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7,195 results for “SET”
high-resolution data set of esterase vb_24B_21 from Shiga toxin-encoding bacteriophage phi24B; PDB id is 6YP6
<p>high-resolution data set of esterase vb_24B_21 from Shiga toxin-encoding bacteriophage phi24B; PDB id is 6YP6</p> <p>Data were collected at Diamond I04 on February 8, 2012 using an ADSC detector.</p>
medium resolution data set of esterase vb_24B_21 from Shiga toxin-encoding bacteriophage phi24B; PDB id 6YP6
<p>medium resolution data set of esterase vb_24B_21 from Shiga toxin-encoding bacteriophage phi24B; PDB id 6YP6</p> <p>Data were collected at Diamond I04-1 on February 6, 2012</p>
Data set | Geoelectrical and transient electromagnetic surveys at Viveros de Netzahualcóyotl in Xochimilco, Mexico City, Mexico
<p>This repository contains geophysical raw data collected during two field campaigns in 2016 and 2017 at <a href="https://goo.gl/maps/K7xEC44MdnQno9CG8">Viveros de Netzahualcóyotl in Xochimilco</a>, Mexico City, Mexico.</p> <p>The data set includes raw data analysed for the first case study discussed in the following publication</p> <p>Bücker, M., Lozano-Garcia. S., Ortega-Guerrero, B., Caballero-Miranda, M., Pérez, L., Caballero, L., Pita de la Paz, C., Sánchez-Galindo, A., Jesús Villegas, F., Flores Orozco, A., Brown, E., Werne, J., Valero Garcés, B., Schwalb, A., Kemna, A., Sánchez-Alvaro, E., Launizar-Martínez, N., Valverde-Placencia, A., Garay-Jiménez, F. (2017). <em><strong>Geoelectrical and Electromagnetic Methods Applied to Paleolimnological Studies: Two Examples from Desiccated Lakes in the Basin of Mexico</strong></em>. Boletín de la Sociedad Geológica Mexicana, 69(2), 279-298, <a href="http://dx.doi.org/10.18268/bsgm2017v69n2a1">http://dx.doi.org/10.18268/bsgm2017v69n2a1</a>.</p> <p>If you find our data useful in your own research, please mention this data set and/or the manuscript.</p> <p>The data are provided under the Creative Commons Attribution 4.0 International license.</p>
Data set - Lagrangian observations and modelling of turbulence along a tidally influenced river
<p>The 'Kaipara_model.mat' files contains the grid and bathymetry of a model of the Kaipara River, New Zealand, created notably in order to study turbulence in a Lagrangian frame of reference. </p> <p>The 'Dataset_Kaipara_Lagrangian.mat' file contains Lagrangian observations collected in the Kaipara river and corresponding model predictions. </p>
Data sets for: EMA-amplicon-based sequencing of untreated and SODIS treated rainwater
<p>Data sets for the EMA-amplicon-based sequencing of untreated and treated rainwater collected from an informal settlement and rural farming community in South Africa. The data sets were used for the publication: EMA-amplicon-based sequencing informs risk assessment analysis of water treatment systems (Submitted to Science of the Total Environment).</p>
Perch positioning affects laying hen locomotion and forces experienced at the keel - FULL DATA SET
<p>Full data set for publication in Animals</p> <p><strong>Perch positioning affects laying hen locomotion and forces experienced at the keel</strong></p> <p><em>Christina Rufener, Ana K. Rentsch, Ariane Stratmann, Michael J. Toscano</em></p>
MarTREC Data Set for Report: Large Scale Evaluation of Erosion Resistance of Biocementation against Bridge Scour and Roadway Shoulder Erosion
<p>Data set used for the report, "Large Scale Evaluation of Erosion Resistance of Biocementation against Bridge Scour and Roadway Shoulder Erosion". The purpose of the report was to develop an alternative approach for armoring the riverbed with biocementation through MICP to mitigate soil erosion. Long-term erosion exposed to outdoor environment, rainfall induced erosion, and accelerated erosion were conducted on MICP-treated samples to prove the feasibility of the MICP technique for potential applications in prevention of bridge scour and road shoulder erosion. The experimental work and discussion about the testing results indicated that exposing to outdoor environment could result in sharp decrease on UCS for MICP-treated samples. But the MICP-treated samples had better resistance to rainfall induced erosion. The bio-surface treatment gave significant help for cement-treated samples to resist accelerated erosion and water absorption, especially the multiple bio-surface treatments method, no erosion could be measured and 5% lower water absorption was achieved after the cement-treated sample was triple treated by bio-surface treatments. The pure MICP-treated samples were also good at resisting accelerated erosion and water absorption. Furthermore, fiber addition and multiple MICP treatments could improve their resistance. The maximum erosion rate of single MICP-treated samples reduced from 0.16 mm/min to 0 mm/min after triple MICP treatment cycles applied on the samples. Extra MICP treatments on soil samples improved the resistance to water absorption significantly. The triple MICP-treated samples achieved a 6% lower absorption than single-treated samples. All these results indicated that the bio-mediated particulate material based on MICP can provide an effective solution for problematic cases of sandy soil in prevention of bridge scour and road shoulder erosion.</p>
A Large-scale Data Set and an Empirical Study of Docker Images Hosted on Docker Hub
<p>The data set schema, fields description, and analysis scripts are in the GitHub artifact repository.</p> <p>https://github.com/linncy/icsme2020-docker-study</p> <p> </p> <p>@inproceedings{LinICSME20,<br>author={Changyuan Lin and Sarah Nadi and Hamzeh Khazaei},<br>title={A Large-scale Data Set and an Empirical Study of Docker Images Hosted on Docker Hub},<br>booktitle={Proceedings of the 36th IEEE International Conference on Software Maintenance and Evolution (ICSME)},<br>year={2020},<br>url_Paper={https://www.dropbox.com/s/3bktcmdr7rlw1ic/LinICSME20.pdf}<br>}</p>
Evaluation set DCASE 2020 task 4 (for submissions)
<p>This repo contains the dataset to download to submit results and be evaluated in task 4 of DCASE 2020.</p> <p> </p> <p>Please, check the submission package in order to follow the instruction to have a submission.</p> <p>*Note: some files are 5 mins long, so if your system is not suitable for this, make sure you aggegate the results.*</p>
Catalan United Nations v1.0 test set
<p>Catalan version [1] of the test set from the United Nations v1.0 [2]. The translation was performed in two steps: we did a first automatic translation from the Spanish test set version into Catalan and then a professional translator post-edited the output.</p> <p><br> [1] Marta R. Costa-Jussà, Noé Casas, Carlos Escolano, and José A. R. Fonollosa. 2019. Chinese-Catalan: A Neural Machine Translation Approach Based on Pivoting and Attention Mechanisms. <em>ACM Trans. Asian Low-Resour. Lang. Inf. Process.</em> 18, 4, Article 43 (August 2019), 8 pages. DOI:https://doi.org/10.1145/3312575</p> <p>[2] Michal Ziemski, Marcin Junczys-Dowmunt, and Bruno Pouliquen. 2016. The United Nations parallel corpus v1.0. In<br> Proceedings of the LREC, 2016</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>
User Stories made by Users Workshop Data Set
<p>In order to enable members of a socio-technical evolutionary-teal organization to design their technical component, we conducted a workshop that structures the collaboration between technical trained participants and non-trained participants. The workshop aims to transform "vague needs" into technical descriptions in the form of user stories.</p> <p>The workshop is the second part of series of workshops all limited to two hours. It uses the methods of <em>Design Thinking</em> and <em>Participatory Design</em>.</p> <p>The workshop has been recorded in video and this data set contains a textual German transcript, transcripts of the moderation cards that have been created during the workshop, invitations of the participants, additional material needed to conduct the workshop (timetable, moderation concept, etc.), and material used during the workshop by the participants (leaflets, slides, etc.).</p> <p>We hope that the material can be used to (a) comprehend the interpretation used in our qualitative research, (b) to adapt the workshop model by other volunteers of our case study Viva con Agua de St. Pauli e.V. (<a href="https://www.vivaconagua.org/">https://www.vivaconagua.org/</a>), and (c) investigate other interesting research questions.</p>
Gadgetron 2020 Summer School Raw Data Set - Part 2
<p>These data were missed when assembling the original archive containing data that accompanied the live coding/demonstration sessions in the Gadgetron 2020 Summer Course sessions. This, with the original archive, should provide all data used for the 2020 class.</p> <p>The layout of files in the unpacked archive should reflect the sessions / lectures on each day.</p> <p> </p>
FID-MRSI (HFMRC, MU Vienna) Example Data: FID-MRSI basis sets with measured MM - 3T & 7T
<p>Data from the Vienna 7T MRSI group<br> Contact: wolfgang.bogner@meduniwien.ac.at , mpovaza1@jhmi.edu</p> <p>Format: LCModel .basis and .RAW</p> <p>Field strength: 3T / 7T</p> <p>Macromolecules: Yes, Measured</p> <p>Sequence: FID-MRSI</p> <p>Acquisition delay: 0ms / 1.3ms / 1.5ms</p> <p>Vendor: Siemens</p> <p>Basis set consisting of metabolite resonances simulated in NMR-Scope(-B) and measured macromolecules. For more information, please contact authors.</p>
Data set for the manuscript "Reproductive physiology corresponds to adult nutrition and task performance in a Neotropical paper wasp: a test of dominance-nutrition hypothesis predictions"
<p>Data set for the manuscript "Reproductive physiology corresponds to adult nutrition and task performance in a Neotropical paper wasp: a test of dominance-nutrition hypothesis predictions"</p>
Numerical Fire Spread Simulation Based on Material Pyrolysis - An Application to the CHRISTIFIRE Phase 1 Horizontal Cable Tray Tests - Data Set
<p>This data set is a supplementary resource for the article "<a href="http://www.mdpi.com/2571-6255/3/3/33">Numerical Fire Spread Simulation Based on Material Pyrolysis - An Application to the CHRISTIFIRE Phase 1 Horizontal Cable Tray Tests</a>", published by the peer-reviewed open access journal <a href="https://www.mdpi.com/journal/fire">Fire</a>. It is part of the "<a href="https://www.researchgate.net/project/Fire-Propagation-in-Cable-Tray-Installations">Fire Propagation in Cable Tray Installations</a>" project. The provided data is only a summary of the full data produced for the article, due to its size.</p> <p>This article was previously submitted to the Fire Safety Journal and got eventually rejected.</p> <p>The information is structured into multiple *.rar archieves, which mimic the sub-directory structure created for the work. To be able to run the analysis scripts without much tweaking, extract all archieves into the same directory, with each archieve being a sub-directory in it.</p> <p>The data set is comprised of:</p> <ul> <li>The PROPTI and FDS input files used for the inverse modelling process (IMP) -- the 13* archieves.</li> <li>Full FDS simulation data of the mirco-combustion calorimeter (MCC) simulations of the best parameter sets per generation of the IMP runs, for jacket and insulator materials.</li> <li>Full FDS simulation data of the Cone Calorimeter simulations of the best parameter sets per generation of the IMP runs, for all three (25 kW/m², 50 kW/m², 75 kW/m²) incident heat flux conditions.</li> <li>FDS input files for the MT3 simulations, but full data only for the best parameter sets per IMP run (see below).</li> <li>Jupyter notebooks used for the analysis of the simulation responses including the scripts and plots generated for, and used in, the paper (RunReports).</li> <li>A general information directory, containing the FDS input file templates, experimental data used as target and Python scripts containing helper functions.</li> <li>Videos of a qualitative comparison of the SmokeView animation of the best parameter set in a cable tray simulation against a video from the experiment and an animation of the GAUGE_HEAT_FLUX development for the same simulation over the course of the simulation.</li> </ul> <p>Due to the size of the MT3 simulation data, only the FDS input files for the best parameter sets per generation are uploaded. Complete FDS simulation data is only provieded for the best perameter set of each IMP run, these are :</p> <p>IMP run, best rep.<br> --------------------------------<br> imp_13b, 110751<br> imp_13c, 121106<br> imp_13d, 137738<br> imp_13e, 97493<br> imp_13f, 91988<br> imp_13g, 86988<br> imp_13h, 17480<br> imp_13b_1, 19033<br> imp_13b_2, 25043<br> imp_13b_3b, 24149<br> imp_13b_4, 29666<br> imp_13h_1, 10685<br> imp_13h_2, 10024<br> imp_13h_3, 10109<br> imp_13i, 13253</p> <p> </p> <p>Note: The individual runs of the IMP are named differently as compared to the labeling used in the paper, as described below:</p> <p>IMP run, label in paper<br> --------------------------------</p> <p>imp_13b, T<sub>b</sub><br> imp_13c, T<sub>a</sub><br> imp_13d, T<sub>c</sub><br> imp_13e, T<sub>a,b,c</sub><br> imp_13f, T<sub>b,c</sub><br> imp_13g, T<sub>a,c</sub><br> imp_13h, T<sub>a,b,c</sub>L<sub>A,L1,HC</sub><br> imp_13b_1, T<sub>b</sub>L<sub>1</sub><br> imp_13b_2, T<sub>b</sub>P<sub>L1</sub><br> imp_13b_3b, T<sub>b</sub>P<sub>L1</sub>L<sub>1</sub><br> imp_13b_4, T<sub>b</sub>P<sub>L2,HT</sub><br> imp_13h_1, T<sub>a,b,c</sub>P<sub>A,L1,HC</sub>L<sub>1</sub><br> imp_13h_2, T<sub>a,b,c</sub>P<sub>A,L1,HC</sub>L<sub>2</sub><br> imp_13h_3, T<sub>a,b,c</sub>P<sub>A,L1,HC</sub>L<sub>3</sub><br> imp_13i, T<sub>b</sub>P<sub>A,L1,HC</sub></p> <p> </p> <p>Version 2 changes:</p> <p>Added pre-print.</p> <p> </p> <p>Version 3 changes:</p> <p>Added new files that where produced during the revision process, after the original manuscript got rejected by the Fire Safety Journal. This revision corresponds to the intitial manuscript that was submitted to the journal <a href="https://www.mdpi.com/journal/fire">Fire</a>. The respective Zip archieves are labeled with a "_Revision01".</p>
data_set_deAnna_et_al_2020_chemotaxis_dispersion
<p>Data set associated to the publication "Chemotaxis under flow disorder shapes microbial dispersion in porous media". It includes the following:</p> <p>i) images of bacterial distribution in a microfluidics porous medium replica while displaced by a continuously injected front of a solute triggering microbial chemotaxis towards positive gradients (attractant).</p> <p>ii) images of bacterial distribution in a microfluidics porous medium replica while displaced by a continuously injected front of a solute triggering microbial chemotaxis towards negative gradients (repellent).</p> <p>iii) images of a continuously injected front of fluorescent tracer.</p> <p>iv) images of a chemotaxis assay to characterize positive/negative chemotaxis towards/from a chemoattractant/repellent: no flow conditions, steady and homogeneous chemical gradient.</p>
The V2S Dataset: A Set of Android Screen Recordings, Training Images, and Models
<p>This is the dataset and models used in the paper entitled "Translating Video Recordings of Mobile App Usages into Replayable Scenarios" published at the 42nd International Conference on Software Engineering (ICSE'20)</p> <p>Link to V2S Paper: https://arxiv.org/abs/2005.09057</p>
Classify on the Clock (CloCk) - An Entry Level Image Data Set for Neural Networks
<p><strong>General information</strong></p> <p>This data set contains synthetic images of an analog clock. Each time point from 00:00:00 - 11:59:59 is included as a separate image. We provide three different versions of each image with an increasing number of additional information:</p> <ul> <li><em>Sparse</em>: The image includes just the hour, minute and second hand</li> <li><em>Reduced</em>: Additional markings around the clock</li> <li><em>Full</em>: Additional written numbers</li> </ul> <p>The hands differ in size and color:</p> <ul> <li><em>Hour</em>: Black, short, wide</li> <li><em>Minute</em>: Blue, long, medium</li> <li><em>Second</em>: Red, long, slim</li> </ul> <p>We provide the following files in this data repository:</p> <ul> <li>RGB images with a size of 512x512 for all three versions</li> <li>A suggested training, validation and test split</li> <li>The creation script as Python file</li> </ul> <p>The script can easily be modified to create images with a different size and color.</p> <p> </p> <p><strong>Clock System</strong></p> <p>The movements of the hands follow a linear relationship. Their angles can be calculated by the forward system:</p> <p><span class="math-tex">\( \begin{bmatrix} 6^\circ & 0 & 0 \\ 0.1^\circ & 6^\circ & 0 \\ 0 & 0.5^\circ & 30^\circ \end{bmatrix} \begin{pmatrix} n_{\text{sec}} \\ n_{\text{min}} \\ n_{\text{hour}} \end{pmatrix} = \begin{pmatrix} \alpha_{\text{sec}}\\ \alpha_{\text{min}} \\ \alpha_{\text{hour}} \end{pmatrix}.\)</span></p> <p>One can also introduce rotated versions of the images. The system becomes non-linear in this case:</p> <p><span class="math-tex">\(\operatorname{mod}\left( \begin{bmatrix} 6^\circ & 0 & 0 \\ 0.1^\circ & 6^\circ & 0 \\ 0 & 0.5^\circ & 30^\circ \end{bmatrix} \begin{pmatrix} n_{\text{sec}} \\ n_{\text{min}} \\ n_{\text{hour}} \end{pmatrix} + \omega, \, 360^\circ \right) = \begin{pmatrix} \alpha_{\text{sec}}\\ \alpha_{\text{min}} \\ \alpha_{\text{hour}} \end{pmatrix}.\)</span></p> <p> </p> <p><strong>Use Cases</strong></p> <p>This data set was originally designed for basic research on Capsule Networks. Use cases are:</p> <ul> <li>Evalutation of classification and regression performance</li> <li>Influence of image transformations, e.g. rotations</li> <li>Detection of the hierarchy & relationship of image parts</li> <li>Concealment of objects in the image</li> <li>Interpretability of the learned model</li> <li>Solving a discrete inverse problem (<em>sparse</em> version)</li> <li>...</li> </ul>
FIG. 6. The specific synanthropic setting where the holotype and a in A new species of Scotophilus (Chiroptera: Vespertilionidae) from western Madagascar
FIG. 6. The specific synanthropic setting where the holotype and a portion of the type series of S. marovaza were collected. The arrow indicates the site and the direction bats would have entered into the day roost site within the palm leaves. (Photograph taken by Mamy Ravokatra)
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.