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.
431
datasets available to search
ShareScore release 0.9.0
Dataset results
431 results for “Decoding”
Dataset for "From Halos to Galaxies. X: Decoding Galaxy SEDs with Physical Priors and Accurate Star Formation History Reconstruction"
<p>This deposit contains the data related to the manuscript "<em>From Halos to Galaxies. X: Decoding Galaxy SEDs with Physical Priors and Accurate Star Formation History Reconstruction</em>" submitted to the Astrophysical Journal. It includes the basic SDSS identifier, stellar mass, star formation rate, fractional formation time, and their errors. A detailed description can be found in Table 1 of the manuscript.</p> <p>The data is stored in a CSV file. It can be read with standard data analysis packages like <a href="https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html">Pandas</a> in Python. </p> <p>This deposit has also be updated to include a machine-readable table that follows the standards of the AAS Journals and Vizier (<span>datafile1_ApJ57534.mrt). More information on this standard can be found in the <a href="https://journals.aas.org/mrt-overview/">AAS</a> or <a href="http://cds.u-strasbg.fr/doc/catstd.htx">CDS</a> documentation. This format can be read in Python with packages like <a href="https://docs.astropy.org/en/stable/api/astropy.io.ascii.Mrt.html">astropy</a>. </span></p>
Neural structure of a sensory decoder for motor control
<p>Data associated with 'Neural structure of a sensory decoder for motor control.' Egger, SW and Lisberger, SG. <em>Nature Communications</em>, 2022.</p>
Data and Codes from: Decoding distributed oscillatory signals driven by memory and perception in the prefrontal cortex
<p>These datasets and codes were used for analysis in a manuscript titled "Decoding distributed oscillatory signals driven by memory and perception in the prefrontal cortex." For more details on the experimental and analysis methods, please refer to that manuscript. The raw data was obtained by recording signals from a 64-channel ECoG array implanted on the PFC of monkeys performing a behavioral task using custom software (NS computer service, Japan) running on LabVIEW Real-Time (National Instruments, TX, USA). The recorded raw data were converted into time-series data of power in six frequency bands using FieldTrip. The total file size of all the raw data is over 50GB, so it is not included here. It is available upon request.</p>
Upper limb movements can be decoded from the time-domain of low-frequency EEG
<p>How neural correlates of movements are represented in the human brain is of ongoing interest and has been researched with invasive and non-invasive methods. In this study, we analyzed the encoding of single upper limb movements in the time-domain of low-frequency electroencephalography (EEG) signals. Fifteen healthy subjects executed and imagined six different sustained upper limb movements. We classified these six movements and a rest class and obtained significant average classification accuracies of 55% (movement vs movement) and 87% (movement vs rest) for executed movements, and 27% and 73%, respectively, for imagined movements. Furthermore, we analyzed the classifier patterns in the source space and located the brain areas conveying discriminative movement information. The classifier patterns indicate that mainly premotor areas, primary motor cortex, somatosensory cortex and posterior parietal cortex convey discriminative movement information. The decoding of single upper limb movements is specially interesting in the context of a more natural non-invasive control of e.g., a motor neuroprosthesis or a robotic arm in highly motor disabled persons.</p>
DataSet & R code used for the analysis of "Mechanisms of mobbing call recognition: Exploring featural decoding in great tits"
<p>Data and R code used in a playback experiment exploring the mechanisms of mobbing call recognition in the great tit, Parus major. Accepted in Animal Behaviour (2024). </p> <p>This experiment aimed at testing the hypothesis of simple featural interpretation in the great tit (i.e., the fact that receivers can focus on specific acoustic features rather than complete note recognition). </p> <p>The experiment is organised with two parts: first, we test the response of great tits to artificial calls that possess either none or all of the characteristics present in their own calls (and shared with other Parids), and compare their level of response to natural mobbing calls. </p> <p>As the 'complete' treatment triggered the same level fo response than the natural calls, we then create artifical calls with only one of the four features used to create our artifical mobbing calls (large frequency range, low frequency, noise and harmonics). </p> <p> </p> <p>More information can be obtained by contacting Ambre SALIS (salis.ambre87[at]gmail.com)</p>
Figure 11: Coding and decoding Cartesian coordinates of geometrical points-Brain Functors: A mathematical model of intentional perception and action
<p>The simplest form of a brain "functor" is just a two-way representation or coding system that constructs and implements a set of codes. Given some set of objects, it is encoded using some isomorphic set of representations or codes for the objects, and then given an instance of the code, it is decoded to determine the object. Coordinatizing is a form of coding. The geometrical plane is a collection of points, and the Cartesian coordinate system represents each point P by a pair (xP, yP) of coordinates. Given a point P , the "coordinate" function selects the coordinates (xP, yP) of the point which is the recognized or coded output, and given the coordinates or code for a point (xP, yP) as an input, the "plot" function designates the point.</p>
Attempted Arm and Hand Movements can be Decoded from Low-Frequency EEG from Persons with Spinal Cord Injury
<p>We show that persons with spinal cord injury (SCI) retain decodable neural correlates of attempted arm and hand movements. We investigated hand open, palmar grasp, lateral grasp, pronation, and supination in 10 persons with cervical SCI. Discriminative movement information was provided by the time-domain of low-frequency electroencephalography (EEG) signals. Based on these signals, we obtained a maximum average classification accuracy of 45% (chance level was 20%) with respect to the five investigated classes. Pattern analysis indicates central motor areas as the origin of the discriminative signals. Furthermore, we introduce a proof-of-concept to classify movement attempts online in a closed loop, and tested it on a person with cervical SCI. We achieved here a modest classification performance of 68.4% with respect to palmar grasp vs hand open (chance level 50%).</p>
Decoding NY-ESO-1 TCR T Cells: Transcriptomic Insights Reveal Dual Mechanisms of Tumor Targeting in a Melanoma Murine Xenograft Model
<p><span>Single-cell RNA-seq data of NY-ESO-1-specific TCR T-cells generated with the BD Rhapsody™ system.</span></p> <p><span>Biogroup information: Control (<em>n</em><span> </span>= 4), PB (murine peripheral blood, <em>n</em><span> </span>= 4).</span></p> <p><span>Cell preparation: NY-ESO-1-specific TCR T-cells were obtained via a retroviral transduction of an anti-NY-ESO-1-TCR construct, murine peripheral blood T-cells were enriched using anti-CD3 magnetic separation via MojoSortTM Human CD3 Selection Kit.</span></p> <p><span>Single-cell analysis system: BD Rhapsody™</span></p> <p><span>Library strategy: 3' mRNA sequencing</span></p> <p><span>Library preparation protocol: BD Rhapsody™ Targeted mRNA and Sample Tag Library Preparation</span></p> <p><span>mRNA panel: BD Rhapsody™ Immune Response Panel HS</span></p> <p><span>BD Pipeline version: 1.11L</span></p>
Dataset related to article "Decoding distinctive features of plasma extracellular vesicles in amyotrophic lateral sclerosis"
<p>The mass spectrometry proteomics data have been deposited at the ProteomeXchange Consortium (<a href="http://proteomecentral.proteomexchange.org/cgi/GetDataset">http://proteomecentral.proteomexchange.org/cgi/GetDataset</a>) via the PRIDE partner repository with the data set identifier PXD020629.</p>
Data associated with "Decoding the hydrodynamic properties of microscale helical propellers from Brownian fluctuations"
<p>Deskewed data, thresholded data, and data analysis associated with https://arxiv.org/abs/2208.13854</p>
EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise'.
<p>The repository contains the unprocessed EEG data recorded for the publications [1, 2]. For convenience, the onsets of the EEG data provided here are time-aligned with the onsets of the audio books in the 'audiobooks' folder, and the EEG data are provided in HDF5 format. Please refer to the original version of this dataset for more details.</p> <p>More details, as well as the original data files, are available at the original repository <a href="https://doi.org/10.5281/zenodo.7086209">here</a>.</p> <p>Examples of using these data (preprocessing, fitting linear models) can be found <a href="https://github.com/Mike-boop/trf-examples">here</a>.</p> <p>The English conditions (clean, lb, mb, hb, fM, fW) comprised a single recording session. The Dutch conditions (cleanDutch, lbDutch, mbDutch, hbDutch) comprised a separate recording session. You see which participants took part in each session in session_info.json.</p> <p>Please note some details about the stimulus presentation for the various listening conditions:</p> <ul> <li>English speech-in-babble-noise (lb, mb, hb): babble noise was played by itself for one second before the audiobook track began. The babble noise was also played for one second after the audiobook track ended. Therefore, you should discard the first second and the last second from these trial during your analysis.</li> <li>Dutch speech-in-babble-noise (lbDutch, mbDutch, hbDutch): the story (narrated in Dutch) was played by itself for one second before the babble noise track began. Then, the babble noise was increased linearly in amplitude for one second. Therefore, you should discard the first two seconds from these trials during your analysis.</li> <li>Dutch in quiet, and Dutch-in-babble-noise (cleanDutch, lbDutch, mbDutch, hbDutch): some English sentences were embedded in the Dutch narratives in order to encourage attention. You should crop these from your analysis. The onsets and offsets of the English sentences (in samples, at 44100Hz) are provided in the audiobooks/*Dutch/english_onsets_info.json files.</li> <li>Competing-speakers conditions (fM, fW): sometimes the attended track is longer than the unattended track, or vice-versa. The onsets of both tracks are aligned. You should crop the trial to the length of the shortest track for your analysis.</li> </ul> <p>If you use this data, please cite the original publications, as well as this repository [1,2,3].</p> <p>[1] Etard O, Kegler M, Braiman C, Forte A E and Reichenbach T. “Decoding of selective attention to continuous speech from the human auditory brainstem response” 2019. <em>NeuroImage</em> <strong>200</strong> 1–11</p> <p>[2] Etard O and Reichenbach T. “Neural speech tracking in the theta and in the delta frequency band differentially encode clarity and comprehension of speech in noise” 2019. <em>J. Neurosci.</em> <strong>39</strong> 5750–9</p> <p>[3] Etard O and Reichenbach T. "EEG Dataset for 'Decoding of selective attention to continuous speech from the human auditory brainstem response' and 'Neural Speech Tracking in the Theta and in the Delta Frequency Band Differentially Encode Clarity and Comprehension of Speech in Noise". Doi: 10.5281/zenodo.7086208</p>
Annotation Data: Decoding the chromosome-scale genome of the nutrient-rich Agaricus subrufescens: A Resource for fungal biology and biotechnology
<p><strong>Decoding the chromosome-scale genome of the nutrient-rich Agaricus subrufescens: A Resource for fungal biology and biotechnology</strong></p> <p>Genome annotation data</p> <p><strong>Genome Browser:</strong> <a href="https://plantgenomics.ncc.unesp.br/gen.php?id=Asub">https://plantgenomics.ncc.unesp.br/gen.php?id=Asub</a></p>
Data from: Using adversarial networks to extend brain computer interface decoding accuracy over time
<p>Existing intracortical brain computer interfaces (iBCIs) transform neural activity into control signals capable of restoring movement to persons with paralysis. However, the accuracy of the "decoder" at the heart of the iBCI typically degrades over time due to turnover of recorded neurons. To compensate, decoders can be recalibrated, but this requires the user to spend extra time and effort to provide the necessary data, then learn the new dynamics. As the recorded neurons change, one can think of the underlying movement intent signal being expressed in changing coordinates. If a mapping can be computed between the different coordinate systems, it may be possible to stabilize the original decoder's mapping from brain to behavior without recalibration. We previously proposed a method based on Generalized Adversarial Networks (GANs), called "Adversarial Domain Adaptation Network" (ADAN), which aligns the distributions of latent signals within underlying low-dimensional neural manifolds. However, we tested ADAN on only a very limited dataset. Here we propose a method based on Cycle-Consistent Adversarial Networks (Cycle-GAN), which aligns the distributions of the full-dimensional neural recordings. We tested both Cycle-GAN and ADAN on data from multiple monkeys and behaviors and compared them to a third, quite different method based on Procrustes alignment of axes provided by factor analysis. All three methods are unsupervised and require little data, making them practical in real life. Overall, Cycle-GAN had the best performance and was easier to train and more robust than ADAN, making it ideal for stabilizing iBCI systems over time.</p>
Supplementary Data and Code: Change Point Analysis to decode Economic Crisis Information
<p>Raw data, results and Python code of the corresponding publication "Efficient Multi-Change Point Analysis to decode Economic Crisis Information from the S&P500 Mean Market Correlation" (accepted in: Entropy; Section: Complexity; Special Issue: Complexity in Finance). The change point analysis can be performed using the <a href="https://anticpy.readthedocs.io/en/latest/">documented</a> Python package <a href="https://github.com/MartinHessler/antiCPy"><em>antiCPy</em></a>. Some further helpful Python scripts are provided here under a <em>GNU General Public License v3.0.</em></p> <p>In Data_Generation you can find a list of</p> <ol> <li>S&P500 companies which are considered in the analysis,</li> <li>a jupyter notebook to create the correlation time series.</li> </ol> <p>Data_Preprocessing contains the</p> <ol> <li>S&P500 mean market correlation Financial_Time_Series_Centered_Interval__42days.csv,</li> <li>the Python code to thin it,</li> <li>the thinned data saved as .npy file,</li> <li>the time scale is saved <ul> <li>as integer numbers in thinned_time_thinning40.npy,</li> <li>as datetime in TimeScale_FinancialData.npy.</li> </ul> </li> </ol> <p>In Change_Point_Analysis you find the following files:</p> <ol> <li>cp_probs...npy contain the joint probabilities of the corresponding change point configurations (The joint probabilities are saved corresponding to the order in which Python's <em>itertools.combinations() </em>creates the configurations. This holds also for the cp_probs_5_cps.npy for which the whole combinations array is not saved for memory reasons),</li> <li>cp_pdfs...npy contain the marginal probability density functions of the ordinal change point positions averaged over all configurations,</li> <li>cp_configs...npy contain the configurations,</li> <li>segment_fit...npy contain the segment fit data,</li> <li>segment_fit_variance...npy contain corresponding variances,</li> <li>In the case of five change points only the plotted 1st, 13th and 26th most probable configuration in config_ranking_CP1_5.npy for memory reasons.</li> <li>the data and results of figure 3 for each crisis event can be found in the corresponding directory's folders: <ul> <li>blue corresponds to the pre-crisis data segments,</li> <li>green corresponds to the data segments up to the green vertical dotted line,</li> <li>red corresponds to the longest data segments incorporating near and in-crisis data.</li> </ul> </li> </ol> <p> </p> <p> </p>
Raw data of Decoding the state of stress and fluid pathways along the Andean Southern Volcanic Zone
<p>Raw data of the manuscript. </p> <p>Focal mechanisms and fault slip data of each volcano are attached to this supplementary material, in text files ending with “.fdt”, which can be found in the “Raw Data” folder. The fault slip data, on these text files, are write as strike/dip of the fault plane; trend/plunge of the rake, and the sense of displacement (N=normal R=Reverse), following the MIM-software input format. Note that by defining the displacement as normal o reverse, we are defining that the hanging wall block is going up or down, but it does not mean that the fault kinematics is restricted to normal or reverse. The fault kinematics can be dextral or sinistral strike-slip kinematics depending on the angle between the horizon and the rake projected in the fault plane. In the case of focal mechanisms, one focal mechanism is characterized by two rows of data that represent both focal plane solutions, following the MIM-software format. These “.fdt” files are ready to be analyzed in the MIM software that is freely available on the internet (at <a href="http://bs.kueps.kyoto-u.ac.jp/tsg/software/mim/">http://bs.kueps.kyoto-u.ac.jp/tsg/software/mim/</a>,visited august 2022).</p> <p>Complementarily, an explanation of data, its reference, spatial correlation with the volcano, and methodological details are explained in the excel table “Details_of_Database.xlsx”. Moreover, locations of the fault slip data and epicenter of focal mechanisms can be found in the attached folder “location of data” as Google Earth files (.kmz).</p>
Parallel window decoding enables scalable fault tolerant quantum computation
<p>Dataset containing raw data presented in the publication <em>"Parallel window decoding enables scalable fault tolerant quantum computation"</em> as well as the stim circuits used to sample circuit-level noise.</p> <p> </p>
Data from: A high-performance brain-computer interface for finger decoding and quadcopter game control in an individual with paralysis
Open the record for dataset details and reuse information.
Data from: Multi-gesture drag-and-drop decoding in a 2D iBCI control task
Open the record for dataset details and reuse information.
Data from: Using adversarial networks to extend brain computer interface decoding accuracy over time
Open the record for dataset details and reuse information.
Decoded telegrams of surface weather observations in the Southern Ocean on board the R/V Akademik Tryoshnikov during the Antarctic Circumnavigation Expedition (ACE) in the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains decoded reports of surface weather observations in the Southern Ocean made aboard the R/V Akademik Tryoshnikov during the Antarctic Circumnavigation Expedition (ACE), in the austral summer of 2016/2017. The observations were made by a meteorologist on board the ship and sent as telegrams encoded with World Meteorological Organisation (WMO) FM 13 code form. Posteriorly, the telegrams were decoded to produce this dataset.</p> <p>The telegrams were recorded on a 3-hourly basis and contain information about present and past weather; latitude, longitude, speed and direction of the ship; visibility; type, height and amount of clouds; speed and direction of wind; air, dew-point and sea-surface temperatures; barometric pressure; period, height and direction of waves; concentration of sea ice; etc. For more information on this type of report, please see the WMO Manual on Codes (WMO-No.306), Volume I.1, Part A. The original encoded files have been published separately (Veledin and Gorodetskaya, 2020; DOI 10.5281/zenodo.3734884).</p> <p>Data files have undergone no processing or quality-checking, therefore, if the variable you need is available in another ACE dataset, it is recommended to use the quality-checked data. Quality-checked meteorological data collected during ACE have been published separately (Landwehr et al., 2019; DOI 10.5281/zenodo.3379590).</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACE_telegram_decoded_YYYY_MM_DD_HHh.txt, data file, ASCII comma-separated</li> <li>README.txt, metadata, text file</li> <li>data_file_header.txt, metadata, text file</li> <li>Telegram_code_format.pdf, metadata, portable document format</li> </ul> <p><strong>Dataset license</strong></p> <p>This set of decoded meteorological telegrams are made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full description can be found at https://creativecommons.org/licenses/by/4.0/</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.