300 Hz and low-passed at 5 kHz.</p>
Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors: V4.NA.02.MAPLE
<p>We estimate ground-level fine particulate matter (PM<sub>2.5</sub>) total and compositional mass concentrations over North America by combining Aerosol Optical Depth (AOD) retrievals from the NASA MODIS, MISR, and SeaWIFS instruments with the GEOS-Chem chemical transport model, and subsequently calibrated to regional ground-based observations of both total and compositional mass using Geographically Weighted Regression (GWR) as detailed in the provided reference for V4.NA.02. V4.NA.02.MAPLE further modified the V4.NA.02 GWR method with additional developments as part of the MAPLE (Mortality–Air Pollution Associations in Low-Exposure Environments) project. This adjustment was of particular value over low concentrations. The GWR method of individual components remains unchanged from V4.NA.02, but are provided are percentages to ensure mass closure and recommended to be applied to the V4.NA.02.MAPLE total PM<sub>2.5</sub>.</p> <p>Annual datasets are provided in NetCDF [.nc]. Gridded files use the WGS84 projection. Compositional estimates are provided for sulfate (SO4), nitrate (NO3), ammonium (NH4), organic matter (OM), black carbon (BC), mineral dust (DUST), and sea-salt (SS). Percentages are denoted with a ‘p’ after component identifiers within filenames. A slight change in file name has been included for 2017, corresponding to minor internal changes compared to earlier years. Overall, however, the dataset is consistent throughout its entire time period and can be appropriately used for trend analysis.</p> <p><strong>Reference:</strong><br> van Donkelaar, A., R. V. Martin, et al. (2019). <strong>Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors.</strong> Environmental Science & Technology, 2019, doi:10.1021/acs.est.8b06392.</p>
Survey questions and raw data for the study in the paper "Educational Technology for Tutors – What are Useful Tools and Information?"
<p>The data include the questions data set, the answers dataset and the codebook for the questions conducted with soscisurvey (https://www.soscisurvey.de/de/index). The survey itself can be imported in soscisurvey (via the XML data) and reused.</p> <p>The answers are unedited.</p>
Input Dataset for Estimating Continuous Soil Water Retention Curves Using Physics-Informed Neural Networks
<p>This dataset was used as input to a physics-informed neural network (PINN) model developed to estimate continuous soil water retention curves (SWRCs). It includes basic soil properties such as particle-size distribution (sand, silt, clay), organic carbon content (OC), bulk density (BD), and measurements of soil water retention at various matric potentials. These inputs allow the model to learn the relationship between soil properties and water retention, via both data and embedded physical constraints. This data set consists of 4,200 Danish soil samples with measurements spanning the wet and dry ends of the SWRC. </p>
Dataset and supplemental codes for : "Referenceless characterisation of complex media using physics-informed neural networks"
<p>Dataset and associated supplemental codes for : "Referenceless characterisation of complex media using physics-informed neural networks".</p>
Phylogenomics of manakins (Aves: Pipridae) using alternative locus filtering strategies based on informativeness
<p>Data used in phylogenomic analyses of manakin birds. </p> <p>Datasets number 1 to 7 include sequence alignments for each locus analyzed, and datasets 4 to 7 also contain gene trees used as input for ASTRAL.</p>
Excavator-generated information from Linux drivers (Decoder Use-Case A)
<p>This dataset is released as part of DECODER's D6.2 deliverable. It contains the information generated by the Excavator tool for easing the verification with Frama-C of the watchdog and ethernet Linux drivers that have been selected as Use-Case A of the project.</p>
Dataset for "Development of allocentric representations using self-motion information"
<p>The present .csv file contains the raw data from a 2017 data collection. Specifically, children from six to 11 years old were tested with a non-visual spatial orientation task, in which they were required to i) observe animal-shaped landmarks located in each of the 4 angles of the experimental room; ii) be guided by the experimenter along a two-legged segment; iii) indicate the location of the four landmarks.</p>
Supporting Data: ontophylo: Reconstructing the evolutionary dynamics of phenomes using new ontology-informed phylogenetic methods
<p>This dataset contains all scripts and data for reproducing the analyses of the paper. The README files contain additional information.</p>
Twitter hashtags time series used in the paper "Universality, criticality and complexity of information propagation in social media"
<pre>These files contain the time series and the associated hashtags we obtained by sampling Twitter for our paper "Universality, criticality and complexity of information propagation on social media". The analysis is reported in <a href="https://arxiv.org/abs/2109.00116">https://www.nature.com/articles/s41467-022-28964-8</a> Please acknowledge the use of these data by citing the paper above. ################################# ################################# DATA ORGANIZATION We created a single zip file with all the time series and a single zip file with all the hashtags. There is a one-to-one correspondence between lines in the two files. ################################# ################################# FILES CONTENT As stated, here is a one-to-one correspondence between lines in the time series file and lines in the hashtags file, i.e., the hashtag stored in line X is the hashtag of the time series stored in line X. Time series are stored as follows: Ka t1 t2 t3 \n Kb t1 t2 t3 t4 t5 \n . . . Kn t1 t2 \n where: Ka, Kb,..., Kn is an integer specifying the number of events that compose the time series a, b,..., n respectively. In the example above we would have Ka=3, Kb=5, Kn=2. t1 t2 ... is the time series, i.e., a sequence of chronologically ordered interevent times. The last interevent time, in our implementation, represents the distance between the end of the temporal window and the last event time. It thus does not represent an event. As stated in the Supplemental Material of our paper, the temporal window ranges from 2019, October 1st to 2019, November 30th. </pre>
DrCyZ: Techniques for analyzing and extracting useful information from CyZ.
<p>DrCyZ: Techniques for analyzing and extracting useful information from CyZ.</p> <p>Samples from NASA Perseverance and set of GAN generated synthetic images from Neural Mars.</p> <p>Repository: <a href="https://github.com/decurtoidiaz/drcyz">https://github.com/decurtoidiaz/drcyz</a></p> <p><br> Subset of samples from (includes tools to visualize and analyse the dataset):</p> <p>CyZ: MARS Space Exploration Dataset. [<a href="https://doi.org/10.5281/zenodo.5655473">https://doi.org/10.5281/zenodo.5655473</a>]</p> <p>Images from NASA missions of the celestial body.</p> <p>Repository: <a href="https://github.com/decurtoidiaz/cyz">https://github.com/decurtoidiaz/cyz</a></p> <p>Authors:</p> <p>J. de Curtò c@decurto.be</p> <p>I. de Zarzà z@dezarza.be</p> <p>------------------------------------------<br> File Information from DrCyZ-1.1<br> ------------------------------------------</p> <p> • Subset of samples from Perseverance (drcyz/c).<br> ∙ png (drcyz/c/png).<br> PNG files (5025) selected from NASA Perseverance (CyZ-1.1) after t-SNE and K-means Clustering. <br> ∙ csv (drcyz/c/csv).<br> CSV file.</p> <p> • Resized samples from Perseverance (drcyz/c+).<br> ∙ png 64x64; 128x128; 256x256; 512x512; 1024x1024 (drcyz/c+/drcyz_64-1024).<br> PNG files resized at the corresponding size. <br> ∙ TFRecords 64x64; 128x128; 256x256; 512x512; 1024x1024 (drcyz/c+/tfr_drcyz_64-1024).<br> TFRecord resized at the corresponding size to import on Tensorflow.</p> <p> • Synthetic images from Neural Mars generated using Stylegan2-ada (drcyz/drcyz+).<br> ∙ png 100; 1000; 10000 (drcyz/drcyz+/drcyz_256_100-10000)<br> PNG files subset of 100, 1000 and 10000 at size 256x256.</p> <p> • Network Checkpoint from Stylegan2-ada trained at size 256x256 (drcyz/model_drcyz).<br> ∙ network-snapshot-000798-drcyz.pkl</p> <p> • Notebooks in python to analyse the original dataset and reproduce the experiments; K-means Clustering, t-SNE, PCA, synthetic generation using Stylegan2-ada and instance segmentation using Deeplab (<a href="https://github.com/decurtoidiaz/drcyz/tree/main/dr_cyz+">https://github.com/decurtoidiaz/drcyz/tree/main/dr_cyz+</a>).<br> ∙ clustering_curiosity_de_curto_and_de_zarza.ipynb<br> K-means Clustering and PCA(2) with images from Curiosity.<br> ∙ clustering_perseverance_de_curto_and_de_zarza.ipynb<br> K-means Clustering and PCA(2) with images from Perseverance.<br> ∙ tsne_curiosity_de_curto_and_de_zarza.ipynb<br> t-SNE and PCA (components selected to explain 99% of variance) with images from Curiosity.<br> ∙ tsne_perseverance_de_curto_and_de_zarza.ipynb<br> t-SNE and PCA (components selected to explain 99% of variance) with images from Perseverance.<br> ∙ Stylegan2-ada_de_curto_and_de_zarza.ipynb<br> Stylegan2-ada trained on a subset of images from NASA Perseverance (DrCyZ).<br> ∙ statistics_perseverance_de_curto_and_de_zarza.ipynb<br> Compute statistics from synthetic samples generated by Stylegan2-ada (DrCyZ) and images from NASA Perseverance (CyZ).<br> ∙ DeepLab_TFLite_ADE20k_de_curto_and_de_zarza.ipynb<br> Example of instance segmentation using Deeplab with a sample from NASA Perseverance (DrCyZ).</p>
Supporting data for "Estimating animal density for a community of species using information obtained only from camera-traps"
<p>Data underlying a paper published in Methods in Ecology and Evolution (<a href="https://doi.org/10.1111/2041-210X.13930">https://doi.org/10.1111/2041-210X.13930</a>).</p> <p>These data are suitable for estimating animal density using the Random Encounter Model and include: i) detection counts for 35 species across 510 camera-trap locations; ii) movement speeds (estimated by tracking animal movements in camera-trap image sequences), iii) activity times (filtered so that records of the same species at the same location are > 60 minutes apart), and iv) measurements of the angular and radial distance from camera-traps for animals that were detected.</p>
Alignment used in "A phylogenomically informed five-order system for the closest relatives of land plants"
<p>Alignment that served as the basis for the phylogenomic analyses presented in "A phylogenomically informed five-order system for the closest relatives of land plants" — preprint on bioRxiv doi: https://doi.org/10.1101/2022.07.06.499032</p>
Deep Deconvolution of Object Information Modulated by a Refractive Lens Using Lucy-Richardson-Rosen Algorithm
<p>A refractive lens is one of the simplest, cost-effective and easily available imaging elements. With a spatially incoherent illumination, a refractive lens can faithfully map every object point to an image point in the sensor plane, when the object and image distances satisfy the imaging conditions. However, static imaging is limited to the depth of focus, beyond which the point-to-point mapping can be only obtained by changing either the location of the lens or the imaging sensor. In this study, the depth of focus of a refractive lens in static mode has been expanded using a recently developed computational reconstruction method, Lucy-Richardson-Rosen algorithm (LRRA). The technique consists of three steps. In this first step, the point spread functions (PSFs) were recorded along different depths and stored in the computer as PSF library. In the next step, the object intensity distribution was recorded. The LRRA was then applied to deconvolve the object information from the recorded intensity distributions in the final step. The results of LRRA were compared against two well-known reconstruction methods namely Lucy-Richardson algorithm and non-linear reconstruction. The data corresponding to experimental analysis is given in the manuscript. (Preprints Link:). The theoretical simulation data is given here.</p>
MengxiaoZhao_Using sky-wave echoes information to extend HFSWR's maximum detection range
<p>1. for Figure 2,3,4</p> <p>a. 'Ground Attention.mat' —— The data of ground wave attenuation<br> [<br> f0 —— four frequency<br> L —— the ground distance<br> Attenuation: 501*4 —— the ground wave attenuation for four different frequency<br> ]</p> <p>b. 'Skywave Attention.mat' —— The data of skywave attenuation<br> [<br> f0 —— four frequency<br> L —— the ground distance<br> attenuation: 34*4 —— the skywave attenuation for four different frequency<br> ]</p> <p>c. 'Path attenuation of 5Mhz.mat' —— four paths' attenuation of 5Mhz<br> [<br> L —— Ground distance<br> path1 —— the attenuation of path 1<br> path23 —— the attenuation of path 2&3<br> path4 —— the attenuation of path4<br> ]</p> <p><br> 2. for Figure 7 - simulation result</p> <p>a. 'simulation_echoes data.mat' —— the data of simulation targets' echoes</p> <p>b. 'Figure7_data.mat' —— the simulation results<br> [<br> data —— Doppler*Range<br> Doppler —— the axis of Doppler <br> Range —— the axisof Range <br> ]</p> <p>3. for Figure 8,9,10 - actual data processing results. We give 5 batches of echoes' data and the final result of Figure 9.</p> <p>a. 'TCDat1.mat','TCDat2.mat','TCDat3.mat','TCDat4.mat','TCDat5.mat',<br> —— the echoes' data</p> <p>b. 'Figure9_data.mat' —— the final result of Figure 9. <br> [<br> data ——Doppler*Range<br> axist_Doppler —— the axisof Doppler<br> axist_Range —— the axisof Range<br> counT —— the number of detections<br> mTgt —— the parameters of detections<br> ]</p> <p>4. 'parameters.txt' —— the parameters of simulation data and actual data</p> <p><br> </p>
Catalog Data for Prior-Informed AGN-Host Spectral Decomposition Using PyQSOFit
<p>This catalog contains 76,565 AGN-host decomposed spectral measurements for all quasars with z<0.8 in SDSS DR16Q. Our prior-informed decomposition method significantly improved the decomposition success rate from less than 60% to 94%. For the first time, we perform the AGN-host spectral decomposition on survey scale catalog.</p> <p>Our spectral decomposition results are highly consistent to those of HSC image decomposition. Our catalog suggests that an average host galaxy contribution at 5100A is 38.8%, which would lead to an overestimation of 0.215 dex in L5100 and 0.219 dex in black hole mass if the host is not removed. The Dn4000 and stellar velocity dispersion measurements from the decomposed host galaxy spectra are also provided.</p> <p>Please read this paper for more techinique details: <a href="https://arxiv.org/abs/2406.17598">arXiv: 2406.17598</a></p>
Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values - Dataset
<p>This dataset accompanies the report <em>"Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values"</em>, which is available via Zenodo.<br><br>It provides record-level data of UKRI-funded and UK-affiliated research output (limited to journal articles with Crossref DOIs) published between 2012 and 2022 - including bibliographic metadata as well as data on open access availability, publisher, national and international collaborations, citations, views and downloads, altmetrics and subjects (fields). All variables are documented in the data dictionary included in this Zenodo record.</p> <p>The code used to generate the dataset from open data sources is available on GitHub. </p> <p>The following data sources were used:</p> <ul> <li> <p>Gateway to Research (records downloaded between 2023-11-05 and 2023-11-13)</p> </li> <li> <p>Crossref (Metadata Plus snaphot 2023-10-31, Crossref member route API 2024-01-23)</p> </li> <li> <p>OpenAlex (data snapshot 2023-10-18)</p> </li> <li> <p>Unpaywall (data snapshot 2023-11-27)</p> </li> <li> <p>IRUS UK (2024-04-03)</p> </li> <li> <p>Crossref Event Data (2023-04-01)</p> </li> </ul> <p><strong></strong><br><br>The project made use of Curtin Open Knowledge Initiative (COKI) infrastructure, which is documented on GitHub: <a href="https://github.com/The-Academic-Observatory">https://github.com/The-Academic-Observatory</a>. </p>
XRDs of Materials used in the Supplementary Information file of A. Lowe et al Exploring the Heat of Water Intrusion ... ACS Appl. Mater. Interfaces 2024, 16, 5286−5293
<p>Data plots were limited to 2theta range from 5 degrees to 50 degrees. CuKa</p>
IRIS preprocessed data used in paper "Multi variables time series information bottleneck"
<p>Prprocessed data used in paper "Multi variables time series information bottleneck" with the <a href="https://github.com/DenisUllmann/IB-MTS">GitHub</a> code</p> <p>This dataset is created from a public available dataset of observations performed by IRIS, a NASA small explorer mission developed and operated by LMSAL with mission operations executed at NASA Ames Research Center and major contributions to downlink communications funded by ESA and the Norwegian Space Centre.</p> <p>Multiple Time Series of IRIS level 2 data are available <a href="https://iris.lmsal.com/search/">here</a></p> <p>The selected data was labeled using these definitions:</p> <p>QS: Quiet Sun<br> AR: Active Regions of the Sun<br> FL: Flare</p> <p>A time series is labeled QS when every single time step refer to a quiet sun activity.<br> When a given time series is partially composed of flaring events, the global time series is labeled as FL.</p> <p>The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True<br> Loaded data is then a python dict described bellow.</p> <p>Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=240 representing a range between 2793.8401Å and 2806.02Å).</p> <p>Each sample is given a 'position' which is a list of length 4:<br> position[1] is a string that gives the name of the event<br> position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data</p> <p>Data file info :</p> <p>Type: .npz<br> Size: 11.89GB</p> <p>*** Key: 'data_TR_QS'<br> ndarray data of length 2467<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_TR_AR'<br> ndarray data of length 1042<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_TR_FL'<br> ndarray data of length 1055<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_VAL_QS'<br> ndarray data of length 325<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_VAL_AR'<br> ndarray data of length 1042<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_VAL_FL'<br> ndarray data of length 714<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_TE_QS'<br> ndarray data of length 1428<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_TE_AR'<br> ndarray data of length 792<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_TE_FL'<br> ndarray data of length 356<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_TR'<br> ndarray data of length 4564<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_VAL'<br> ndarray data of length 2081<br> containing np.ndarray of shapes ['various', 240]</p> <p><br> *** Key: 'data_TE'<br> ndarray data of length 2576<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'position_TR_QS'<br> ndarray data of length 2467<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TR_AR'<br> ndarray data of length 1042<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TR_FL'<br> ndarray data of length 1055<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_VAL_QS'<br> ndarray data of length 325<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_VAL_AR'<br> ndarray data of length 1042<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_VAL_FL'<br> ndarray data of length 714<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TE_QS'<br> ndarray data of length 1428<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TE_AR'<br> ndarray data of length 792<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TE_FL'<br> ndarray data of length 356<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TR'<br> ndarray data of length 4564<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_VAL'<br> ndarray data of length 2081<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TE'<br> ndarray data of length 2576<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</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.