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13,117 results for “Maps”
Gene/Protein BridgeDb ID Mapping Database (Ensembl Plants 49)
<p>Ensembl Plants 49 derived ID mapping database for use with BridgeDb.<br> The scripts used to create these databases based on Ensembl BioMart can be found at <a href="https://github.com/bridgedb/create-bridgedb-genedb">https://github.com/bridgedb/create-bridgedb-genedb</a>.</p> <p>This work was funded by the <a href="https://fairplus-project.eu/">FAIRplus project</a> (grant agreement no 802750) and <a href="https://www.nwo.nl/en/researchprogrammes/open-science/open-science-fund/open-science-fund-2021-awarded-grants">NWO Open Science Fund</a> (grant no <a href="https://www.nwo.nl/en/projects/203001121">203.001.121</a>).</p>
Map 3 in On the Palaearctic and Oriental species of Scymbalium and Micrillus (Coleoptera: Staphylinidae: Paederinae)
Map 3: Distributions of Micrillus indicus (EPPELSHEIM) (triangles), M. nigriceps (CAMERON) (diamonds), M. intermedius (CAMERON) (open and filled circles), and M. siamensis nov.sp. (filled circle) in the Oriental region, based on examined records.
Map 2 in On the Palaearctic and Oriental species of Scymbalium and Micrillus (Coleoptera: Staphylinidae: Paederinae)
Map 2: Distributions of Micrillus aegyptiacus (BERNHAUER) (circles), M. sudanicus ASSING (triangles), and M. aethiopicus (CAMERON) (diamonds; open diamonds unrevised records; unidentified locality "Nargi" omitted).
Map 4 in A revision of Palaearctic and Oriental Rugilus. III. Five new species from the Palaearctic region and additional records (Coleoptera: Staphylinidae: Paederinae)
Map 4: Distributions of Rugilus pygmaeus (KRAATZ) (triangles) and R. lucens ASSING (circles), based on examined records.
Map 2 in Six new species and additional records of Lathrobium from the Palaearctic region
Map 2: Type localities of Lathrobium uttaricum nov.sp. (triangle), L. hartmanni nov.sp. (circle), and L. makaluicum nov.sp. (diamond).
Mobilitätsberichterstattung Erhebungsdaten der qualitative Methoden Community Mapping und Teilnehmende Beobachtungen
<p>Als Forschungsdaten der qualitativen Methoden in der Mobilitätsberichterstattung werden die Interviewmitschnitte der Proband*innen aus den neun Community Mappings und 18 Teilnehmende Beobachtungen zur Verfügung gestellt. Aus Datenschutzgründen wurden die Interviews anonymisiert. Die Dateinamen zeigen die Personenmerkmale, aufgrunddessen die Proband*innen für die Erhebungen ausgewählt wurden. Dies umfasste eine gemischte Gruppen aus den drei Stadtraumtypen in Pankow: Innenstadt, Innenstadtrand und suburbanes Gebiet. Darüber hinaus wurden in allen drei Gebieten Schulkinder beteiligt: Primarstufe in der Innenstadt (PS), Sekundarstufe I im Innenstadtrand (ISS) und Sekundarstufe II (OSZ) im suburbanen Gebiet. Zusätzlich wurden Personengruppen mit Mobilitätseinschränkungen und besonderen Mobilitätsbedarfen beteiligt: Menschen mit Gehbehinderungen in der Innenstadt, Senior*innen im Innenstadtrand und zur Arbeit Pendelnde im suburbanen Gebiet. Über die Dateiendung "TB" wird angezeigt, dass das Interview nach einer Teilnehmenden Beobachtung mit den Proband*innen auf ihren Alltagswegen durchgeführt wurde. Diese wurden meistens auf Hinwegen am Morgen und Rückwegen am Nachmittag durchgeführt, wodurch meistens zwei Audiodateien pro Person entstanden sind, die in der Dateibezeichnung nummeriert wurden. Alle anderen Interviews wurden mithilfe des Community Mappings durchgeführt.</p> <p>Genauere Informationen zur Methodik können dem Leitfaden Mobilitätsberichterstattung und auf der Webseite <a href="http://mobilbericht.de">mobilbericht.de</a> entnommen werden. Die Ergebnisse wurden in der Erstellung des 1. Pankower Mobilitätsberichts aufgearbeitet.</p>
Fig. 10. Geographic records map. A in New species of the cuckoo bee genus Austrosphecodes Michener, 1978 (Hymenoptera: Apoidea: Sphecodini) and a key for Brazilian species
Fig. 10. Geographic records map. A. Austrosphecodes asmodeus sp. nov., A. balrog sp. nov., A. brasiliensis (Schrottky, 1910). B. A. cerberus sp. nov., A gorgon sp. nov., A. inornatus (Schrottky, 1902). C. A. jurupari sp. nov., A. krampus sp. nov., A. lucifer sp. nov. D. A. minarum (Schrottky, 1910), A. orcus sp. nov., A. tartarus sp. nov.
Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models
<p>Supporting data for <a href="https://arxiv.org/abs/2111.13736">https://arxiv.org/abs/2111.13736</a>.</p> <p>ACEhamiltonians.jl code</p> <p>This is an archived copy of the ACEhamiltonians.jl code to accompany the paper <a href="https://arxiv.org/abs/2111.13736">arXiv:2111.13736</a>.</p> <p>See <a href="https://github.com/ACEsuit/ACEhamiltoniansExamples">https://github.com/ACEsuit/ACEhamiltoniansExamples</a> for examples of how to use this code.</p> <p>The code is written in <a href="https://julialang.org/">Julia</a> and requires v1.6 or later. To install the Julia depenendencies:</p> <pre><code><code>$ cd ACEhamiltonians.jl $ julia julia> import Pkg julia> Pkg.activate(".") julia> Pkg.instantiate() </code></code></pre> <p>The scripts <code>test/plots.jl</code>, <code>test/fcc-to-bcc.jl</code> and <code>test/vacancy.jl</code> which produce all the plots in the paper can then run as, e.g.</p> <pre><code><code>julia --project=. test/plots.jl </code></code></pre> <p>Training data</p> <p>The <code>training_data</code> folder contains the atomic structure, Hamiltonian and overlap matrices stored in HDF5 format with the following schema:</p> <ul> <li>Data Group : <strong>aitb/</strong></li> <li>Datasets : <ul> <li><strong>H</strong> : Real-space Hamiltonian Matrix. Type: Float64. Shape: Tensor(# of TB Cells, # of Rows, # of Columns)</li> <li><strong>S</strong> : Real-space Overlap Matrix. Type: Float64. Shape: Tensor(# of TB Cells, # of Rows, # of Columns)</li> <li><strong>energy</strong> : Energy. Unit: eV. Type: Float64. Shape: Scalar</li> <li><strong>freeenergy</strong> : Free Energy. Unit: eV. Shape: Scalar</li> <li><strong>unitcell</strong> : Unit cell vectors. Type: Float64. Shape: Matrix(3,3)</li> <li><strong>positions</strong> : Atom positions. Type: Float64. Shape: Array(3)</li> <li><strong>forces</strong> : (Optional, if available) Forces. Type: Float64. Shape: Array(3)</li> <li><strong>metadata</strong> : JSON String including dictionary of information of FHIaims calculation (k-points, basis sets), TB Cells, Cutoff, Orbital definitions.,</li> </ul> </li> </ul> <p>The molecular dynamics and FHI-aims parameters are described in the manuscript.</p> <p>On-site models</p> <p>The <code>onsite_models_ord2</code> folder contains our correlation order 2 models for the on site blocks of the Hamiltonian, in a JSON format readable by the <a href="https://github.com/acesuit/ACE.jl">ACE.jl</a> and <a href="https://github.com/ACEsuit/ACEhamiltonians.jl">ACEhamiltonians.jl</a> Julia packages. There are separate files for the Hamiltonian (<code>*_H.json</code>) and overlap (<code>*_S.json</code>) models. The JSON files also contain training and test sets and associated errors as plotted in Figure 3 in our manuscript.</p> <p>Models have a unique identifier (UUID) which is a hash of the input parameters and training data. The mapping from (order, max_degree) to UUID is as follows:</p> <pre><code><code>(2,4) - 13427527590286463256 (2,5) - 10538156191357510769 (2,6) - 1646489440533135164 (2,7) - 12130775482127724115 (2,8) - 12487060958610974041 (2,9) - 2653067664384673997 (2,10) - 1143382251563115664 (2,11) - 4564001820340015372 (2,12) - 9474261500251782658 </code></code></pre> <p>Off-site models</p> <p>The <code>offsite_models_ord1</code> and <code>offsite_models_ord2</code> folders contain our order 1 and order 2 offsite models for Hamiltonian and overlap matrices. The mapping from (H_order, H_max_degree) + (S_order, S_max_degree) to UUID is as follows:</p> <pre><code><code>(1,6) + (1,8) - 7014526518680934587 (1,7) + (1,9) - 8594416159488562244 (1,8) + (1,10) - 10204186688118368371 (1,9) + (1,11) - 13078304848585360574 (1,10)+ (1,12) - 14750835312950641338 (1,11)+ (1,13) - 9883802224093245794 (1,12)+ (1,14) - 3907899412408606585 (1,13)+ (1,15) - 201683837542179657 (1,14)+ (1,16) - 277744202775070779 (2,6) + (1,8) - 4699475053563592071 (2,7) + (1,9) - 489637409713831432 (2,8) + (1,10) - 18034631670613263469 (2,9) + (1,11) - 720654516759450160 (2,10)+ (1,12) - 15214900801060024044 (2,11)+ (1,13) - 13798832597295943078 (2,12)+ (1,14) - 13162803789413134473 </code></code></pre> <p>FCC only</p> <p>Onsite models:</p> <pre><code><code>2 6 5311732756869418284 2 7 13030014632886405308 2 8 5820099621734447846 2 9 10161014511878227635 2 10 11298425190201843107 2 11 9932031839231628354 2 12 9447261873515969583 </code></code></pre> <p>Optimised FCC model <code>16110190062237887798</code></p> <p>BCC only</p> <p>Onsite models:</p> <pre><code><code>2 6 8949023800586845770 2 7 8045797268444730200 2 8 6919809282139600809 2 9 9935027806122780319 2 10 6376963380608532713 2 11 5001375576268070883 2 12 9678585765722197901 </code></code></pre> <p>Optimised BCC model <code>10293566074413000591</code></p> <p>FCC+BCC optimised models</p> <p>Onsite models</p> <pre><code><code>2 6 2154760103892646619 2 7 6450474921309693835 2 8 14227277988574899288 2 9 476820595195218567 2 10 5364136683220082110 2 11 14619519825012606580 2 12 14181614899005838824 </code></code></pre> <p>Offsite FCC+BCC optimised model - <code>4570230078043807257</code></p> <p>Model errors</p> <p>The <code>model_errors</code> directory contains summarised model errors for the training and testing errors for the models listed above.</p> <p>Reference data</p> <p>Reference electronic structure data computed for the BCC and FCC crystals, along the Bain path and for the relaxed vacancy is stored in the <code>reference_data</code> folder. The Hamiltonian and overlap matrices are stored as compressed binary HDF5 files. The format and metadata can be viewed with the <code>h5dump</code> utility, or read in using the supplied Julia code (or indeed from other languages).</p> <p>Predicted data</p> <p>The <code>predicted_data/FCC</code> and <code>predicted_data/BCC</code> folders contain HDF5 files with the results of all model predictions shown in the manuscript on the FCC and BCC crystal structures. <code>predicted_data/FCC-to-BCC</code> contains the results of predictions along the Bain path with the optimized model described in the manuscript and <code>predicted_data/vacancy</code> contains the vacancy calculations.</p>
A 5000 km2 ASTER alteration map of the Oman–UAE ophiolite crust: Data archive and remote sensing toolkit
<p>This archive contains data and maps accompanying the journal article <em>"Multispectral discrimination of spectrally similar hydrothermal minerals in mafic crust: A 5000 km<sup>2</sup> ASTER alteration map of the Oman–UAE ophiolite</em>".</p> <p>The archive includes the full resolution, multi-format alteraton maps of hydrothermal alteration of the entire Oman–UAE ophiolite crust generated by ASTER remote sensing. Additional files necessary to reproduce or build on this work are also provided, constituting a remote sensing toolkit for the Oman–UAE ophiolite. A complete list of contents is provided within. Please contact TMB in case of compatibility issues.</p>
Fig. 1. Map showing sampling localities during scientific expeditions carried out along the Brazilian continental shelf and slope area from 1999 in New insights gained from museum collections: new deep-sea species of Typhlotanais (Tanaidacea, Typhlotanaidae) from Brazil
Fig. 1. Map showing sampling localities during scientific expeditions carried out along the Brazilian continental shelf and slope area from 1999 to 2016. Abbreviations: ES = Espírito Santo; RJ = Rio de Janeiro.
Ethiopia – Distribution of Mother-Tongues – a rare 1976 map uncovered
<p>As part of our study on historical cartography and ethno-linguistic maps in the Horn of Africa and Western Tigray, we discovered the language map for this book, which contains significant information on the geographic region where the Tigrinya language is spoken: "<em>Language in Ethiopia”, by M. L. Bender, J. D. Bowen, R. L. Cooper and C. A. Ferguson, published by Oxford University Press in 1976.</em></p> <p>With regard to Western Tigray, on the map, the Tigrinya-speaking area extends from the Sudanese border up to the Rift Valley escarpment at the east. Two major rivers cross the Tigrinya language area, the Mereb at north and the Tekeze in the southern part. The same language community lives on both sides of the rivers. In addition to Tigrinya, the map also shows that Kunama is spoken on the southern shores of the lower Tekeze River.</p> <p>The extent of the Tigrinya language on this map is in line with the Tigrinya-speaking area shown on all-but-one other language maps (often prepared by senior linguists), which are further detailed in a parallel dataset (Nyssen, J. 2022. Database: Western Tigray in 96 historical and ten ethno-linguistic maps (1607-2009): Zenodo. <a href="https://doi.org/10.5281/zenodo.6557918">https://doi.org/10.5281/zenodo.6557918)</a>.</p>
Peatland maps and wetland GHG emission factors
<p>The data set includes maps of degraded (~46 Mha globally) and intact peatland (~375 Mha globally) for the year 2015. The spatial resolution is 0.5 degree. The data set also includes IPCC wetland GHG emission factors for degraded and rewetted peatlands.</p> <p>This dataset has been published originally as supplementary data set in </p> <p>Humpenöder, F., Karstens, K., Lotze-Campen, H., Leifeld, J., Menichetti, L., Barthelmes, A., and Popp, A. (2020). Peatland protection and restoration are key for climate change mitigation. Environ. Res. Lett. <em>15</em>, 104093. DOI <a href="https://10.1088/1748-9326/abae2a">10.1088/1748-9326/abae2a</a>.</p> <p> </p>
Data in support of "An exploration of linkage fine-mapping on sequences from case-control studies"
<p>These data were simulated for an exploration of linkage fine-mapping on sequences from case-control studies. The code to generate and analyze the data is available on GitHub in the scripts at <a href="https://github.com/SFUStatgen/PBJ0">https://github.com/SFUStatgen/PBJ0</a>. Queries may be directed to Payman Nickchi at <a href="mailto:pnickchi@sfu.ca">pnickchi@sfu.ca</a> or Charith (Bhagya) Karunarathna at <a href="mailto:ch757276@dal.ca">ch757276@dal.ca</a>.</p>
Surface Water Maps of Pune District in India
<p>Many applications that target water resource management requires detection, monitoring and assessment of water for quantity and quality on regular basis. The advancement in remote sensing have led in new era in the detection of surface water with its changing dynamics. We processed cloud free images for year 2016 acquired by Landsat 8 OLI (Path : 147, Row: 47) as available from from <a href="http://earthexplorere.usgs.gov/">http://earthexplorere.usgs.gov</a>. The processing includes calculation of Top of Atmospheric reflectance (ToA) and derived Modified Normalized Differential Water Index (MNDWI) images. We have generated in-land surface water maps for all months excluding rainy seasons for the Pune district in India. The derived water maps were compared with a reference surface water map, Global Surface Water Explorer (GSWE), prepared by The European Commission’s Joint Research Centre in the framework of the Copernicus Programme. The kappa coefficient between derived maps and GSWE image were found to be in the range of 0.52 to 0.96 with an average agreement of 0.82 indicating strong level of agreement.</p>
2d adaptive extinction maps for the VVV footprint
<p><strong>2d adaptive resolution extinction maps for the VVV footprint</strong></p> <p>Extinction maps associated with the publication <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220510378S">Sanders et al. (2022, MNRAS)</a> </p> <p>Three maps are provided: E(J-Ks), E(H-Ks) and E(H-[4.5]). The E(J-Ks) and E(H-Ks) maps have been computed over the entire VVV footprint whilst the E(H-[4.5]) map is only computed for the inner -1.5<l<1.5, -1.5<b<1.5. To convert to AKs multiply by 0.449, 1.293, 0.700 respectively (or otherwise for other extinction laws).</p> <p>The files *.csv.gz give the colour excess (e*) and its spread (sigma_e*) at a set of Healpix labelled by their unique index. A series of Healpix resolutions have been used to provide higher resolution where needed (except for the E(H-[4.5]) map that is only at level=13). The indices are using the nested scheme given the Galactic coordinates (l,b). In this way, it is simple to handle the varying resolution (see <a href="https://ivoa.net/documents/MOC/">https://ivoa.net/documents/MOC/</a>).</p> <p>The colour excesses have been found from Gaussian fits to the red clump colours for (J-Ks) and (H-Ks) and an average over all giant stars for (H-[4.5]) (accounting for the weak gradient of the giant branch in the (H-[4.5]) vs. Ks colour-magnitude space). The spreads in extinction come from the width of the Gaussian peak for (J-Ks) and (H-Ks) and the width of the full distribution for (H-[4.5]) after subtracting the average photometric uncertainties and accounting for a (0.05,0.02,0.00) intrinsic colour width for (J-Ks, H-Ks, H-[4.5]) respectively.</p> <p>The provided file extinction_maps.py provides a class for reading in all extinction maps (version=JK,HK,H45 allows one to pick the required map) and querying the colour excess and its spread for large numbers of Galactic coordinates. Also there is functionality for finding the resolution of the map at a given location.</p> <p>The queries will throw a warning but return a value if the coordinate is outside the reliable footprint (the entire VVV footprint for JK and HK and the inner -1.5<l<1.5, -1.5<b<1.5 for H45).</p> <p>The example.ipynb notebook shows an example of querying the extinction map and plotting the result.</p> <p> </p> <p> </p>
Apertif drift scan based compound beam maps (1220-1520 MHz)
<p>Apertif (APERture Tile In Focus) is a Phased Array Feed (PAF) system on the Westerbork Synthesis Radio Telescope (WSRT) that conducted the Apertif legacy surveys between the 1st of July 2019 and the 28th of February 2022. Apertif carried out a two tiered imaging survey, with a shallow, wide-area and a medium-deep, small-area component, and a time domain survey. In the standard observing mode for imaging 40 partially overlapping compound beams (CBs) are formed simultaneously in a rectangular configuration. Each of these 40 CBs have a unique shape that differs substantially from the "old" WSRT beam shape.</p> <p>This data set contains Apertif drift scan based CB maps measured at the second Apertif imaging survey frequency setting (1220-1520 MHz). Apertif imaging observations were take at these frequencies between January 2021 and February 2022. The CB maps are generated from drift scan observations using the AperPB scrips (DOI: https://doi.org/10.5281/zenodo.6544109). These CB maps can be used to primary beam correct or mosaic images or data cubes from the Apertif imaging surveys.</p> <p>The dataset contains two types of fits files:</p> <p>1) Reconstructed CB maps from the drift scans. These maps are within directories called "drift_maps" and have the following naming scheme: <source>_<CB map ID>_<CB number>_<polarisation>.fits. The source refers to the continuum source used for the drift scan observations, this can be either CygA or CasA. The CB map ID is the identification of a set of CB maps based on the observing date in the format of yymmdd e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents (numbering from 00 to 39). Polarisation denotes which polarisation the map shows, these can be xx, yy, I and diff, where diff refers to xx-yy. These maps are 3.36 × 2.3 deg in size and are oriented upside down compared to the sky.</p> <p>2) Spline fitted CB maps. These are within directories called "beam_models" and have the following naming: <CB map ID>_<CB number>_<polarisation>_model.fits. The CB map ID is the identification of a set of CB maps based on the observing date e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents. Polarisation denotes which polarisation the map shows, these can be xx, yy, I. These maps are 1.1 × 1.1 deg in size and are subdivided into 18 frequency bins in directories called "channel_<num>". These maps are suitable for primary beam correcting or mosaicing Apertif images and data cubes. Since the CB shapes of Apertif change over time, we recommend to use the closest CB data set in time to the observation that the primary beam correction or mosaicing is going to be applied to. </p> <p>All CB data sets also contain per antenna CB maps and spline fitted CB maps within the directory "per_antenna_maps". The naming and organisation of the per antenna maps is the same as described above, with the exception that the spline fitted CB maps are not subdivided by frequency bins into separate directories, but are contained within a single fits file. The Apertif system uses 12 of the 14 WSRT antennas. In some cases one or two antennas were malfunctioning during the observations and there are only 10 or 11 per antenna CB map sets. </p> <table> <caption>Notes on the individual CB data sets</caption> <tbody> <tr> <td>CB map ID</td> <td>continuum source</td> <td>notes</td> </tr> <tr> <td> <p>210205</p> </td> <td>Cyg A</td> <td> <p>RFI in channels 13-15 (1.46-1.48 GHz)</p> </td> </tr> <tr> <td> <p>210402</p> </td> <td>Cas A</td> <td> <p>RFI in channels 13-15 (1.46-1.48 GHz), no RTC and RTD, spline fit for CB01 failed for the per antenna maps</p> </td> </tr> <tr> <td> <p>211103</p> </td> <td>Cas A</td> <td> <p>RFI in channels 13-15 (1.46-1.48 GHz), spline fit for CB01 failed for the per antenna maps</p> </td> </tr> <tr> <td> <p>211118</p> </td> <td>Cyg A</td> <td> <p>RFI in channels 13-15 (1.46-1.48 GHz), spline fit for CB01 failed for the per antenna maps</p> </td> </tr> </tbody> </table> <p>For a detailed description of the characteristics of the Apertif CBs and how the CB maps are produced see the upcoming publication Dénes et al. 2022 submitted to A&A (ArXiv: https://arxiv.org/abs/2205.09662).<br> <br> For more information on the Apertif system see: van Cappellen et al. 2022, A&A, 658, 146 DOI: https://doi.org/10.1051/0004-6361/202141739</p>
Apertif drift scan based compound beam maps (1130-1430 MHz)
<p>Apertif (APERture Tile In Focus) is a Phased Array Feed (PAF) system on the Westerbork Synthesis Radio Telescope (WSRT) that conducted the Apertif legacy surveys between the 1st of July 2019 and the 28th of February 2022. Apertif carried out a two tiered imaging survey, with a shallow, wide-area and a medium-deep, small-area component, and a time domain survey. In the standard observing mode for imaging 40 partially overlapping compound beams (CBs) are formed simultaneously in a rectangular configuration. Each of these 40 CBs have a unique shape that differs substantially from the "old" WSRT beam shape.</p> <p>This data set contains Apertif drift scan based CB maps measured at the first Apertif imaging survey frequency setting (1130-1430 MHz). Apertif imaging observations were take at these frequencies between July 2019 and January 2021. The CB maps are generated from drift scan observations using the AperPB scrips (DOI: https://doi.org/10.5281/zenodo.6544109). These CB maps can be used to primary beam correct or mosaic images or data cubes from the Apertif imaging surveys.</p> <p>The dataset contains two types of fits files:</p> <p>1) Reconstructed CB maps from the drift scans. These maps are within directories called "drift_maps" and have the following naming scheme: <source>_<CB map ID>_<CB number>_<polarisation>.fits. The source refers to the continuum source used for the drift scan observations, this can be either CygA or CasA. The CB map ID is the identification of a set of CB maps based on the observing date in the format of yymmdd e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents (numbering from 00 to 39). Polarisation denotes which polarisation the map shows, these can be xx, yy, I and diff, where diff refers to xx-yy. These maps are 3.36 × 2.3 deg in size and are oriented upside down compared to the sky.</p> <p>2) Spline fitted CB maps. These are within directories called "beam_models" and have the following naming: <CB map ID>_<CB number>_<polarisation>_model.fits. The CB map ID is the identification of a set of CB maps based on the observing date e.g. 190628. The CB number refers to which of the 40 Apertif CBs the map presents. Polarisation denotes which polarisation the map shows, these can be xx, yy, I. These maps are 1.1 × 1.1 deg in size and are subdivided into 18 frequency bins in directories called "channel_<num>". These maps are suitable for primary beam correcting or mosaicing Apertif images and data cubes. Since the CB shapes of Apertif change over time, we recommend to use the closest CB data set in time to the observation that the primary beam correction or mosaicing is going to be applied to. </p> <p>Wit the exception of two datasets (191008 and 191023), all CB data sets also contain per antenna CB maps and spline fitted CB maps within the directory "per_antenna_maps". The naming and organisation of the per antenna maps is the same as described above, with the exception that the spline fitted CB maps are not subdivided by frequency bins into separate directories, but are contained within a single fits file. The Apertif system uses 12 of the 14 WSRT antennas. In some cases one or two antennas were malfunctioning during the observations and there are only 10 or 11 per antenna CB map sets. </p> <p>Notes on the individual CB data sets:</p> <table> <tbody> <tr> <td>CB map ID</td> <td>continuum source</td> <td>notes</td> </tr> <tr> <td>190628</td> <td>Cyg A</td> <td>issue in channel 2 (1.31 GHz) with CBs 15-16,<br> issue in channels 5-6 (1.36 - 1.37 Ghz) and 8-9 (1.4 - 1.41 GHz), spline fit for CBs 01-07 failed for the per antenna maps</td> </tr> <tr> <td>190722</td> <td>Cyg A</td> <td>RFI in chan 6 (1.37 GHz) for CB 1</td> </tr> <tr> <td>190821</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>190826</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>190912</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>190916</td> <td>Cyg A</td> <td> </td> </tr> <tr> <td>191008</td> <td>Cyg A</td> <td>CBs 14, 19, 20 and 26 are distorted, no per antenna maps</td> </tr> <tr> <td>191023</td> <td>Cyg A</td> <td>RFI in channel 6 ( 1.37 GHz) for CBs 0, 3-4, 9-10, 15-17, 22, no per antenna maps</td> </tr> <tr> <td>191120</td> <td>Cyg A</td> <td>RFI in channels 8-9 (1.4 - 1.41 GHz)</td> </tr> <tr> <td>200130</td> <td>Cyg A</td> <td>no RT5, RFI in channel 6 (1.37 GHz) for CBs 1, 6-8, 14, 21, 33</td> </tr> <tr> <td>200430</td> <td>Cyg A</td> <td>no RTC and RTD</td> </tr> <tr> <td>200710</td> <td>Cyg A</td> <td>no RTB, CBs 0, 20-39 are affected by RFI</td> </tr> <tr> <td>200819</td> <td>Cyg A</td> <td>issue with CBs 37-39, RFI in channel 6 (1.37 GHz), spline fit for CB01 failed for the per antenna maps</td> </tr> <tr> <td>201009</td> <td>Cyg A</td> <td>no RTB, antenna 9 (RTB) files are empty in the antenna based maps</td> </tr> <tr> <td>201028</td> <td>Cas A</td> <td>spline fit for CB01 failed for the per antenna maps</td> </tr> <tr> <td>201218</td> <td>Cyg A</td> <td> </td> </tr> </tbody> </table> <p>For a detailed description of the characteristics of the Apertif CBs and how the CB maps are produced see the upcoming publication Dénes et al. 2022 submitted to A&A (ArXiv: https://arxiv.org/abs/2205.09662).<br> <br> For more information on the Apertif system see: van Cappellen et al. 2022, A&A, 658, 146 DOI: https://doi.org/10.1051/0004-6361/202141739</p>
Data in support of "An exploration of linkage fine-mapping on sequences from case-control studies"
<p>These data were simulated for an exploration of linkage fine-mapping on sequences from case-control studies. The scripts to generate and analyze the data are available at <a href="https://github.com/SFUStatgen/PBJ0">https://github.com/SFUStatgen/PBJ0</a>. Queries may be directed to Payman Nickchi at <a href="mailto:pnickchi@sfu.ca">pnickchi@sfu.ca</a> or Charith (Bhagya) Karunarathna at <a href="mailto:ch757276@dal.ca">ch757276@dal.ca</a>.</p> <p><strong>README file for All_data directory</strong></p> <p><strong>Directory structure</strong></p> <p>The All_data directory consists of this README file and 500 sub-directories named DatasetX, for X=1 to 500. Within each DatasetX sub-directory are further sub-directories named alt and null containing files named pop_data.RData and sample_data.RData.</p> <p><strong>alt <em>versus</em> null directories</strong></p> <p>The files in the alt and null directories contain the same variant data but different phenotype data. In particular, under the null hypothesis, disease status is simulated at random according to a 5% prevalence in the population, whereas under the alternative hypothesis disease status is simulated according to a penetrance model that depends on causal SNVs. The R script to simulate data<br> under the alternative hypothesis is in the file 1_SimulateData.R in the Github repository <a href="https://github.com/SFUStatgen/PBJ0">https://github.com/SFUStatgen/PBJ0</a>.</p> <p><strong>pop_data.RData and sample_data.RData files</strong></p> <p>The data structures contained in the pop_data.RData and sample_data.RData files are described below. The structure is the same under both the null and alternative hypothesis.</p> <p><strong>pop_data.RData</strong></p> <p>From R, load("pop_data.RData") loads a list named pop_data whose elements describe the population’s haplotype and phenotype data. The list elements are as follows.</p> <ul> <li>Variants: a matrix of variants for the population of 6200 haplotypes <ul> <li>rows are SNVs,</li> <li>columns are sequences</li> </ul> </li> <li>Positions: a data frame of SNV positions <ul> <li>rows are SNVs,</li> <li>column 1 is the SNV name and column 2 is the SNV position in base pairs</li> </ul> </li> <li>Population.Mapping: a data frame telling us how the sequences are paired into individuals <ul> <li>rows are individuals</li> <li>First column 1 is an individual ID from 1,…,3100; columns 2 and 3 are the sequence IDs of the first and second sequence for that individual where the sequence IDs are the column names of the Variants matrix.</li> </ul> </li> <li>Genotype.Matrix: a matrix of genotypes (i.e. variant counts) for the 3100 individuals <ul> <li>rows are SNVs</li> <li>columns are the individuals</li> </ul> </li> <li>causal_region: a vector containing the lower- and upper-limit of the causal region in base pairs.</li> <li>cSNV: a vector containing the IDs of the causal SNVs, where the SNV IDs are the row names of the Variants matrix.</li> <li>DISCRETE: a list with the following elements. <ul> <li>CaseIndividuals: vector of IDs of the affected individuals in the population.</li> <li>ControlIndividuals: vector of IDs of the unaffected in the population.</li> <li>BinaryTrait: a vector of trait status (0=unaffected, 1=affected) for each individual.</li> </ul> </li> </ul> <p><strong>Note:</strong> Within the same DatasetX directory, the only difference between the pop_data data structures under the null and alternative hypothesis is the phenotype information contained in their respective DISCRETE list elements. Both the null and alternative pop_data data structure share list elements: Variants, Positions, Population.Mapping, Genotype.Matrix, causal_region and cSNV.</p> <p><strong>sample_data.RData</strong></p> <p>From R, load("sample_data.RData") loads a list whose elements describe the sequences and phenotypes of the sample of 50 affected individuals (cases) and 50 unaffected individuals (controls) from the population.</p> <ul> <li>Haps: a list with two elements. <ul> <li>sample_haps: a matrix of 200 sequences for the 50 cases and 50 controls. Rows are SNVs and columns are sequences, with the sequences of sampled cases appearing first (i.e. first 100 columns), followed by the sequences of sampled controls (i.e. last 100 columns). Sequences include only those SNVs that are polymorphic in the sample.</li> <li>ccStatus: a vector indicating the case/control status of the individual to which the sequence belongs, with case=1 and control=0.</li> </ul> </li> <li>Genos: a list with two elements. <ul> <li>sample_genos: a matrix of 100 genotypes for the 50 cases and 50 controls. Rows are SNVs and columns are genotypes, with genotypes of cases appearing first, followed by genotypes of controls.</li> <li>ccStatus: a vector indicating the case/control status of each individual, with case=1 and control=0.</li> </ul> </li> <li>Posn: a data frame of SNV positions for each SNV that is polymorphic in the sample. The first column is the SNV name and the second is the SNV position in base pairs. Posn is a subset of pop_data$Positions.</li> <li>poly_cSNV: a vector of IDs for causal SNVs that are polymorphic in the sample.</li> <li>CaseIND: a vector of individual IDs for the case individuals (see pop_data$Population.Mapping).</li> <li>ControlIND: a vector of individual IDs for the control individuals (see pop_data$Population.Mapping).</li> <li>CaseHapID: a vector of IDs for the sequences that belong to cases (see the sequence IDs in the column names of the matrix pop_data$Variants).</li> <li>ControlHapID: a vector of IDs for the sequences that belong to controls (see the sequence IDs in the column names of the matrix pop_data$Variants).</li> </ul> <p> </p>
Building fraction map of Germany (Sentinel-1/-2 based, 10m and 100m resolution)
<p>This dataset features a map of building fractions (as opposed to built-up fractions including other impervious surfaces such as roads) for Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. The data were created by using machine learning regression and spectral unmixing, using synthetically mixed training data. The dataset is completely based on freely accessible satellite imagery, and was validated with freely available building footprint reference data for three federal states.</p> <p>We recommend to use data at an aggregated resolution of 20m, 50m, or 100m, and to clip data at about 20% building fraction when using 10m resolution maps (or roughly the corresponding RMSE at any other resolution).</p> <p><strong>Temporal extent</strong><br> Used Sentinel-2 data were acquired in 2018, and Sentinel-1 data were acquired in 2017 (see publication). The map is, thus, representative for 2017/2018. Validation results can be affected by building footprint reference data from different years.</p> <p><strong>Data format</strong><br> The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Building fraction values are in percent, from 0 to 100. In the original dataset with 10m spatial resolution, fraction values are equivalent to area in m². In the aggregated dataset with 100m spatial resolution, the values must be multiplied with 100 in order to see area in m².</p> <p><strong>Further information</strong><br> For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de). A web-visualization of this dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/building-area/">here</a>.</p> <p><strong>Publication</strong><br> Schug, F.; Frantz, D.; Okujeni, A.; Hostert, P. (2022). Sub-pixel building area mapping based on synthetic training data and regression-based unmixing using Sentinel-1 and -2 data. Remote Sensing Letters. DOI: 10.1080/2150704X.2022.2088253</p> <p><strong>Acknowledgements</strong><br> The dataset was generated by FORCE v. 3.6.1 (<a href="https://doi.org/10.3390/rs11091124">paper</a>, <a href="https://github.com/davidfrantz/force">code</a>), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the <a href="https://scihub.copernicus.eu/">European Space Agency and the European Commission</a>. Sentinel-1 data were provided by <a href="https://eodc.eu/">EODC</a>. We thank the providers of the building footprint reference data (see publication).</p> <p><strong>Funding</strong><br> This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (<a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">MAT_STOCKS</a>, grant agreement No 741950).</p> <p> </p>
Fig. 1. A in Mapping the biosphere: Exploring species to understand the origin, organization and sustainability of biodiversity
Fig. 1. A partial list of the trans-disciplinary expertise required to plan, undertake and complete an inventory and mapping of Earth's flora and fauna and three examples of scientific and engineering domains advanced by the resulting knowledge of the biosphere's species and their properties, relationships and distributions.
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
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.