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37,310 results for “6”
Synthesis of an anion receptor using 3,6-diaminophenanthrene as a scaffold
<p>1H and 13C NMR spectra for the compounds:</p> <ul> <li>2-iodo-4-nitrobenzoic acid (<strong>6</strong>)</li> <li>(2-iodo-4-nitrophenyl)methanol (<strong>7</strong>)</li> <li>2-iodo-4-nitrobenzaldehyde (<strong>8</strong>)</li> <li>5,5'-dinitro-[1,1'-biphenyl]-2,2'-dicarbaldehyde (<strong>9</strong>)</li> <li>3,6-dinitrophenanthrene (<strong>10</strong>)</li> <li>3,6-diaminophenanthrene (<strong>11</strong>)</li> <li>1,1'-(phenanthrene-3,6-diyl)bis(3-(3,5-bis(trifluoromethyl)phenyl)urea) (<strong>13</strong>)</li> </ul> <p>MNOVA files for the compounds are provided, as well as the original files recorded on a 400MHz JEOL NMR spectrometer in jdf format.</p>
Capillary- and stored-elastic-energy-driven anisotropic sub-grain coarsening (PRX2D) (Part 6)
<p><strong>Further details in the TopologyTracer documentation</strong><br> https://github.com/mkuehbach/TopologyTracer/tree/master/docs/build</p> <p><strong>Further details microstructure synthesis in the IMMMicrostructur generator documentation</strong><br> https://github.com/GraGLeS/IMM_MicrostructureGenerator/tree/master/docs/build/html</p> <p><strong>Repository content</strong></p> <p>Texture_Faces_260_to_449.zip<br> Grain boundary network geometry and grain meta data tracking data for time step 260 to 449 with 1 time step resolution</p>
Agriculture - General: Animal Production and Health 6
<p>This dataset contains the digitized treatments in Plazi based on the original journal article Viana, Jéssica Herzog, Ribeiro-Costa, Cibele Stramare (2013): Review of the largest species group of the New World seed beetle genus Sennius Bridwell (Coleoptera: Chrysomelidae), with host plant associations. Zootaxa 3736 (5): 501-535, DOI: <a href="http://dx.doi.org/10.11646/">http://dx.doi.org/10.11646/</a></p>
Agriculture - General: biological diversity 6
<p>A database of tree of biological, cultural, ecological or historical interest because of their age, size or condition. National Biodiversity Data Centre (2016). Heritage Trees of Ireland. Occurrence dataset <a href="https://doi.org/10.15468/9athfc">https://doi.org/10.15468/9athfc</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 6
<p>2007, 2011 and 2012, microarthropod fauna inventory in a nature restauration experiment (re-introduction) on a calcareous grassland and three reverence sites in the province of Limburg using pF-cores</p> <p>Smits N, Dimmers W (2016). Alterra (NL) - Microarthropods inventory in calcareous grasslands. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/28jocn">https://doi.org/10.15468/28jocn</a> accessed via GBIF.org</p>
Coarse fragments % (volumetric) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Coarse fragments % (volumetric) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>coarsefrag.vfraction = variable: coarse fragments volumetric fraction,</li> <li>usda.3b1 = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Silt content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Silt content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p> </p> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>silt.wfraction = variable: silt weight fraction,</li> <li>usda.3a1a1a = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Back to the edge: relative coordinate system for use-wear analysis [complement to Online Resource 6]
<p>Raw data, and R markdown scripts and HTML outputs of the statistical procedures.</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
FT31 Tauber (1) 6-key tenoroon: basic data
<p>Dataset of FT31 Tauber (1) 6-key tenoroon containing basic data. Measurement data will be collected at a later date.</p> <p> </p>
FT8 Astor-Horwood 6-key tenoroon: basic data, photos.
<p>Dataset of FT8 Astor-Horwood 6-key tenoroon containing detailed information/data and photos. (More complete measurement data to be collected at a later date.)</p>
FT11 Cahusac (1) 6-key tenoroon: measurements, photos, endoscopic video
<p> Dataset of FT11 Cahusac 6-key tenoroon containing detailed external and internal measurements, photos, and an endoscopic video. </p>
Molecular dynamics simulations of the interaction of wild type human CYP2J2 with arachidonic acid (POSES 5 and 6)
<p><strong>Description of files in this dataset:</strong></p> <p><strong>MD_wt_CYP2J2_AA_StateX_repeatY.zip</strong> : Series of zipped directories for molecular dynamics simulations of arachidonic acid in the active site of wild type CYP2J2. X is the docking pose number that constitutes the starting point of the simulation (the 6 lowest-energy poses from docking were selected as the starting points of the simulations - this dataset is State(pose) 1). Y is the repeat (each simulation was repeated 4 times, hence there are 4 repeats per pose). </p> <p>Each directory contains the following sub-directories:</p> <p>001.leap : Amber parameter and coordinate files; PDBs; ligands; leap commands</p> <p>002.min : Minimisation stage</p> <p>003.heat : Heating stage</p> <p>004.equil: Equilibration stage</p> <p>005.md : Production stage</p> <p>006.analysis : Basic energy graphs</p> <p>007.cpptraj: Contains only the file strip.md.nc (Amber trajectories stripped of water in netCDF format)</p>
Dataset of Kantorovich-Rubinstein-Wasserstein Polytopes of Metric Spaces on up to 6 Points
<p>We present a complete list of all combinatorial types of generic Kantorovich-Rubinstein-Wasserstein (KRW) polytopes associated with metric spaces on up to 6 points that are generic in the sense of Gordon and Petrov, see [1]. These polytopes and their properties are described in detail in [2].</p> <p>The catalog of KRW polytopes was computed using certain regular triangulations of the full root polytope, see Section 4 in [2]. These regular triangulations were enumerated up to symmetry by Jörg Rambau using the new <em>topcom</em> package described in [3].</p> <p>The provided data comes in three parts.</p> <ul> <li>The files ending in ".result" contain the original <em>topcom</em> output including the specific regular triangulations of the root polytope.</li> <li>There are <em>julia</em> files that contain these triangulations ("triangulations_x.jl"), one triangulation per line.</li> <li>There is an <em>OSCAR</em> script ("read_triangulations.jl") that reads these triangulations and produces sample metrics associated with each of these triangulations. </li> </ul> <h3>References:</h3> <p>[1] J. Gordon and F. Petrov: Combinatorics of the Lipschitz polytope, 2017, Arnold Math. J. <em>3.2.</em></p> <p>[2] E. Delucchi, L. Kühne, and L. Mühlherr: <em>Combinatorial invariants of finite metric spaces and the Wasserstein arrangement</em>, 2024, in preparation.</p> <p>[3] J. Rambau: <em>Symmetric lexicographic subset reverse search for the enumeration of circuits, cocircuits, and triangulations up to symmetry, </em>2023, <a href="https://www.wm.uni-bayreuth.de/de/team/rambau_joerg/TOPCOM/SymLexSubsetRS-2.pdf" target="_blank" rel="noopener">preprint</a>.</p>
Velocity from the shelf at Station CSI 6 in 2017
<div>Title: Velocity from the shelf at Station CSI 6 in 2017</div> <div> </div> <div>Velocity Data from ADCP deployed at CSI 6 (~ 20 m)</div> <div>Time - days counted from Jan. 1, 2017 (UTC) - Jan. 1 is Day 1.</div> <div>u, v, horizontal velocity in east and north direction in m/s</div> <div>Zmab (bin depth meter above bottom)</div> <div>Invalid data is indicated by a large velocity of -32.768 m/s</div> <div>PI: Chunyan Li at Louisiana State University</div> <div>Project: NSF 1736713 </div> <div>File name: Velocity_from_shelf_CSI6_2017.dat</div> <div> </div> <div>Pubications: </div> <div>Li, C. A. Sheremet, W. Huang, P. Dash, A. Katkar, M. N. Allahdadi, N. Chaichitehrani, C. M. Bachmann, V. H. Rivera-Monroy (2024). Dynamic impact of transiting weather systems on coastal currents in the northern Gulf of Mexico, Deep Sea Research Part II: Topical Studies in Oceanography, Volume 216, 2024, 105395, https://doi.org/10.1016/j.dsr2.2024.105395.</div> <p> </p>
Experiment to measure the thickness loss due to corrosion of a 5mm uncoated steel sample exposed close to the shore during 6 months
<p>This is the data obtained during a experiment conducted in Grand Canary Island close to the shore to collect data using CEIT's ultrasound sensor node attached to a 5mm uncoated steel sample. We collected during 6 months the measured thickness taking two measures per day. The sample was considerably degraded during that time. The estimated Corrosion Rate (134um/year) was in coherence with the typical corrosion rates in real environments (100-200um/year). </p>
Over 6 billion liters of Canadian milk wasted since 2012
<p>This file provides data used to approximate the annual Canadian milk yield based on herd statistics and estimated on-farm use. Using a mass balance approach, subtracting farm gate sales data, the balance is assumed to be wasted before leaving the farm gate. Data sources are provided with supporting literature where relevant.</p> <p>This recomended citation for this record is:</p> <p>Elliot, T., Goldstein, B. P., and Charlebois, S. (2025). Over 6 billion liters of Canadian milk wasted since 2012. <em>Ecological Economics, 227</em>, 108413. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ecolecon.2024.108413" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.ecolecon.2024.108413</span></span></a></p>
IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 1st Web-Delphi process to HTA stakeholders, organized into 6 separate parallel panels
<p>IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 1<sup>st</sup> Web-Delphi process to HTA stakeholders, organized into 6 separate parallel panels (one panel per stakeholder group, 2 rounds), about the views of stakeholders regarding “This aspect should be considered in the evaluation of new medicines on a common basis” (2019)</p> <p>For details on the Web-Delphi process, see: IMPACT HTA, Work Package 7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2, Deliverable 7.2 (Multi-criteria evaluation framework), Advancing knowledge and MCDA tools to assist HTA agencies in evaluating medicines on a common basis (2021) Oliveira, M.D. (IST), Panos Kanavos (LSE), Bana e Costa, C. (IST)</p>
Wiener Feuerwehrplan (Zone II, Kolonie 6), 1928
<ul> <li>Gezeichnet im Planbüro der Wiener Berufsfeuerwehr, 22. Juni 1928</li> <li>Druck: Kartografisches, früher Militärgeografisches Institut in Wien</li> <li>Original verfügbar im Landkartenarchiv des Bundesamtes für Eich- und Vermessungswesen (BEV)</li> <li>Quelle: BEV, 2019</li> </ul>
Surfalex HF formability study - Workflow 6 - Generate random volume element
<p>This MatFlow workflow is the sixth in a set of eight workflows developed during our formability study of the Surfalex HF (AA6016A) material. In this workflow, we generate a comparison volume element from a random texture and equiaxed microstructure. This RVE is used in a comparison of the simulated Lankford coefficients between the Surfalex model RVE and this "random" RVE.</p> <p>This workflow can be downloaded and explored in a Jupyter notebook, as explained in the <a href="https://github.com/LightForm-group/surfalex_data_explorer">GitHub repository here</a>.</p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 6 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the sixth part of 14 parts of the full dataset (6/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 35ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </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.