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6,467 results for “process”
SARWIND LG-Mod (v4.01) output data with reference to the Sentinel-1A (2014/12/31, Camargue), EW, GRD, MR, VV-polarized, descending orbit, SNAP pre-processed data (+ the additional SKYRON reference wind)
<p>A zipped folder containing SARWIND LG-Mod (v4.01) output data obtained by processing the Sentinel-1A (2014/12/31, Camargue), EW, GRD, MR, VV-polarized, descending orbit, SNAP pre-processed data (+ the additional SKYRON reference wind).</p> <p>Pre-processed data derived from the Sentinel-1 SAR image have been obtained by using the Sentinel Application Platform (SNAP), ver. 3.0.3, distributed by the European Space Agency (ESA). The reference wind components have been provided by the Numerical Weather Model (NWM) SKYRON.</p> <p>These Earth Observation (EO) data have been used as inputs to the SARWIND LGMod algorithm, ver. 4.01, developed by Rana Fabio Michele (2014-2016) for the sea surface wind field retrieval.</p> <p>The available Sentinel-1, Extra Wide Swath Mode, Ground Range, Multi-look, Detected, Medium Resolution, Vertical polarisation on transmit, Vertical polarisation on receive, Descending orbit, C-band SAR image, has been acquired on the 31st Dec 2014, 05:51:53.</p>
Robot Self-Assembly as Adaptive Growth Process
<p>Autonomous self-assembly allows to create structures and scaffolds on demand and automatically. The desired structure may be predetermined or alternatively it is the result of an artificial growth process that adapts to environmental features and to the intermediate structure itself. In a self-organizing and decentralized control approach the robots interact only locally and form the structure collectively. Designing a complete approach that allows the robot group to collectively decide on where to start the self-assembly, that adapts at runtime to environmental conditions, and that guarantees the structural stability is challenging and does not yet exist. We present an approach to self-assembly inspired by diffusion-limited aggregation that generates an adaptive structure reacting to environmental conditions in an artificial growth process. During a preparatory stage the robots collectively decide where to start the self-assembly also depending on environmental conditions.In the actual self-assembly stage, the robots create tree-like structures that grow towards light. We report the results of robot self-assembly experiments with 50 Kilobots. Our results demonstrate how an adaptive growth process can be implemented in robots. We explain how our approach will be extended to a 3-d growth process and how robot self-assembly as an open-ended adaptive growth process opens up a multiplicity of future opportunities.</p>
Visual Perception of Complex Shape-Transforming Processes
<p>Dataset relative to the following publication:</p> <p>Schmidt, F., & Fleming, R. W. (2016). Visual Perception of Complex Shape-Transforming Processes. <em>Cognitive Psychology, 90</em>, 48-70. <a href="http://dx.doi.org/10.1016/j.cogpsych.2016.08.002"> http://dx.doi.org/10.1016/j.cogpsych.2016.08.002</a></p> <p>Each folder contains the stimuli and data relative to one experiment and a text file with comments.</p>
Data for Gaussian-Process-Based Emulators for Building Performance Simulation
<p>The ZIP folder contains the MAT files you need to rerun the experiment described in</p> <p>Rastogi, Parag, Mohammad Emtiyaz Khan, and Marilyne Andersen. 2017. “<strong>Gaussian-Process-Based Emulators for Building Performance Simulation</strong>.” In <em>Proceedings of BS 2017</em>. San Francisco, CA, USA: IBPSA.</p> <p>-------------------------------------------------------------</p> <p>The two m-scripts (MATLAB) help you to load the results reported in the paper. Make sure to CHECK the file paths inside the scripts, especially to the MAT files. Usually, the paths should be fine if you update the variable <em>pathMATfolder</em> inside the script <em>RunThis.m</em> .</p> <p>There are two types of MAT files inside the folder called "Data" :</p> <p>1. Original data (building simulations) --> gpdata_BaseSimulation.mat</p> <p>2. Errors and predictions - errs_BaseSimulation_N_M.mat and ystore_BaseSimulation_N_M.mat --> The first contains all the error quantities and the second the 'y' predictions. The number N represents the run number (subset of master training data set sampled for the given run). The number M can take only two values - 1 or 2. The models for heating load are represented by 1 and for cooling by 2.</p> <p>3. Metadata - trainN* --> These files contain metadata for setting up the plots.</p> <p>See github repository <strong>https://github.com/paragrastogi/GPregressionInBS.git</strong> for more scripts. See <strong>www.paragrastogi.com</strong> or <strong>www.ibpsa.org</strong> for the conference paper.</p>
Figure 8. - Metasternal process (in the circle) and aedeagi of Dicronocephalusadamsidrumonti and Dicronocephalusadamsiadamsi. A, B, C, D Dicronocephalusadamsidrumonti (Tibet) E, F, G, H Dicronocephalusadamsidrumonti (Sichuan) I, J, K, L Dicronocephalusadamsiadamsi (South Korea) M, N, O, P Dicronocephalusadamsiadamsi (North Korea) Q, R, S, T Dicronocephalusadamsiadamsi (Dandong, China).
Figure 8. - Metasternal process (in the circle) and aedeagi of Dicronocephalusadamsidrumonti and Dicronocephalusadamsiadamsi. A, B, C, D Dicronocephalusadamsidrumonti (Tibet) E, F, G, H Dicronocephalusadamsidrumonti (Sichuan) I, J, K, L Dicronocephalusadamsiadamsi (South Korea) M, N, O, P Dicronocephalusadamsiadamsi (North Korea) Q, R, S, T Dicronocephalusadamsiadamsi (Dandong, China).
Quasi-periodic Gaussian process rotation period posterior samples of Kepler light curves.
<p>.h5 files containing light curves, hyperparameter priors, period priors and posterior samples for the QP-GP rotation period model in Angus, Morton, Aigrain, Foreman-Mackey & Rajpaul (2017). </p> <p>koi_results_02_15.tgz contains posterior samples for 1132 Kepler objects of interest.</p> <p>results_acfprior_02_16.tgz contains posterior samples for 998 simulated light curves (previously published in Aigrain et al., 2015: https://arxiv.org/abs/1504.04029) with an ACF-based prior.</p> <p>results_noprior_02_16.tgz contains posterior samples for 997 simulated light curves (previously published in Aigrain et al., 2015) with an uninformative prior.</p> <p>To load the samples in python:</p> <p>>>> df = pd.read_hdf("<filename>", key="samples")</p> <p>>>> ln_period_samples = df.ln_period</p> <p>>>> ln_A_samples = df.ln_A</p> <p>>>> ln_l_samples = df.ln_l</p> <p>>>> ln_G_samples = df.ln_G</p> <p>>>> ln_sigma_samples = df.ln_sigma</p> <p> </p>
Quasi-periodic Gaussian process rotation period posterior samples (with an uninformative prior) for 998 simulated Kepler light curves.
<p>997.h5 files containing light curves, hyperparameter priors, period priors and posterior samples for the QP-GP rotation period model in Angus, Morton, Aigrain, Foreman-Mackey & Rajpaul (2017).</p> <p>997 .png light curve images.</p> <p>To load the samples in python:</p> <p>>>> df = pd.read_hdf("1.h5", key="samples")</p> <p>>>> ln_period_samples = df.ln_period</p> <p>>>> ln_A_samples = df.ln_A</p> <p>>>> ln_l_samples = df.ln_l</p> <p>>>> ln_G_samples = df.ln_G</p> <p>>>> ln_sigma_samples = df.ln_sigma</p> <p> </p>
Quasi-periodic Gaussian process rotation period posterior samples (with an ACF prior) for 998 simulated Kepler light curves.
<p>998.h5 files containing light curves, hyperparameter priors, period priors and posterior samples for the QP-GP rotation period model in Angus, Morton, Aigrain, Foreman-Mackey & Rajpaul (2017).</p> <p>998 .png light curve images.</p> <p>To load the samples in python:</p> <p>>>> df = pd.read_hdf("1.h5", key="samples")</p> <p>>>> ln_period_samples = df.ln_period</p> <p>>>> ln_A_samples = df.ln_A</p> <p>>>> ln_l_samples = df.ln_l</p> <p>>>> ln_G_samples = df.ln_G</p> <p>>>> ln_sigma_samples = df.ln_sigma</p>
Figure 5. - Leucotrichiaadela Wells & Wichard, 1989 (redrawn from Wells and Wichard 1989). Male genitalia: A segments VII–X, lateral B segments IX–X, dorsal C mesoventral process of segment VII and segment VIII, ventral.
Figure 5. - Leucotrichiaadela Wells & Wichard, 1989 (redrawn from Wells and Wichard 1989). Male genitalia: A segments VII–X, lateral B segments IX–X, dorsal C mesoventral process of segment VII and segment VIII, ventral.
Figure 4. - Brightfield image showing the posterior mesosoma and anterior metasoma of Pteroceraphronmirabilipennis Dessart 1981. Arrows point to bifurcated anteromedian process of the propodeum-metanotum complex.
Figure 4. - Brightfield image showing the posterior mesosoma and anterior metasoma of Pteroceraphronmirabilipennis Dessart 1981. Arrows point to bifurcated anteromedian process of the propodeum-metanotum complex.
Underlying data for Slimani et al. Identification of dominant hydrogeochemical processes for groundwaters in the Algerian Sahara supported by inverse modeling of chemical and isotopic data
<p>The data hereafter underlie the paper by Slimani et al. doi:10.5194/hess-20-1-2016,</p> <p>appeared to Hydrol. Earth Syst. Sci., 20, 1-23, 2016.</p> <p> </p> <p> </p> <p>1. File Tableaux_data.xls</p> <p> </p> <p>This is an Excel sheet file. It contains:</p> <p>- raw analytical data, mostly in mg/L;</p> <p>- data converted in mmol/L;</p> <p>- data corrected from the defect of cations - anions balance; the correction is made proportionally.</p> <p>- the previous data completed with logarithms of activities, computed by Phreeqc, </p> <p>for calcium, sulfate, carbonate and water; those data are used to plot equilibrium diagrams for calcite and gypsum (figure 6);</p> <p>- for Phreatic aquifer only, saturation indexes for halite, anhydrite, calcite, dolomite and gypsum, along with distance from south to north, used in figure 7.</p> <p>2. Directory Phreeqc_res</p> <p>Contains the input file with all samples from CI, CT and Phr in a single file, and the selected output file.</p> <p>All calculations were made with version phreeqc-3.1.2 and database sit.dat.</p> <p>3. Directory Inverse models</p> <p>This directory contains inverse models for computing transformations:</p> <p>- from CI (average) to CT (average):</p> <p>- from CT (average) to Phr (pole I, average);</p> <p>- from pure water to Phr (pole II, sample P036);</p> <p>- for mixing Phr pole I and II, and try to explain a sample typical of medium mixing ratio, sample P068.</p>
Pre-processed PubMed data for a study of coauthorship
<p>This dataset was collected from the PubMed portal to MEDLINE and other repositories of biomedical research (https://www.ncbi.nlm.nih.gov/pubmed/). Analysis of the dataset led to the paper "Effects of research complexity and competition on the incidence and growth of coauthorship in biomedicine", published in PLOS One (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0173444). The raw data were pre-processed using the script "clean.r" in the project directory on GitHub (https://github.com/corybrunson/coauthor) to obtain the file presented here.</p> <p>The dataset is formatted as a data table (https://cran.r-project.org/web/packages/data.table/index.html), a class of data frame in R, and saved as a .RData file, which can be loaded into an R session via `load("path/to/dataset/pmDat.RData")`. The fields are as follows:</p> <ul> <li>`pmid` - the unique publication identifier (PMID) used by PubMed</li> <li>`jid` - the unique journal identifier used by PubMed</li> <li>`issn` - the (print) ISSN of the journal</li> <li>`ym` - the month and year of publication</li> <li>`nau` - the number of authors credited by the publication (up to any limits imposed by PubMed, and counting each author collective as a single author)</li> <li>`cau` - whether any corporate author was credited</li> <li>`rev` - whether the publication was tagged as a review</li> <li>`trial` - whether the publication was tagged as a clinical trial</li> <li>`npmt` - the number of MeSH terms assigned to the publication that were flagged as "major" topics</li> <li>`nmh` - the number of top-level MeSH headings assigned to the publication</li> <li>`supp` - whether the publication was tagged as having received financial support</li> <li>`ng` - the number of grants acknowledged by the publication</li> <li>`co` - the country in which the journal was published</li> </ul> <p>Note that the field values for any publication can be validated by searching for the PMID in PubMed.</p>
Distinct Visual Processing of Real Objects and Pictures of Those Objects in 7- to 9-month-old Infants
<p>Data set of Gerhard, T.M., Culham, J.C. & Schwarzer, G. (2016). Distinct visual processing of real objects and pictures of those objects in 7- to 9-month-old infants. <em>Frontiers in Psychology, 7. doi:10.3389/fpsyg.2016.00827</em></p>
Experimental data for the paper "Scalable Fine-Grained Proofs for Formula Processing"
<p>We provide here the binary, options and experimental data for our CADE paper and the companion report.</p> <p><strong>Setup</strong></p> <p>The tarball containing the source code of veriT used in our experiments is available here. The command line parameters of veriT used in each of the configurations described in the paper are:</p> <ul> <li>Basic: "--old-processing --disable-sym --disable-simp --disable-unit-simp --disable-unit-subst-simp --disable-ackermann --disable-bclause"</li> <li>Extended: "--old-processing --disable-sym --disable-unit-simp --disable-unit-subst-simp --disable-ackermann --disable-bclause"</li> <li>Complete: "--old-processing"</li> <li>with proofs: "--proof=/dev/null --proof-with-sharing"</li> <li>with new code: remove parameter "--old-processing"</li> </ul> <p>The benchmarks are from the SMT-LIB categories QF_ALIA, QF_AUFLIA, QF_IDL, QF_LIA, QF_LRA, QF_RDL, QF_UF, QF_UFIDL, QF_UFLIA, QF_UFLRA, AUFLIA, AUFLIRA, UF, UFIDL, UFLIA, and UFLRA.</p> <p>Our experiments were conducted on servers equipped with two Intel Xeon E5-2630 v3 processors, with eight cores per processor, and 126 GB of memory. The time limit was set to 30 s.</p>
Superior Temporal Sulcus Disconnectivity During Processing of Metaphoric Gestures in Schizophrenia
<p><strong><em>Data related to the following publication:</em></strong></p> <p>Straube, B., Green, A., Sass, K., & Kircher, T. (2014). Superior Temporal Sulcus Disconnectivity During Processing of Metaphoric Gestures in Schizophrenia. <em>Schizophrenia Bulletin</em>, <em>40</em>(4), 936–944. http://doi.org/10.1093/schbul/sbt110</p> <p> </p>
The relation between crawling and non-crawling 9-month-old infants' visual prediction abilities in spatial object processing.
<p>The data set Kubicek et al._JECP_DataSet.sav containts the data of the paper from Kubicek, C., Jovanovic, B., & Schwarzer, G. (2017). The relation between crawling and non-crawling 9-month-old infants' visual prediction abilities in spatial object processing. Journal of Experimental Child Psychology, 158, 64–76.</p>
Pre-processed B cell receptor sequences from BioProject PRJNA349143
<p>Processed sequencing data from BioProject PRJNA349143.</p> <p><strong>Study Design</strong></p> <p>Samples were collected from human volunteers as described in Laserson and Vigneault et al, 2014 (1). Briefly, blood samples were collected from three individuals both pre- and post-vaccination for seasonal influenza. Samples were collected for sequencing at time points -8 days, -2 days, -1 hour, +1 hour, +1 day, +3 days, +7 days, +14 days, +21 days and +28 days relative to injection with seasonal influenza vaccine.</p> <p><strong>Library Preparation and Sequencing</strong></p> <p>The original samples from Laserson and Vigneault et al, 2014 (1) were re-sequenced as described in Gupta et al, 2017 (2). Briefly, sequencing libraries were prepared from mRNA using 5'RACE with addition of 17-nucleotide unique molecular identifiers (UMIs). Amplification was performed using constant region primers specific to IGHA, IGHD, IGHE, IGHG, IGHM, IGKC and IGLC. Sequencing was conducted on the Illumina MiSeq platform using the 600 cycle kit with 325 cycles for read 1 and 275 cycles for read 2. A 10% PhiX spike-in was added for sequencing.</p> <p><strong>Data Processing</strong></p> <p>Sequences were processed using the pRESTO (3) and Change-O (4) toolkits as described in Gupta et al, 2017 (2).</p> <p>Note, the provided data has been filtered significantly, including the removal of sequences that fail V(D)J alignment and the exclusion of non-functional sequences.</p> <p><strong>Format</strong></p> <p>Processed sequences are provided in FASTA format annotated using the pRESTO scheme.</p> <p>Annotations included are as follows:</p> <ul> <li><strong>CONSCOUNT:</strong> Raw read count from which UMI consensus sequences were generated, summed over all UMIs for the given unique sequence.</li> <li><strong>DUPCOUNT:</strong> UMI count for the given unique sequence.</li> <li><strong>PRCONS:</strong> Constant region primer (isotype).</li> <li><strong>SUBJECT:</strong> Subject identifier.</li> <li><strong>TIME_POINT:</strong> Time point label.</li> </ul> <p><strong>Citations</strong></p> <ol> <li>Laserson U and Vigneault F, et al. High-resolution antibody dynamics of vaccine-induced immune responses. Proc Natl Acad Sci USA 111, 4928-33 (2014).</li> <li>Gupta NT, et al. Hierarchical Clustering Can Identify B Cell Clones with High Confidence in Ig Repertoire Sequencing Data. J Immunol 1601850 (2017).</li> <li>Vander Heiden JA and Yaari G, et al. pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. Bioinformatics 30, 1930–2 (2014).</li> <li>Gupta NT and Vander Heiden JA, et al. Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics 31, 3356–8 (2015).</li> </ol>
Polygon Laser Texturing Process
<p>Polygon Laser Texturing Process</p>
Screen Capture & Audior Recordings - Empirical Study "Business Process Model Validation Through Virtual Enactment"
<p>Screen captures of task completion and audio recordings of interviews of the empirical study which has been conducted within the context of the master thesis "Business Process Model Validation Through Virtual Enactment"</p>
Hemispheric diferences in the processing of visual consequences of active vs. passive movements: a transcranial direct current stimulation study
<p>Dataset related to the following publication:</p> <p>Straube, B., Schülke, R., Drewing, K., Kircher, T., van Kemenade, B.M. (2017). Hemispheric differences in the processing of visual consequences of active vs. passive movements: a transcranial direct current stimulation study. Exp. Brain Res. DOI: 10.1007/s00221-017-5053-x</p>
ScienceDex guides
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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.