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13,117 results for “Maps”
Figure 6. An inside view of Mary and John Gray Library-Modeling, Designing, and Implementing an Avatar-based Interactive Map
<p>Figure 6 exhibits the ambience of the study environment that allows students to have group discussions, and when to access Internet, and more. The photographs have been digitized in a very realistic way.</p>
Figure 1. Mind mapping of Concepts and Versions of Micro learning (Hug, 2005)-Micro Learning: A Modernized Education System
<p>The methods of micro learning are in line with the way that the learner’s brain naturally takes in information, so that the body does not get stressed-out. One of the salient features of micro learning is that it allows the user to find exactly what he or she is looking for. When the mind focuses on a particular question, it is the most open to receiving that answer (www.digitalpromise.org/microcredentials dated on 10/10/2015). It allows the learner’s brain to explore its own curiosity and its own patterns.</p>
Fig. 2. Distribution maps. A in Revision of the genus Callipia Guenée, 1858 (Lepidoptera, Geometridae), with the description of 15 new taxa
Fig. 2. Distribution maps. A. The balteata group and Callipia paradisea Thierry-Mieg, 1904. B. The vicinaria group. C. The constantinaria group. D. The parrhasiata group. More details can be retrieved from a Google Earth kml file (Supplementary File 3).
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 9.The educational blog on Google+ Time Maps page–the weaving techniques
<p>For this subject two video films were posted on Google+ (a performance and a 3D reconstruction), slightly different from those available on the Time Maps web site, but containing the same information. The children had to make a little effort to relate this information with the one presented on the site, to make a connection between the questions, the fragments from videos at which the answers referred to and the information from the site. The set of questionnaires lead the school children through the majority of data offered by the web site regarding to the two historical periods (Figures 9, 10).</p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 14. The survey as a public posting on Google+ Time Maps page
<p>During this experimentation phase a symposium was organized at Vădastra School with the purpose to present our learning experiment to a group of 30 teachers from the Olt County. An open history lesson on the Time Maps web site was held by a history teacher, and a school girl described the Facebook page of the Vădastra School (Figure13), maintained by both teachers and children. The invited teachers gave a feedback on the effectiveness and utility of the Time Maps learning system by responding to a questionnaire-based survey, which was posted on the Google+ page (Figure 14). </p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 10. The educational blog on Google+ Time Maps page – the glass manufacturing techniques
<p>For this subject two video films were posted on Google+ (a performance and a 3D<br> reconstruction), slightly different from those available on the Time Maps web site, but containing<br> the same information. The children had to make a little effort to relate this information with the one<br> presented on the site, to make a connection between the questions, the fragments from videos at<br> which the answers referred to and the information from the site.<br> The set of questionnaires lead the school children through the majority of data offered by the<br> web site regarding to the two historical periods (Figures 9, 10).</p>
RAD-SEQ LINKAGE MAPPING AND PATTERNS OF SEGREGATION DISTORTION IN SEDGES: MEIOSIS AS A DRIVER OF KARYOTYPIC EVOLUTION IN ORGANISMS WITH HOLOCENTRIC CHROMOSOMES" in Journal of Evolutionary Biology
<p>This a data set from the paper RAD-SEQ LINKAGE MAPPING AND PATTERNS OF SEGREGATION DISTORTION IN SEDGES: MEIOSIS AS A DRIVER OF KARYOTYPIC EVOLUTION IN ORGANISMS WITH HOLOCENTRIC CHROMOSOMES" to be published in Journal of Evolutionary Biology</p>
Part of Cadastral Survey Map. Moja Mansar. PC no. 24 1934
<p>Part of Cadastral Survey Map. Moja Mansar. PC no. 24 1934. Ancient Buddhist Remains at Mansar (Dist. Nagpur) M. S.</p>
Quantitative susceptibility mapping of articular cartilage: ex vivo findings at multiple orientations and following different degradation treatments
<p>This dataset contains all the raw source data and MATLAB analysis functions that comprise the study:</p> <p><br> Quantitative susceptibility mapping of articular cartilage: Ex vivo findings at multiple orientations and following different degradation treatments</p> <p>Magnetic Resonance in Medicine | DOI: 10.1002/mrm.27216</p> <p>Nykänen Olli(1*), Rieppo Lassi(2,3), Töyräs Juha(1,4), Kolehmainen Ville(1), Saarakkala Simo(2,3,5), Shmueli Karin(6) and Nissi Mikko Johannes(1)</p> <p>(1) Department of Applied Physics, University of Eastern Finland, POB 1627, FI-70211 Kuopio, Finland<br> (2) Research Unit of Medical Imaging, Physics and Technology, University of Oulu, POB 5000, FI-90014 Oulu, Finland<br> (3) Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland<br> (4) Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland<br> (5) Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland<br> (6) Department of Medical Physics & Biomedical Engineering, University College London(UCL), London, United Kingdom</p> <p><br> *Corresponding author:<br> Olli Nykänen<br> Department of Applied Physics,<br> University of Eastern Finland<br> POB 1627<br> FI-70211, Kuopio, Finland<br> olli.nykanen@uef.fi<br> +358-50-5556357</p> <p><br> Keywords: cartilage, collagen matrix, quantitative susceptibility mapping, MRI, osteoarthritis</p> <p><br> Included folders and files are:<br> - article_figures: all figures published in the manuscript<br> - data: all MRI, histological, and PLM data used in this article<br> - matlab_functions: matlab functions used in data analysis with subfolders:<br> - aedes_plugins: plugins for aedes (http://aedes.uef.fi) for calculation of QS- and T2* maps<br> - fitting_functions: functions for fitting relaxation times or TKD-method for QSM, used by the functions in above folder<br> - miscellaneous_functions: small helper functions for a number of small tasks utilized by the other scripts and functions<br> - ReadMe.txt: this file</p> <p><br> Notes for setting up Aedes correctly for this dataset:<br> Run Aedes -> Tools -> Edit VNMR Defaults:<br> - Return: FT + K-space<br> - DC: off<br> - Zeropadding: off<br> - Sorting & fastread: on<br> - Precision: single<br> - Read_fcn: readvnmr<br> - Orient: no</p> <p>See more info in separate readme files included in each folder.</p> <p><br> (Olli Nykänen, Apr 17, 2018)</p>
MADFORWATER: WP1: Water and water-related vulnerabilities in Egypt, Morocco and Tunisia: Task1.2: Analysis and mapping of water stress, water vulnerability and potential for water reuse in Egypt, Morocco and Tunisia: Subtask1.2.b: Data collection on water stress and vulnerability: Souss-Massa Region Subset
<p>This folder contains the dataset that I used to write my conference paper "Groundwater Resources Scarcity in Souss-Massa Region and Alternative Solutions for Sustainable Agricultural Development"</p>
MATISE ("Mapping Tool for Innovation Systems Evaluation")
<p>This dataset contains the underlying data for the following publication: Nikas, A., Doukas, H., Lieu, J., Alvarez Tinoco, R., Charisopoulos, V., & van der Gaast, W. (2017). Managing stakeholder knowledge for the evaluation of innovation systems in the face of climate change. Journal of Knowledge Management, 21(5), 1013-1034, ISSN: 1367-3270<strong><em>. </em></strong>Full details of methods used to create the tool are provided within this publication<em>.</em></p>
New Ideas for Brain Modelling 4-Figure 4. LHS relates to neuron binding ensemble mass, with central column activated. RHS relates to hierarchy, with a direct mapping. The two red lines show where the ensemble is missing and so it needs to be learned. The blue lines show extra neurons from the hierarchy back to the ensemble, but can be removed as error. The other paired black squares represent where the patterns match and can oscillate together.
<p>This paper continues the research that considers a new cognitive model based strongly on the human brain, last updated in Greer (2016). In particular, it considers figure 4 of that paper (Figure below) and how it might be useful in practice. The paper also describes some new methods in the areas of image processing and behaviour simulation. The image processing introduces a most classical form of pattern cross-referencing, while the behaviour equations used feedback for a memory-type of cross-referencing. The work is all based on earlier research by the author and the new additions are intended to fit in with the overall design. For image processing, a grid-like structure is used with ‘full linking’, if you like. Each cell in the classifier grid stores a list of all other cells it gets associated with and this is used as the learned image that new input is compared with. For the behaviour metric, a new prediction equation is suggested, as part of a simulation, that uses feedback and history to dynamically determine its current state and course of action. While the new methods are from widely different topics, both can be compared with the binary-analog type of interface that is the main focus of the paper. Sensory input may be static and binary, but cross- references result in variable comparisons that make the input more dynamic. It is suggested that the simplest of linking between a tree and ensemble can explain neural binding and variable signal strengths.</p>
New Ideas for Brain Modelling 4-Figure 1. Example mapping of cells presented as an image.
<p>The author has used this structure before in Greer (2016) and it is a type of entropy classifier. It attempts to reduce the error overall and is not so concerned with minimising individual associations. The paper Greer (2017) describes a classifier that is conjecturally more visual in nature than other types and it also uses a complete linking method. Instead of several levels of feature refactoring, it is a 1-level impression only. With the image classifier, each cell stores a count of every other cell it gets associated with, when averaging this can determine what cells are most similar to the pixel in question. Figure is an example of the clustering technique. If the top LHS grid is the first image to be mapped, then for cell A1, the other black cells are recorded as shown, with a count of 1. The count would then be incremented each time a cell is recorded again, for example, after the second image, cell A3 would lose a count. The idea of linking everything this way has now been used 3 times.</p>
Mapa georreferenciado de las zonas básicas de salud de CANARIAS. Año 2017. España. || Georeferenced map of primary care area of reference in the CANARY ISLANDS. Year 2017. Spain.
<p><strong>// ES::Español</strong><br> Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma de CANARIAS, España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria.</p> <p>Los mapas están proyectados en el sistema de referencia de coordenadas geográficas.</p> <p><strong>// EN::English</strong><br> Geographical information and maps of the primary care settings of the Autonomous Community of CANARIAS, Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area.</p> <p>Maps are projected in the geographic coordinates reference system.</p>
Mapa georreferenciado de las zonas básicas de salud de MADRID. Año 2017. España. || Georeferenced map of primary care area of reference in MADRID. Year 2017. Spain.
<p><strong>// ES::Español</strong><br> Información geográfica y mapas relativos a las zonas básicas de salud de la Comunidad Autónoma de <strong>MADRID</strong>, España. Las zonas básicas de salud se refieren a las áreas de referencia de atención primaria. Cada zona básica de salud está determinada por la existencia de un Equipo de Atención Primaria, que asiste a la población de referencia de su zona desde un Centro de Salud de<br> Atención primaria.</p> <p>Los mapas están projectados en el sistema de referencia de coordenadas geográficas.</p> <p><strong>// EN::English</strong><br> Geographical information and maps of the primary care settings of the Autonomous Community of <strong>MADRID</strong>, Spain. The primary care areas of reference are configured following the availability of a Primary Care Team (health professionals) assisting reference populations' health needs from a primary care centre within<br> the area.</p> <p>Maps are projected in the geographic coordinates reference system.</p>
small EBSD map from annealed ferritic steel
<p>There are two files in this dataset: </p> <p>1 - "DX54_biaxial_ref_small.h5" containing a small Electron Backscatter Diffraction map</p> <p>2 - "Fe_2866_2866_ver3.tif" containing a simulated EBSD from alpha-Fe on a stereographic projection</p> <p> </p> <p><strong>1 - "DX54_biaxial_ref_small.h5"</strong></p> <p>A small Electron Backscatter Diffraction dataset obtained from an annealed ferritic steel and used for algorithm development and testing. The data were collected on a Zeiss Merlin SEM at 20keV using a Bruker eFlash HR (1st generation) . The crystal orientations were determined through the Bruker eSprit software. The EBSD map consists of 64 by 48 points at a step size of ~0.5 microns, and with EBSD patterns recorded at 800 by 576 pixels (ie 2x2 binning). Axis systems and Euler angle conventions used are detailed in <a href="https://dx.doi.org/10.1016/j.matchar.2016.04.008">'Crystal orientations and EBSD — Or which way is up?'</a></p> <p>The dataset is given in an HDF5 file format. You can learn more about this format from <a href="https://www.hdfgroup.org">the HDF Group</a> and download some tools including HDFView which will allow you to explore the file structure. You could also take a look at the discussion given by <a href="https://doi.org/10.1186/2193-9772-3-4">Jackson et al (2014)</a> of archival data formats for EBSD.</p> <p>This map was used by the <a href="http://users.ox.ac.uk/~ajw/">Oxford Micromechnics Group</a> in development of tools for Multivariate Statistical Analysis of EBSD and TKD Datasets. You can read details of this method in the open access <a href="https://arxiv.org/abs/1806.02087">preprint on arXiv</a>. </p> <p> </p> <p><strong>2 - "Fe_2866_2866_ver3.tif" </strong></p> <p>This image file shows dynamical diffraction theory simulation results for alpha Fe undertaken using Bruker Dynamics software tool. The Kikuchi band intensities are presented on a stereographic projection.</p> <p>This was used as the 'Master Pattern' from which a simulated EBSD pattern library was constructed and used in a template matching approach to index characteristic patterns generated from a multivariate statistical analysis of EBSD data described above.</p> <p> </p> <p>These outputs were generated as part of a project on <em>Micro-mechanical modelling techniques for forming texture, non-proportionality and failure in auto materials</em> funded by the Engineering and Physical Sciences Research Council in the UK (grant number EP/I021043/1)</p> <p> </p>
Dakar land use map at street block level
<p>This datatset contains a land use classification of Dakar (Senegal) at the street block level. It was created following the methodology presented in [1].</p> <p>Description of the files:</p> <ul> <li>"Dakar_landuse_shapefile.zip" : Shapefile of the street blocks extracted from OpenStreetMap using [2] with classification results in the attribute table.</li> <li>"Dakar_landuse_style.zip" : Files for style of the shapefile.</li> </ul> <p>Attribute table content:</p> <ul> <li>"CAT", "GID" : ID of the street block</li> <li>"PROB_ACS" : Probability to belong to class ACS</li> <li>"PROB_AGRI" : Probability to belong to class AGRI</li> <li>"PROB_BARE" : Probability to belong to class BARE</li> <li>"PROB_DEPR" : Probability to belong to class DEPR</li> <li>"PROB_PLAN" : Probability to belong to class PLAN</li> <li>"PROB_VEG" : Probability to belong to class VEG</li> <li>"FIRST_LABE" : Class with the highest classification probability</li> <li>"SEC_LABEL" : Class with the second highest classification probability</li> <li>"FIRST_PROB" : Value of the highest classification probability</li> <li>"SEC_PROB" : Value of the second highest classification probability</li> <li>"UNCERTAIN" : Difference between "FIRST_PROB" and "SEC_PROB"</li> <li>"BUILT_PERC" : Percentage of the street blocks covered by built-up (from land cover map)</li> <li>"MAP_LABEL" : Final classification label with uncertainty and different density classes</li> </ul> <p>Legend classes label:</p> <ul> <li>"AGRI" : Agricultural vegetation</li> <li>"VEG" : Natural vegetation</li> <li>"BARE" : Bare soils</li> <li>"ACS" : Non-residential built-up (administrative, commercial, services, etc.)</li> <li>"PLAN" : Planned residential built-up</li> <li>"PLAN_LD" : Planned residential low density built-up</li> <li>"DEPR" : Deprived residential built-up</li> <li>"UNCERT" : Uncertain classification</li> </ul> <p>References:</p> <p>[1] Grippa, Tais, 2018, "Mapping urban land use at street block level using OpenStreetMap, remote sensing data and spatial metrics", <em>ISPRS Int. J. Geo-Inf.</em> <strong>2018</strong>, <em>7</em>(7), 246. <a href="https://doi.org/10.3390/ijgi7070246">https://doi.org/10.3390/ijgi7070246</a> </p> <p>[2] Grippa, Tais. 2018. “Osm Street Blocks Extraction.” Zenodo. <a href="https://doi.org/10.5281/zenodo.1290637">https://doi.org/10.5281/zenodo.1290637</a>.</p> <p>Funding: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p>
Extension of Partial Gene Transcripts by Iterative Mapping of RNA-Seq Raw Reads
<p>Trinity assembled transcriptome of <em>Drosophila melanogaster</em> and <em>Osmia </em><em>bicornis</em></p>
Google Location Criteria ID (Geotargets) to Geonames mapping
<p>This deposit contains the RDF version of the Google Location Criteria ID dataset (named Geotargets, available at https://goo.gl/cZXkiJ - last access: 2018-06-29) and a subset of the Geonames XML dump.</p> <p>All datasets contain only locations located in Spain or Germany.</p> <p>There are also mapping files that contain <em>sameAs</em> links between instances described in these datasets. Links have been created with a custom SILK pipeline.</p>
Nitrogen Dioxide Sentinel-5P 16-day composite map (2018-07-10 to 2018-07-25)
<p>Nitrogen Dioxide map of Ukraine and Eastern Europe developed based on Sentinel-5P L2 images. Mean NO2 values calculated using images over the period from 2018-07-10 to 2018-07-25. Air pollution hot spots identification.</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.