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.
3,684
datasets available to search
ShareScore release 0.9.0
Dataset results
3,684 results for “extract”
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 4b. The EMD decomposition results for subject 2 when he imagines left hand movement
<p>d et al., 2011). Fig. 4 shows the EMD decomposition result of one-trial (left hand movement imagination) for subject 2 in the channels C3 and C4 respectively (the pre-filtered EEG signal used for this illustration is not corrupted by blinking artifact.). Each channel is decomposed into ten IMFs and one residue.</p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 6. The general conception of our asynchronous system BCI (offline - online) for reinforcement of a joystick movement
<p>Once the motor imagery is identified, a command may be associated to this mental task in order to control a machine (Prataksita et al., (2014)) (Guger et al., 1999). In this work, we constructed a new Simuhnk/MathWork model to translate on-line the EEG signals into low-level commands. Fig. 6 shows our experimental EEG-based BCI System </p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 1. General architecture of an online (BCI)
<p>One major challenge of our BCI system is to describe the signals EEG by a few relevant values called features i.e. step 3 in Fig (1). The success of the mental imagery classification depends on the choice of features used to characterize the raw EEG signals. These features can then be used in step 4 in order to classify the user’s mental state. Several approaches for feature extraction have been proposed in literature. </p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 3. Hybrid EMD-BP approach for one trail feature extraction
<p>In this work, we propose a direct nonlinear approach to extract the more relevant IMFs corresponding to the different frequency components in the and bands and then obtain the BP in order to use them as features for mental task classification (see Fig. 3). The feature vector pi used for the demonstration in this paper is composed, for each sample I, 1 < i < 2048, in a given trial (among a total of 160 trials) of four bandpower, calculated of the rhythms and in positions C3 and C4 (Trad et al., 2011).</p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 2. Timing of one trial of the experiment with continuous feedback (Guger et al, 2001)
<p>At the beginning of each trial (t = 0 s), a fixation cross appeared on the black screen. After two seconds a warning stimulus was given in the form of a beep. From 3 to 4.25s, an arrow (cue stimulus), pointing to the left or right, was shown on the screen. The subject was instructed to imagine a left or right hand movement until the end of the trial, depending on the direction of the arrow. The EEG was sampled and classified on line throughout the session. Between 4.25 and 8s, the classification result was used to give a continuously updated feedback stimulus in the form of a horizontal bar that appeared in the center of the screen. The paradigm is illustrated in fig (2).</p>
BRAIN Journal-Sentiment Analysis on Embedded Systems Blended Courses-Figure 3. Sentiment analysis on extracted themes
<p>Figure 3 is presenting the sentiment analysis results from the point of view of the themes extracted from the corpus. The same preoccupation for the cost of the course is revealed, but this time the fact that MOOCs are free is appreciated. Students perceive that an integration of MOOCs into blended courses leads to a rapid information of the topics, such a feature receiving a high positive score of +3.46. The detailed explanations in this blended approach received a positive impact from the students with a total score of +2.70, but also the gained knowledge is among the most highly rated corpus themes. </p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 2. A Sample Image from extracted feature
<p>Using these data, it creates a new picture and uses these data as a starting point for drawing. By using the data, it gets a model and shape of face without color and facial expression (Gourier et al.; 2004), such as Figure 2. It got a model of faces using these features. </p>
Quantifying biostabilisation effects of biofilm-secreted and extracted extracellular polymeric substances (EPS) on sandy substrate
<p>This dataset provides the data underlying the manuscript <em>Quantifying biostabilisation effects of biofilm-secreted and extracted extracellular polymeric substances (EPS) on sandy substrate </em>published in Earth Surface Dynamics - an interactive open-access journal of the European Geosciences Union.</p> <p>The work was conducted in the Total Environment Simulator at the University of Hull as part of Task 9.3 of the Hydralab+ inititiative. </p> <p>Microbial assemblages ('biofilms') preferentially develop at water-sediment interfaces and are known to have a considerable influence on sediment stability and erodibility. There is potential for significant impacts on sediment transport and morphodynamics and, hence, on the longer-term evolution of coastal and fluvial environments. However, the biostabilisation effects remain poorly understood and quantified due to the inherent complexity of biofilms and the large spatial and temporal (i.e. seasonality) variations involved. Here, we use controlled laboratory tests to systematically quantify the effects of natural biofilm colonisation as well as extracted extracellular polymeric substances (EPS) on sediment stability. Extracted EPS may be useful to simulate biofilm-mediated biostabilisation, and potentially provides a method of speeding up time scales of physical modelling experiments investigating biostabilisation effects. We find a mean biostabilisation effect due to natural biofilm colonisation and development of almost four times that of the uncolonised sand. The presented cumulative probability distribution of measured critical threshold for erosion of colonised sand reflects the large spatial and temporal variations generally seen in natural biostabilised environments. For identical sand, engineered sediment stability from the addition of extracted EPS compares well across the measured range of the critical threshold for erosion and behaves in a linear and predictable fashion. Yet, the effectiveness of extracted EPS to stabilise sediment is sensitive to the preparation procedure, time after application and environmental conditions such as salinity, pH and temperature. These findings are expected to improve bio-physical experimental models in fluvial and coastal environments and provide much-needed quantification of biostabilisation to improve predictions of sediment dynamics in aquatic ecosystems.</p>
BRAIN Journal-A Robust Approach of Facial Orientation Recognition from Facial Features-Figure 2. A Sample Image from extracted feature
<p>Using these data, it creates a new picture and uses these data as a starting point for drawing. By using the data, it gets a model and shape of face without color and facial expression (Gourier et al.; 2004), such as Figure 2. It got a model of faces using these features.</p>
The BigGrams: the semi-supervised information extraction system from HTML: an improvement in the wrapper induction - dataset
<p><strong>Brief description</strong></p> <p>The zip file contains two folders. The <strong>"websites"</strong> folder includes crawled web pages from real websites, like a agatameble.pl (an e-shop website), filmweb.pl (a website about films), and ptaki.info (a website about birds). The <strong>"reference-seeds"</strong> folder contains three subfolders, i.e. agatameble.pl, filmweb.pl, and ptaki.info. Each subfolder contains reference-seeds.csv file. The file contains data, i.e. reference instances - carefully labelled ground-truth of corresponding values in each web page of given websites mentioned above.</p> <p><strong>Reference</strong></p> <p>I would appreciate it if you cite the following paper when using the dataset:</p> <p>Marcin Mirończuk The BigGrams: the semi-supervised information extraction system from HTML: an improvement in the wrapper induction, Knowledge and Information Systems, Volume 54, Issue 3, p. 711–776, 2018, (pdf Open Access – http://rdcu.be/u88F lub DOI http://dx.doi.org/10.1007/s10115-017-1097-2)</p>
Compound profiling matrices extracted from screening data
<p>Compound profiling matrices record assay results for compound libraries tested against panels of targets. In addition to their relevance for exploring structure-activity relationships, such matrices are of considerable interest for chemoinformatic and chemogenomic applications. For example, profiling matrices provide a valuable data resource for the development and evaluation of machine learning approaches for multi-task activity prediction. However, experimental compound profiling matrices are rare in the public domain. Although they are generated in pharmaceutical settings, they are typically not disclosed. Herein, we present an algorithm for the generation of large profiling matrices, for example, containing more than 100,000 compounds exhaustively tested against 50 to 100 targets. The new methodology is a variant of bi-clustering algorithms originally introduced for large-scale analysis of genomics data. Our approach is applied here to assays from the PubChem BioAssay database and generates profiling matrices of increasing assay or compound coverage by iterative removal of entities that limit coverage. Weight settings control final matrix size by preferentially retaining assays or compounds. In addition, the methodology can also be applied to generate matrices enriched with active entries representing above-average assay hit rates.</p>
Deprecated - VAMDC extraction with query token = vald:1e937aca-42f7-403c-8ea3-5a97df96f628:get
<p>Deprecated because the file upload was corrupted</p> <p>This dataset comes from the VAMDC(Virtual Atomic and Molecular Data Center) node named http://vald.astro.uu.se/atoms-12.07/tap/ by doing these queries: (query=select * where ( atomsymbol = 'h' ); ) . The corresponding dataset is versioned on 2018-04-04 with the XSAMS 12.07 format. This data is associated with a unique identifier uuid=1ce2c1b9-f4dd-493c-ae3a-a36a9a32cde9. Bibliographic references are: N.N. (2018). {Murphy}, P.~W. (1968). {Transition Probabilities in the Spectra of Ne I, Ar I, and Kr I}. Journal of the Optical Society of America (1917-1983). {Redfors}, A. (1991). {Oscillator strengths for Y III and Zr III in the IUE region}. aap.</p>
Deprecated - VAMDC extraction with query token = vald:bff5bd96-aefe-46b2-85ed-f18fd6f90161:get
<p>Deprecated because the file upload was corrupted.</p> <p>This dataset comes from the VAMDC(Virtual Atomic and Molecular Data Center) node named http://vald.astro.uu.se/atoms-12.07/tap/ by doing these queries: (query=select * where ( IonCharge = '1' ) ) . The corresponding dataset is versioned on 2018-04-04 with the XSAMS 12.07 format. This data is associated with a unique identifier uuid=6609d3b9-0ecd-4cda-9693-8aed0f044d7c. Bibliographic references are: N.N. (2018). {Bridges}, J.~M. (1973). {Arc measurements of Fe II oscillator strengths}. {Ekberg}, J.~O. (1997). {Extended analysis of doubly ionized chromium, Cr III}. physscr. 10.1088/0031-8949/56/2/003 {Kurucz}, R.~L. (1975). None. {Murphy}, P.~W. (1968). {Transition Probabilities in the Spectra of Ne I, Ar I, and Kr I}. Journal of the Optical Society of America (1917-1983). {Redfors}, A. (1991). {Oscillator strengths for Y III and Zr III in the IUE region}. aap. {Smith}, G. (1988). {Oscillator strengths for neutral calcium lines of 2.9 eV excitation}. Journal of Physics B Atomic Molecular Physics. 10.1088/0953-4075/21/16/008 {Theodosiou}, C.~E. (1989). Accurate calculation of the 4p lifetimes of Ca^{ + }. Physical Review A. 10.1103/PhysRevA.39.4880</p>
Deprecated - VAMDC extraction with query token = topbase:9eff7c45-0683-4c41-a74c-15c1685464f8:head
<p>Deprecated because the file upload was corrupted.</p> <p>This dataset comes from the VAMDC(Virtual Atomic and Molecular Data Center) node named http://topbase.obspm.fr/12.07/vamdc/tap/ by doing these queries: (query=select * where ( atomsymbol = 'li' ); ) . The corresponding dataset is versioned on 2017-01-30 with the XSAMS 12.07 format. This data is associated with a unique identifier uuid=5ab8c38a-bdad-4c38-be72-40cbcf015797. Bibliographic references are: N.N. (2018). Peach, G. and Saraph, H. E. and Seaton, M. J. (1988). Atomic data for opacity calculations. IX. The lithium isoelectronic sequence. Journal of Physics B Atomic Molecular Physics. Fernley, J. A. and Seaton, M. J. and Taylor, K. T. (1987). Atomic data for opacity calculations. VII - Energy levels, f values and photoionisation cross sections for He-like ions. Journal of Physics B Atomic Molecular Physics. Seaton, M. J. (1995). . unpublished.</p>
Deprecated - VAMDC extraction with query token = topbase:f5ca4588-1814-427c-b417-040f7fe09029:get
<p>Deprecated because the file upload was corrupted.</p> <p>This dataset comes from the VAMDC(Virtual Atomic and Molecular Data Center) node named http://topbase.obspm.fr/12.07/vamdc/tap/ by doing these queries: (query=select * where ( IonCharge = '1' ) ) . The corresponding dataset is versioned on 2017-01-30 with the XSAMS 12.07 format. This data is associated with a unique identifier uuid=b540b211-ea04-4092-8375-d55fd068f8c7. Bibliographic references are: N.N. (2018). Fernley, J. A. and Hibbert, A. and Kingston, A. E. and Seaton, M. J. (1999). Atomic data for opacity calculations: XXIV. The boron-like sequence. Journal of Physics B Atomic Molecular Physics. Mendoza, C. and Eissner, W. and LeDourneuf, M. and Zeippen, C. J. (1995). Atomic data for opacity calculations. XXIII. The aluminium isoelectronic sequence. Journal of Physics B Atomic Molecular Physics. Hibbert, A. and Scott, M. P. (1994). Atomic data for opacity calculations. XXI. The neon sequence. Journal of Physics B Atomic Molecular Physics. Butler, K. and Mendoza, C. and Zeippen, C. J. (1993). Atomic data for opacity calculations. XIX. The magnesium isoelectronic sequence. Journal of Physics B Atomic Molecular Physics. Tully, John A. and Seaton, Michael J. and Berrington, Keith A. (1990). Atomic data for opacity calculations. XIV - The beryllium sequence. Journal of Physics B Atomic Molecular Physics. Luo, D. and Pradhan, A. K. (1989). Atomic data for opacity calculations. XI - The carbon isoelectronic sequence. Journal of Physics B Atomic Molecular Physics. Luo, D. and Pradhan, A. K. and Saraph, H. E. and Storey, P. J. and Yu, Yan (1989). Atomic data for opacity calculations. X - Oscillator strengths and photoionisation cross sections for O III. Journal of Physics B Atomic Molecular Physics. Peach, G. and Saraph, H. E. and Seaton, M. J. (1988). Atomic data for opacity calculations. IX. The lithium isoelectronic sequence. Journal of Physics B Atomic Molecular Physics. Fernley, J. A. and Seaton, M. J. and Taylor, K. T. (1987). Atomic data for opacity calculations. VII - Energy levels, f values and photoionisation cross sections for He-like ions. Journal of Physics B Atomic Molecular Physics. Seaton, M. J. (1995). . unpublished. Burke, V. M. and Lennon, D. J. (1995). . unpublished. Butler, K. and Zeippen, C.J. (1995). . unpublished. Butler, K. and Zeippen, C.J. (1995). . unpublished. Taylor, K.T. (1995). . unpublished. Butler, K. and Mendoza, C. and Zeippen, C.J. (1995). . unpublished. Storey, P. J. and Taylor, K. T. (1995). . unpublished. Saraph, H. E. and Storey, P. J. (1995). . unpublished.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 9. Resized image and extracted data for a 100*100 size image
<p>For the extraction, our goal is to divide an image into smaller blocks and keep the row and column data for these blocks. But for our experiment we used a single block, which means taking the full image as a single block. For bigger image we should always divide the image in separate blocks and work on them par rally. As in figure 8, after extracting the data we can add the row and column bits information in the resized image or saved in a separate file. For proof of concept we saved it in a text file. And later that file is used to feed GA to make the fitness function, in figure 9 an extraction has been shown. In upper and side textbox containing the information which later is saved in a text file.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 8. Sample Data Extraction for a 20*20 size image
<p>As in figure 7 we are storing the extra data which is look like figure 8. Where a 20*20 size image of alphabet ‘A’ data has been stored. When we regenerate image, we are using these data.</p>
Deprecated - VAMDC extraction with query token = vald:8402ed6f-b887-4dfb-a5a5-c02c4e3c0ca0:get
<p>Deprecated because the file upload was corrupted.</p> <p>This dataset comes from the VAMDC(Virtual Atomic and Molecular Data Center) node named http://vald.astro.uu.se/atoms-12.07/tap/ by doing these queries: (query=select * where ( atomsymbol = 'he' and radtranswavelength < 600 and radtranswavelength > 500 ); ) . The corresponding dataset is versioned on 2018-04-04 with the XSAMS 12.07 format. This data is associated with a unique identifier uuid=955c9268-d40a-4427-9bea-7b715f978769. Bibliographic references are: N.N. (2018). {Kurucz}, R.~L. (1975). None. {Smith}, G. (1988). {Oscillator strengths for neutral calcium lines of 2.9 eV excitation}. Journal of Physics B Atomic Molecular Physics. 10.1088/0953-4075/21/16/008</p>
Deprecated - VAMDC extraction with query token = starkb:64abe979-6ff1-47ef-bb80-fca200635ae8:head
<p>Deprecated because the file upload was corrupted.</p> <p>This dataset comes from the VAMDC(Virtual Atomic and Molecular Data Center) node named http://stark-b.obspm.fr/12.07/vamdc/tap/ by doing these queries: (query=select * where ( atomsymbol = 'mg' and ioncharge = 0 and radtranswavelength <= 5200 and radtranswavelength >= 5100 ); ) . The corresponding dataset is versioned on 2017-06-23 with the XSAMS 12.07 format. This data is associated with a unique identifier uuid=60ccb3ad-4411-43d1-badd-5e09ee659f39. Bibliographic references are: N.N. (2018). Dimitrijević M.S. and Sahal-Bréchot S. (1994). Stark broadening parameters tables for Mg I lines of interest for Solar and Stellar spectra research. I. Bull. Astron. Belgrade. Dimitrijević M.S. and Sahal-Bréchot S. (1994). Stark broadening parameters tables for Mg I lines of interest for Solar and Stellar spectra research. II. Bull. Astron. Belgrade. Dimitrijević M.S. and Sahal-Bréchot S. (1996). Stark broadening of Mg I solar lines. A&AS. Dimitrijević M.S. and Sahal-Bréchot S. (1998). Electron-impact broadening of Mg II spectral lines for astrophysical and laboratory plasma research. Phys.Scr.. Dimitrijević M. S. and Sahal-Bréchot S. (1994). Stark Broadening Parameter Tables for MG I Lines of Interest for Solar and Stellar Spectra Research. I. Bull. Obs. Astron. Belgrade.</p>
Deprecated - VAMDC extraction with query token = gecasda:d9e14463-b0d6-43a7-98fc-633a5f9df75d:head
<p>Deprecated because the file upload was corrupted.</p> <p>This dataset comes from the VAMDC(Virtual Atomic and Molecular Data Center) node named http://vamdc.icb.cnrs.fr/gecasda/tap/ by doing these queries: (query=select * where ( inchikey in ['quzpnffhzprkjd-aklpvkdbsa-n', 'quzpnffhzprkjd-bjudxgsmsa-n', 'quzpnffhzprkjd-igmarmgpsa-n', 'quzpnffhzprkjd-oiobtwansa-n', 'quzpnffhzprkjd-oubtzvsysa-n'] ); ) . The corresponding dataset is versioned on 1 with the XSAMS 12.07 format. This data is associated with a unique identifier uuid=5c91e7c7-e0c9-474f-b76a-62bff7b38468. Bibliographic references are: N.N. (2018). Boudon,V. and Grigoryan,T. and Philipot,F and Richard,C and Kwabia Tchana,F and Manceron,L. and Rizopoulos,A and Vander Auwera,J and Encrenaz,T (2018). Line positions and intensities for the $ u_3$ band of 5 isotopologues of germane for planetary applications. Journal of Quantitative Spectroscopy and Radiative Transfer.</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.