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4,694 results for “data analysis”
Forward entrainment in pitch discrimination (data analysis)
Open the record for dataset details and reuse information.
The sensory basis of schooling by intermittent swimming in the rummy-nose tetra (Hemigrammus rhodostomus) -- [Data analysis]
Open the record for dataset details and reuse information.
Data to support "Plastic Rivers project: Consumer-Based Actions to Reduce Plastic Pollution in Rivers: a Multi-Criteria Decision Analysis Approach"
Focusing on the most commonly occurring consumer plastic items present in European freshwater environments, we identified and evaluated consumer-based actions with respect to their direct or indirect potential to reduce macroplastic pollution in freshwater environments. As the main end users of these items, concerned consumers are faced with a bewildering array of choices to reduce their plastics footprint, notably through recycling or using reusable items. Using a Multi-Criteria Decision Analysis approach, we explored the effectiveness of 27 plastic reduction actions with respect to their feasibility, economic impacts, environmental impacts, unintended social/environmental impacts, potential scale of change and evidence of impact. Total action scores have been calculated using a multi-criteria decision analysis (MCDA) on 27 plastic reduction actions identified though a literature review. Each of the total action scores is the weighted sum of 1-5 scores (assigned to each of ten criteria to assess the positive environmental impact of each action, based on the literature review) and % weights of each criterion to rank their relative importance. We provide two tables in csv format: 1) the % weights assigned by 15 experts to rank the relative importance of socio-economic and environmental criteria to assess plastic reduction actions; 2) the 1-5 scores assigned to each of the ten criteria in relation to each of the 27 actions; the median weights for each of the ten criteria calculated from 1); and the total action scores of each of the 27 actions calculated as weighted sum (e.g. TAS action 1: sum of ten products of % criterion weight * 1-5 scores assigned to each combination criterion - plastic reduction action).
Data on weights and lengths from retrospective growth analysis of different stem age classes of Betula nana ramets from the Arctic LTER Nutrient and Warming manipulations in mosit acidic tussock tundra at 1995, Toolik Lake, AK.
This data file contains the data on weights and lengths from retrospective growth analysis of different stem age classes of Betula nana ramets from the Arctic LTER Nutrient and Warming manipulations in moist acidic tussock tundra at Toolik Lake.
Microsoft Academic data set for RPYS analysis
<p>Microsoft Academic data set for RPYS analysis, used in "Discovering seminal works with marker papers", see https://arxiv.org/abs/1901.07352</p> <p>Please see also:</p> <p>https://aka.ms/msracad</p> <p>Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, and Kuansan Wang.<br> 2015. An Overview of Microsoft Academic Service (MA) and Applications. In Proceedings of the 24th<br> International Conference on World Wide Web (WWW '15 Companion). ACM, New York, NY, USA,<br> 243-246. DOI=http://dx.doi.org/10.1145/2740908.2742839</p>
Training data for "Differential exon usage analysis"
<p>In RNA-Seq, we usually want to know the differentially expressed genes, as explained in several Galaxy Training Material tutorials. Sometimes,the question is more "which exons are differentially expressed". The process to identify differentially expressed exons is really similar to the one for differentially expressed genes.</p> <p>In this tutorial, we identify exons regulated by the <em>Pasilla</em> gene using RNA-Seq data from <a href="https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html#brooks2011conservation">Brooks <em>et al.</em> 2011</a>.</p>
InterFlex WP3 data set for scalability and replicability analysis of InterFlex demonstrators
<p>This set of data includes the data for the scalability and replicability task of InterFlex project under GA 731289 for the functional (grid operation) and ICT conducted analyses of the demonstrated use cases. The results and the detailed report can be found in deliverable D3.8</p>
The Qualitative Analysis of Repertory Grid Data: Interpretive Clustering (datasets only)
<p>Datasets accompanying the publication "The Qualitative Analysis of Repertory Grid Data: Interpretive Clustering" by </p> <p>Burr, King, and Heckmann.</p>
LogChunks: A Data Set for Build Log Analysis
<p>We collected 797 Travis CI logs from a wide range of 80 GitHub repositories from 29 different main development languages.<br> You can find our collection tool in `log-collection` and the logs sorted by language and repository in `logs`.</p> <p>We manually labeled the part (chunk) of the log describing why the build failed.In addition, the chunks are annotated with keywords that we would use to search for them and categorized according to their structural representation within the log.<br> You can find this data in an xml-file for each repository in `build-failure-reason`.</p>
Input data files for RSS-NET analysis of IBD GWAS summary statistics and NK cell regulatory network
<p>Details of these data files are provided in https://suwonglab.github.io/rss-net/ibd2015_nkcell.</p> <p>Contact:<code> xiangzhu[at]psu.edu </code></p>
data & analysis scripts of " Behavioral effects of rhythm, carrier frequency and temporal cueing on the perception of sound sequences"
<p>Analysis scripts and data accompanying the manuscript "Behavioral effects of rhythm, carrier frequency and temporal cueing on the perception of sound sequences"</p>
Data for "Dataset for Temporal Analysis of English-French Cognates"
<p>This is the data for the LREC 2020 paper "Dataset for Temporal Analysis of English-French Cognates". If you use this resource, please cite the paper:</p> <pre><code>@inproceedings{frossard2020dataset, title = "Dataset for Temporal Analysis of {E}nglish-{F}rench Cognates", author = {Frossard, Esteban and Coustaty, Micka\"el and Doucet, Antoine and Jatowt, Adam and Hengchen, Simon}, booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'20)", year = "2020", location = "Marseille" }</code></pre> <p> </p> <p>This work has been supported by the European Union Horizon 2020 research and innovation programme under grants 825153 (Embeddia) and 770299 (NewsEye).</p>
Data for tutorial of RNA interactome data analysis
<p>Data required for galaxy training tutorial of RNA interactome data analysis</p>
Data file: a qualitative social media content analysis of the Dutch #breakthesilence campaign on negative and traumatic experiences of labour and birth
<p>Data file with typed out quotes from the Dutch #breakthesilence campaign analysed for the study 'Left powerless: a qualitative social media content analysis of the Dutch #breakthesilence campaign on negative and traumatic experiences of labour and birth'.</p>
NanoGalaxy: Nanopore long-read sequencing data analysis in Galaxy
<p>The data presented in "NanoGalaxy: A Galaxy tool kit with workflows for third-generation sequence analysis" to illustrate the functionality of the tools was obtained from: Wick, Ryan R., et al. "Completing bacterial genome assemblies with multiplex MinION sequencing." <em>Microbial genomics</em> 3.10 (2017).</p> <p>+</p> <p>Li, Ruichao, et al. "Efficient generation of complete sequences of MDR-encoding plasmids by rapid assembly of MinION barcoding sequencing data." <em>Gigascience</em> 7.3 (2018): gix132.</p>
FIGURE 22 in Description of Pallisentis thapari n. sp. and a re-description of Acanthosentis seenghalae (Acanthocephala, Quadrigyridae, Pallisentinae) using morphological and molecular data, with analysis on the validity of the sub-genera of Pallisentis
FIGURE 22. Phylogenetic tree generated by Chaudhary et al. (2019) using maximum likelihood (ML) analysis of 18s rDNA sequence data of Pallisentis indica and related species. Tree modified from Chaudhary et al. (2019) to remove distance values and different branch lengths but retaining hypothesized phylogenetic relationships and to include the names of species associated with the sequences identified in the present work. Putative sub-genera (sensu Amin et al. 2000) indicated in color.
FIGURES 9–14 in Description of Pallisentis thapari n. sp. and a re-description of Acanthosentis seenghalae (Acanthocephala, Quadrigyridae, Pallisentinae) using morphological and molecular data, with analysis on the validity of the sub-genera of Pallisentis
FIGURES 9–14. Drawing of specimens of Acanthosentis seenghalae Chowhan, Gupta, Khera, 1988: 9. Male worm; 10. Proboscis; 11. Hooks of the proboscis; 12. Female worm; 13. Posterior end and gonopore; 14. Eggs. Scale bars: 9 = 1 mm; 10 and 12 = 500 µm; 11 = 20 µm; 13 = 200 µm; 14 = 100 µm.
Fine-grained automated visual analysis of herbarium specimens for phenological data extraction: an annotated dataset of reproductive organs in Strepanthus herbarium specimens
<p>This dataset contains annotations of 31 herbarium specimens of <em>Streptanhus tortuosus Kellogg</em> for which we have we carefully and manually drew and annotated the contours of four reproductive organs: “bud”, “flower”, “immature fruit” and “mature fruit”.</p> <p>The dataset can be used to assess the ability of automated methods to count and detect precisely the shapes of these reproductive organs, with a view to conducting phenological studies.</p> <p>The annotations are formatted in accordance with the COCO data format, a usual format for object detection tasks in the field of Computer Vision. The annotations are divided into two files:</p> <ul> <li>train_21_full_masks.json contains the mask coordinates and labels of 21 herbarium sheets that can be used for training models</li> <li>test_10_full_masks.json contains the mask coordinates and labels of 10 other herbarium that can be used as a groundtruth file for evaluating the predictions, typically with the COCO evaluation scripts (<a href="https://github.com/cocodataset/cocoapi">https://github.com/cocodataset/cocoapi</a>)</li> </ul> <p>Please refer to the following publication for a first assessment of this dataset with a Mask-RCNN approach:</p> <p><em>H. Goëau, A. Mora-Fallas, J. Champ, N. Love, S. Mazer, E. Mata-Montero, A. Joly, P. Bonnet. </em>2020. New fine-grained method for automated visual analysis of herbarium specimens: a case study for phenological data extraction. <em>Applications in Plant Sciences </em></p> <p> </p> <p> </p> <p> </p> <p> </p>
SCINDA GPS and UHF data supporting analysis in "On the assessment of daily Equatorial Plasma Bubble occurrence modeling and forecasting"
<p>This dataset consists of ionospheric scintillation data collected from August 1, 2013 until July 25, 2014 by a collection of GPS and UHF receiver stations in the Scintillation Network Decision Aid (SCINDA) network (Groves et al., 1997). This particular dataset supports the analysis conducted in Carter et al. (2020).</p> <p><br> Carter, B.A., J.L. Currie, T. Dao, E. Yizengaw, J.M. Retterer, M. Terkildsen, K. Groves and R. Caton (2020), On the assessment of daily Equatorial Plasma Bubble occurrence modeling and forecasting, Submitted to Space Weather, Jun 2020.</p> <p>Groves, K.M., S. Basu, E. J. Weber, M. Smitham, H. Kuenzler, C.E. Valladares, R. Sheehan, E. MacKenzie, J.A. Secan, P. Ning, W.J. McNeill, D.W. Moonan, and M.J. Kendra (1997), Equatorial scintillation and systems support, Radio Science, 32, 2047-2064, doi:10.1029/97RS00836.</p>
The WWU DUNEuro reference data set for combined EEG/MEG source analysis
<p>The provided dataset consists of two high-quality realistic head models and combined EEG/MEG data which can be used for state-of-the-art methods in brain research, such as modern finite element methods (FEM) to compute the EEG/MEG forward problems using the software toolbox DUNEuro (http://duneuro.org).</p> <p>A combined EEG/MEG dataset from a somatosensory experiment is provided (<strong>sep_sef.zip</strong>): Somatosensory evoked potentials (SEP) and fields (SEF) were elicited by stimulating the median nerve at the wrist of the right arm with monophasic square-wave electrical pulses with 0.5 ms duration. A random stimulus onset asynchrony between 350 and 450 ms was used and the strength was adjusted to invoke a clear movement of the thumb. The duration of the experiment was 10 minutes for a measurement of 1200 trials and data was acquired with a sampling rate of 1200 Hz and online low pass filtered at 300 Hz. An artifact reduction was achieved by reversing the polarity of the stimulation during the second half of the measurement. A 74-channel EEG (EASYCAP GmbH, Herrsching, Germany), for which the electrode positions were digitized using a Polhemus device (FASTRAK, Polhemus Incorporated, Colchester, Vermont, U.S.A.), and a whole-head MEG with 275 axial gradiometers and 29 reference coils (OMEGA2005, VSM MedTech Ltd., Canada) were used in the measurement.</p> <p>Ethics Statement: One healthy subject (49 years, male) participated in this study. The subject had no history of psychiatric or neurological disorders and had given written informed consent before the experiment. All procedures had been approved by the ethics committee of the University of Erlangen, Faculty of Medicine on 10.05.2011 (Ref. No. 4453).</p> <p>Additionally, two different advanced realistic head models are supplied, which both use a six-compartment segmentation from T1/T2-MRI of the test subject. They differentiate between scalp, skull compacta, skull spongiosa, cerebrospinal fluid (CSF) and gray and white matter tissue. One head model is a tetrahedral volumetric mesh (<strong>realistic_tet_mesh_6c.msh</strong>), while the other provides the geometric information by level-sets for each tissue boundary (<strong>realistic_levelsets_6c.zip</strong>). </p> <p>A detailed description of the construction of the tetrahedral mesh can be found <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.25272">here</a> (subsection 2.3), the main steps are presented in the following. First, the MR images were co-registered and resampled so that the voxels of the anatomical data are cubic. Furthermore, the images were cut sufficiently below the skull of the participant. Subsequently, the segmentation of the T1w and T2w was performed in order to create six volumetric masks representing the six tissue compartments. The brain compartment was segmented via the <a href="http://surfer.nmr.mgh.harvard.edu">FreeSurfer</a> software. The remaining preprocessing and creation of the volumetric masks was entirely performed via routines available in <a href="https://www.fieldtriptoolbox.org/">FieldTrip</a>. In particular, the scalp and skull segmentations were done via the <a href="https://www.fil.ion.ucl.ac.uk/spm/software/spm12/">spm12</a> software, embedded in FieldTrip. Once the masks were assembled, a volumetric tetrahedral mesh was created using the <a href="https://doc.cgal.org/Manual/3.5/doc_html/cgal_manual/Mesh_3/Chapter_main.html">CGAL</a> software embedded in <a href="http://iso2mesh.sourceforge.net/cgi-bin/index.cgi">iso2mesh</a>, resulting in 885,214 nodes and 5,335,615 tetrahedrons. The mesh is provided in <a href="https://gmsh.info">gmsh</a> format, including information about the node positions, elements defined by their node indices, and labels for each element indicating the tissue compartment.</p> <p>For the construction of the unfitted head model, a six-compartment voxel segmentation was constructed based on the T1- and T2-weighted MR images, distinguishing between skin, skull compacta and spongiosa, CSF, gray and white matter using <a href="https://www.fil.ion.ucl.ac.uk/spm/software/spm12/">SPM12</a> via <a href="https://www.fieldtriptoolbox.org/">Fieldtrip</a>, <a href="https://fsl.fmrib.ox.ac.uk/fsl">FSL</a> and internal MATLAB routines. Surfaces were extracted from this voxel segmentation to distinguish between the different tissue compartments. In order to smooth the surfaces while sustaining the available information from the segmentation, we applied an anti-aliasing algorithm created for binary voxel images presented in (<a href="https://doi.org/10.1145/353888.353893">Whitaker, 2000</a>). The resulting smoothed surfaces are represented as discrete level-set functions, i.e., by <span class="math-tex">\(N^3\)</span>-dimensional arrays (<span class="math-tex">\(N\)</span>=257), the value on each node indicates the signed distance to the respective surface.</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.