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13,117 results for “Maps”
MicrobiomeSupport Mapping Data
<p>This dataset is the result of a mapping exercise of the online database containing relevant information on microbiome-related policies and strategies, funding programmes, research projects and organisations. This included collecting research programmes and projects from (1) the full H2020 database on Cordis, (2) a database with research projects funded via ERA-NETs and Joint Programming Initiatives, (3) COST Action research networks, (4) European Molecular Biology Organization projects, (5) the Horizon Frontier Science Programme, (6) the Bill and Melinda Gates Foundation, and (7) the Gordon and Betty Moore Foundation as well as relevant new policies, research strategies and infrastructures and knowledge platforms. </p> <p>The database is accessible on the project website: <a href="https://www.microbiomesupport.eu/microbiome-research-database/">LINK</a></p>
Fig. 21. Distribution maps. A in A systematic revision of the genus Juga from fresh waters of the Pacific Northwest, USA (Cerithioidea, Semisulcospiridae)
Fig. 21. Distribution maps. A. Juga newberryi (I. Lea, 1860). B. Juga nigrina (I. Lea, 1856). C. Juga occata (Hinds, 1844). Red stars, type localities; black dots, sequenced specimens; gray dots, unsequenced museum material. Abbreviations: CA = California; NV = Nevada; OR = Oregon; WA = Washington.
Fig. 7. Distribution maps. A in A systematic revision of the genus Juga from fresh waters of the Pacific Northwest, USA (Cerithioidea, Semisulcospiridae)
Fig. 7. Distribution maps. A. Juga plicifera (I. Lea, 1838). B. Juga acutifilosa (Stearns, 1890). C. Juga bulbosa (A. Gould, 1847). Red stars, type localities; black dots, sequenced specimens; gray dots, unsequenced museum material. Abbreviations: CA = California; NV = Nevada; OR = Oregon; WA = Washington.
A characterization of cardiac-induced noise in R2* maps of the brain
<p>A subset of data used in the original publication for computations of the results. These data contain 5D k-space data (kx/ky/kz/cardiac phase/TE) from one of the participants and can be used to perform the cardiac-induced noise characterization and retrieve the results from the paper.</p>
Data from "Mapping Protoplanetary Disk Vertical Structure with CO Isotopologue Line Emission"
<p>CO isotopologue line emission image cubes ("[DISK]_[LINE]_cube.fits"), line+continuum image cubes ("[DISK]_[LINE]_cube_wcont.fits"), and zeroth moment maps ("[DISK]_[LINE]_M0.fits") associated with Law et al., 2023, "Mapping Protoplanetary Disk Vertical Structure with CO Isotopologue Line Emission," The Astrophysical Journal</p> <p>The raw data are available on the ALMA archive (see Table 2 in the paper for a listing of the relevant ALMA project codes).</p>
Io hot spots map derived by Juno/JIRAM orbits: 10, 11, 16, 17, 18, 20, 24, 25, 26, 27, 32, 33
<p>Io hot spots map GIS shapefile derived by Juno/JIRAM orbits: 10, 11, 16, 17, 18, 20, 24, 25, 26, 27, 32, 33</p>
Mapping the Swiss Landscape of Diamond Open Access Journals. The PLATO Study on Scholar-Led Publishing. Dataset
<p><strong>Context</strong><br> From March to September 2022, the <a href="https://www.openscience.uzh.ch/en/openaccess/plato.html">«Platinum Open Access Funding» Project (PLATO)</a>, in collaboration with the Institute for Applied Data Science & Finance at the Bern University of Applied Sciences, undertook a bibliometric and empirical study of the Platinum/Diamond open access journal landscape in Switzerland. The PLATO project is an initiative of six Swiss universities – the University of Zurich, the University of Bern, the University of Geneva, the University of Neuchâtel, the Zurich University of the Arts and ETH Zurich –, dedicated to furthering community-led scholarly publishing in Switzerland. Diamond open access stands for a concept of equitable open access to and participation in scholarly publishing that is free for both authors and readers.</p> <p><strong>Presentation</strong><br> The main objective of the PLATO Study was to gain insight into the Platinum/Diamond open access publishing ecosystem in Switzerland through a mixed-method approach. The study consisted of three parts: First, bibliometric data were combined with inputs from Swiss open access publishers, institutional open access experts as well as information on journal websites to identify Swiss Diamond OA journals and their main characteristics. Second, seven semi-structured interviews with editors of select Diamond OA journals were conducted to generate a thorough understanding of their workflows, infrastructures, business models, challenges and opportunities. Third, based on the inputs from the interviews, three surveys were designed and sent to authors/reviewers, editors, and representatives of hosting and funding institutions of Swiss Diamond OA journals.</p> <p>The results of the study are published in the form of the following outputs:</p> <ul> <li>DOI Report: 10.5281/zenodo.7461728</li> <li>DOI Bibliometric List: <a href="https://zenodo.org/record/6992615#.YzK3ElJBw-Q">10.34914/olos:l2tys6tie5f63h35lqpqzzlx24</a></li> <li>DOI Data Set: 10.5281/zenodo.7461754</li> </ul> <p>The Data Set comprises the following files:</p> <ul> <li>Survey questionnaires</li> </ul> <p> SurveyQuestionnaire_Author.pdf<br> SurveyQuestionnaire_Editor.pdf<br> SurveyQuestionnaire_Publisher.pdf</p> <ul> <li>Data collected in three survey studies addressing journal editors, authors/reviewers as well as representatives of hosting and funding institutions:</li> </ul> <p> StudyData_Author.csv<br> StudyData_Editor.csv<br> StudyData_Publisher.csv</p> <ul> <li>Codebooks explaining the coding of the survey data files:</li> </ul> <p> StudyCodebook_Author.pdf<br> StudyCodebook_Editor.pdf<br> StudyCodebook_Publisher.pdf</p>
Data set: historical and ethno-linguistic maps covering Western Tigray (1607-2014)
<p>Excerpts of 142 co-eval historical and ethno-linguistic maps covering Western Tigray (Ethiopia).</p> <p>Original data, used to prepare the article</p> <p>Nyssen, J., Biadgilgn Demissie, 2023. Administrative and ethno-linguistic boundaries of Western Tigray (Ethiopia) since 1683. Journal of Maps.</p>
Gene/Protein BridgeDb ID Mapping Database (Ensembl Metazoa 52)
<p>Mapping databases derived from Ensembl Metazoa 52. These files can be used with BridgeDb.<br> The scripts which were 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>
Sri Ksetra, Bago, Myanmar. Map of Sri Ksetra, phase 4.
<p>Sri Ksetra, Bago, Myanmar. Map of Sri Ksetra, phase 4.</p>
Aragón (Spain) forest map around interurban roads
<p>Shapefile that contain the forest map of a 500-meter buffer surrounding interurban roads of Aragón (Spanish autonomous region) with relevant information such as land use. Original data source is the The National Geographic Institute of Spain - IGN. If you are interested in any other Spanish region or the whole country, please do not hesitate to contact me and I will forward it to you.</p> <p>The context is the Final Master's Degree Project 'Analysis and Predictive Modelling of Wildlife–Vehicle Collision on Interurban Roads in Spain' (Data Science Master’s Degree of Universitat Oberta de Catalunya - UOC).</p> <p>This dataset is the output of the land use analysis and the <a href="https://github.com/alba620/analisis-prediccion-accidentes-trafico-animales">code repository</a> is available on GitHub.</p>
Text-fig. 1. Locality map and Keshin Formation section at Cape Tsvetkov, East Taimyr (after Kazakov et al. 2002). 1 – tuff conglomerate, 2 – sandstone, 3 – grained siltstone, 4 – siltstone, 5 – mudstone, 6 – foraminifers, 7 – conchostracans, 8 – plant megafossils, 9 – locality of described plants, 10 – Tsvetkov Cape (East Taimyr). in Taimyria Gen. Nov., A New Genus Of Evolutionary Advanced Gymnosperms From Triassic Of The Taimyr Peninsula, Siberia, Russia
Text-fig. 1. Locality map and Keshin Formation section at Cape Tsvetkov, East Taimyr (after Kazakov et al. 2002). 1 – tuff conglomerate, 2 – sandstone, 3 – grained siltstone, 4 – siltstone, 5 – mudstone, 6 – foraminifers, 7 – conchostracans, 8 – plant megafossils, 9 – locality of described plants, 10 – Tsvetkov Cape (East Taimyr).
CLAMATO DR2: IGM Lyman-Alpha Forest Tomography Survey Public Data Release of Spectra and Maps
<p> </p><p><strong>CLAMATO Data Release 2</strong></p><p>Updated 2023 Jan 11th by Khee-Gan Lee (<a href="mailto:kglee@ipmu.jp">kglee@ipmu.jp</a>)</p><p>Supporting paper has been published in <a href="https://ui.adsabs.harvard.edu/abs/2022ApJS..263...27H/abstract">ApJS</a> (arXiv:2109.09660)</p><p>These are data products associated with the second data release (DR2) of the COSMOS Lyman-Alpha Mapping And Tomography Observations (CLAMATO) survey with the Keck-I telescope, which mapped 3D Lyman-alpha forest absorption at 2.05<z<2.55 within the COSMOS field. This is an updated version of the original DR2 repository (https://doi.org/10.5281/zenodo.5842842), which had accidentally left out several files. The other files that was in that repository are duplicated here.</p><p>The following is the summary of the main products:</p><ul><li>Source catalog (cl2020_valueadded_release_20200602.txt)</li><li>Reduced spectra, in spec_v0 .tar.gz tarball</li><li>Continuum-fitted 2.05<z<2.55 Lyman-alpha forest pixel data (pixel_data_v0.bin)</li><li>Wiener-reconstructed 3D absorption map (map_2020_v0.bin)</li><li>Reconstructed 3D matter density field using the TARDIS-II algorithm (TARDIS_CLAMATO_DR2_v0.4_smoothed.npy)</li><li>Eigenvalues of the pseudo-deformation tensor calculated from the density field (TARDIS_eigenvalues_CLAMATO_DR2_v0.4_smoothed.npy)</li></ul><p><strong> Redshift Catalog and Spectra</strong></p><p>We provide our redshift catalog and reduced spectra obtained with Keck-I/LRIS-Blue.</p><p>The source catalog is provided in the ASCII file cl2020_valueadded_release_20200602.txt, with the following columns:</p><ul><li>SPECFIL: Filename of spectrum (within spec_v0.tar.gz)</li><li>TOMO_ID: CLAMATO ID number</li><li>GMAG: g-magnitude (AB) per Capak et al 2007 photometric catalog</li><li>CONF: Redshift confidence grade: see https://arxiv.org/abs/1710.02894</li><li>ZSPEC: Spectroscopic redshift as determined from CLAMATO spectrum</li><li>QSO: QSO flag (1 if QSO, 0 if non-QSO)</li><li>RA: R.A. in degrees (J2000)</li><li>DEC: Dec in degrees (J2000)</li><li>S/N_1: Estimated Lya-forest S/N at 2.05<z<2.15, -9.0 denotes no estimate</li><li>S/N_2: Estimated Lya-forest S/N at 2.15<z<2.35, -9.0 denotes no estimate</li><li>S/N_2: Estimated Lya-forest S/N at 2.35<z<2.55, -9.0 denotes no estimate</li><li>S/N_RED: Estimated S/N over restframe 1250 Å < \(\lambda\) < 1350 Å, -9.0 denotes no estimate</li><li>TOMOFLAG: Flag on whether sightline was used in tomographic map (0 for no, 1 for yes)</li><li>EXPTIME: Exposure time on the spectrum, in seconds (aggregate)</li></ul><p>The tarball spec_v0.tar.gz include all the reduced spectra from LRIS-Blue, with the respective filename indicated by the first column of the catalog. We decided not to make available the LRIS-Red spectra.</p><p>The individual LRIS spectra are provided in FITS format, with the following HDU Extensions:</p><ul><li>HDU0: Object spectral flux density, in units of \(10^{-17}\,\mathrm{erg\,s^{-1}\,cm^{-2}\,angstrom^{-1}}\)</li><li>HDU1: Noise standard deviation</li><li>HDU2: Pixel Wavelengths in angstroms</li></ul><p><strong>Pixel Data</strong></p><p>The binary file PIXEL_DATA_v0.BIN stores the concatenated Lyman-alpha forest pixels at 2.05<z<2.55 that have been extracted from the 1D spectra and continuum-fitted. </p><p>The first value in the binary is a 32-bit integer specifying the number of pixels (84608), followed by 5 double-precision floating point (64-bit) vectors storing the x, y, z, sigma_f, and delta_f of the pixels.</p><p>An example python script to read pixel_data is as follows:</p><p>import numpy as np with open('pixel_data_v0.bin','r') as f: npix = np.fromfile(f, dtype=np.int32, count=1) f.seek(4) pixel_data = np.fromfile(f,dtype=np.float64).reshape((npix,5))</p><p>LIST_TOMO_INPUT_2020.TXT is a summary file of corresponding to PIXEL_DATA.BIN, listing the [x,y,z] position of the sightlines that contributed to the file as well as, in the final two columns, the index range that can be used to grab the relevant pixels from the concatenated pixel list.</p><p>The origin of the map coordinates is at [RA=149.89150 deg, Dec=2.0915050 deg], where x is increasing in the R.A. dimension and y is increasing in the Dec.</p><p><strong>Tomographic Map</strong></p><p>The Wiener-reconstructed map of the 2.05<z<2.55 IGM within the CLAMATO field is the result of applying the "dachshund" algorithm (http://github.com/caseywstark/dachshund) to PIXEL_DATA.BIN, with the configuration file INPUT.CFG . (Caveat: the version of PIXEL_DATA.BIN here is not actually the right version to directly input into the dachshund code: the first integer in this file should not be present for input to dachshund). </p><p>The reconstructed map is MAP_2020_V0.BIN, which is a 68x56x876 = 3335808 pixel double-precision binary file. The dimension that changes fastest is the z-dimension along the line-of-sight (876 pixels per dimension), followed by the y-dimension in increasing Declination (56 pixels per dimension) and x-dimension along the direction of increasing R.A. (68 pixels per dimension).</p><p>Each map pixel represents a 0.5Mpc/h comoving voxel of the Ly-alpha forest absorption. See the Appendix of https://arxiv.org/abs/1710.02894 for the conversion factors to assume to switch between pixel/voxel and [RA, Dec, redshift].</p><p>Note that no additional smoothing has been applied in this binary, whereas most of the visualizations in the paper have had Gaussian smoothing applied.</p><p><strong>Reconstructed Density Field and Cosmic Web</strong></p><p>We also release the underlying matter density field in the CLAMATO volume, estimated using the TARDIS-II algorithm (https://arxiv.org/abs/2007.15994). This is in numpy format, and can be read directly into a 3D array using Python as follows:</p><p>>>> import numpy as np >>> den=np.load('TARDIS_CLAMATO_DR2_v0.4_smoothed.npy') >>> np.shape(den) (34, 28, 438) >>> eigen = np.load('TARDIS_eigenvalues_CLAMATO_DR2_v0.4_smoothed.npy') >>> np.shape(eigen) (34, 28, 438, 3)</p><p>The 3 dimensions correspond to the R.A., Declination, and line-of-sight directions, respectively. </p><p>Note that unlike the Wiener-filtered absorption map<i>, the TARDIS-II reconstruction outputs are in 1Mpc/h comoving voxels. </i>The coordinate zero-point sand line-of-sight comoving distance-redshift relationship are otherwise the same as the Wiener map. </p><p>The eigenvalues of the pseudo-deformation tensor are in TARDIS_eigenvalues_CLAMATO_DR2_v0.4_smoothed.npy, with the same array shape as the matter density, but with an additional array dimension storing the 3 sorted eigenvalues at each point in the volume. </p><p>For reference, we have included an iPython notebook (Tomographic_Maps.ipynb) that plots the Wiener-filtered absorption alongside the TARDIS-II densities and eigenvalues (e.g. Figure 8 of the main paper).</p>
Baseline assemblies for "ntLink: a toolkit for de novo genome assembly scaffolding and mapping using long reads" protocol
<p>ntLink is a flexible <em>de novo</em> genome scaffolding toolkit which can be run in various modes depending on the desired user output, with multiple new functionalities recently introduced. Here, we provide the baseline assembly datasets used in the ntLink protocol paper "ntLink: a toolkit for <em>de novo </em>genome assembly scaffolding and mapping using long reads". The provided assemblies are ABySS (short-read) and Flye (long-read) assemblies of <em>Caenorhabditis elegans </em>genome sequencing data. The ABySS (v2.1.4) assembly utilized paired-end short reads (accession DRR008444), and was run with the following parameters: k=64 l=40 s=1000 q=15 B=10G j=8 kc=3 H=4 S=1000-10000 N=9.The <em>C. elegans</em> Flye (v2.5) assembly was run using Oxford Nanopore long reads (accession SRR10028109) and the following parameters: --nano-raw SRR10028109.fastq -g100m -t48.</p>
Ogham Stone Maps: Squirrel Stones in Ireland
<p>Ogham Stone Maps: Squirrel Stones in Ireland</p> <blockquote> <p>Mapping of Squirrel Stones (Q106602643) aggregated to the administrative level of counties and baronies, created with QGIS from the attached GeoJSON files and the baselayer Esri.WorldShadedRelief (Tiles © Esri - Source: Esri), EPSG:3857 (as of 01/17/2023).</p> </blockquote>
Monthly and annual evapotranspiration maps in Berlin (Germany)
<p>Monthly and annual evapotranspiration (ET) maps of Berlin, Germany at a 10-m resolution are provided. This dataset is related to the manuscript "City-wide, high-resolution mapping of evapotranspiration to guide climate-resilient planning" (under review).</p> <p>The monthly and annual ET sums are provided as rasters (.tif files). The monthly ET sums are given in the files named "ETmonthly_2019_(month).tif" The annual ET sum for 2019 is named "ETannual_2019.tif". The coordinate reference system (CRS) is "+proj=longlat +datum=WGS84 +no_defs."</p> <p>For access to daily ET maps in 2019, please contact Stenka Vulova (stenka.vulova@tu-berlin.de).</p> <p>The abstract of the manuscript is given for background information on the dataset:</p> <p>"The impacts of global change, including extreme heat and water scarcity, are threatening an ever-growing urban world population. Evapotranspiration (ET) mitigates the urban heat island, reducing the effect of heat waves. It can also be used as a proxy for vegetation water use, making it a crucial tool to plan resilient green cities. To optimize the trade-off between urban greening and water security, reliable and up-to-date maps of ET for cities are urgently needed. Despite its importance, few studies have mapped urban ET accurately for an entire city in high spatial and temporal resolution. We mapped the ET of Berlin, Germany in high spatial (10-m) and temporal (hourly) resolution for the year of 2019. A novel machine learning (ML) approach combining Sentinel-2 time series, open geodata, and flux footprint modeling was applied. Two eddy flux towers with contrasting surrounding land cover provided the training and testing data. Flux footprint modeling allowed us to incorporate comprehensive land cover types in training the ML models. Open remote sensing and geodata used as model inputs included Normalized Difference Vegetation Index (NDVI) from Sentinel-2, building height, impervious surface fraction, vegetation fraction, and vegetation height. NDVI was used to indicate vegetation phenology and health, as plant transpiration contributes to the majority of terrestrial ET. Hourly reference ET (RET) was calculated and used as input to capture the temporal dynamics of the meteorological conditions. Predictions were carried out using Random Forest (RF) regression. Weighted averages extracted from hourly ET maps using flux footprints were compared to measured ET from the two flux towers. Validation showed that the approach is reliable for mapping urban ET, with a mean R2 of 0.76 and 0.56 and a mean RMSE of 0.0289 mm and 0.0171 mm at the more vegetated site and the city-center site, respectively. Lastly, the variation of ET between Local Climate Zones (LCZs) was analyzed to support urban planning. This study demonstrated the capacity to map urban ET at an unprecedented high spatial and temporal resolution with a novel methodology, which can be used to support the sustainable management of green infrastructure and water resources in an urbanizing world facing climate change."</p>
Map 2 in Monograph of African Costaceae
Map 2 Distribution of Costus acutissimus Maas & H.Maas (▲), C. albiflos Maas & H.Maas (●) and C. aureus Maas & H.Maas (■).
Red Relief Image Maps of the Ruinon Landslide, Northern Italy (2021-2022)
<p>Red Relief Image Maps of the Ruinon Landslide, Northern Italy.</p> <p>The maps are from UAV surveys carried out above the landslide on the following dates:</p> <ul> <li>06 July 2021</li> <li>29 October 2021</li> <li>02 May 2022</li> <li>05 July 2022</li> <li>05 October 2022</li> <li>29 October 2022</li> </ul> <p>The maps are in WGS 84/ UTM zone 32N (EPSG:32632). They <strong>are not finely</strong> co-registered.</p> <p>The map production process details are discussed in <em>Yordanov, V.; Truong, Q.X.; Brovelli, M.A. Estimating Landslide Surface Displacement by Combining Low-Cost UAV Setup, Topographic Visualization and Computer Vision Techniques. Drones <strong>2023</strong>, 7, 85. <a href="https://doi.org/10.3390/drones7020085">https://doi.org/10.3390/drones7020085</a> </em></p> <p>The maps are produced as part of the "Geoinformatics and Earth Observation for Landslide Monitoring" Italy-Vietnam.</p> <p>The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring” CUP D19C21000480001 (Italian side) and partially funded by the Ministry of Science and Technology of Vietnam (MOST) (Vietnamese side) by the Bilateral Scientific Research project between Vietnam and Italy, code: NÐT/IT/21/14</p>
NOAA NCCOS Assessment: Agency priorities for mapping coral reef ecosystems in Hawaiʻi, 2022-07-08 to 2022-08-01
<p>Description</p> <p>NOAA's Coral Reef Conservation Program (CRCP) has identified a need for priority locations based on emerging management requirements in shallow coral reef areas (up to 40 meters) surrounding the main Hawaiian Islands. The priorities provided by participating agencies will inform research and monitoring activities, address current and future management needs, and maximize opportunities to leverage and complement existing regional efforts.</p> <p>To meet this need, NOAA’s National Centers for Coastal Ocean Science (NCCOS) developed a systematic, quantitative approach and online GIS application to gather seafloor mapping priorities from researchers and coral reef managers. Participants placed virtual coins into a grid overlaid on the project area to express the location of their mapping priorities. They also used pull-down menus to indicate specific mapping data needs and the rationale for their selections. Participants’ inputs were compiled and analyzed to identify high priority areas along with their justifications and requirements. A total of 17 participant groups entered their mapping priorities into the online tool. Identifying these high priority areas provide a critical spatial framework for prioritizing mapping efforts in shallow coral reef ecosystems in Hawaiʻi.</p> <p>Purpose:</p> <p>The overall goal of the project was to systematically gather and quantify suggestions for mapping needs to support management of shallow coral reef ecosystems along the coasts of the main Hawaiian Islands. This dataset supports these goals by compiling input from a diversity of regional experts on their recommended priorities for mapping data collection.</p> <p>Methods:</p> <p>An advisory group was established which included individuals from NOAA CRCP and NOAA Fisheries. This advisory team customized the prioritization process specifically to meet the needs of CRCP and local coral reef manager priorities. In the online prioritization tool, the study area was divided into 1786 hexagonal grid cells 2.6 km<sup>2</sup> in size. Existing relevant spatial datasets (<em>e.g.</em>, bathymetry, protected areas, etc.) were provided as a digital atlas to help participants understand information and data gaps within the project area and to identify locations they wanted to prioritize for future data collections. Each participant was provided with 540 virtual coins to place into grid cells to denote their mapping needs. They were instructed to place more coins in grid cells that were higher priority. A maximum of 54 coins could be placed into an individual grid cell by each respondent. Participants also selected from a drop-down list of predefined management uses from the following list: endangered species management (e.g.,), habitat restoration, monitoring, coastal vulnerability planning, watershed management, fisheries management, consultations and permitting, emergency response, and spatial protection and management. Respondents also selected what map product requirements were needed in priority cells by selecting a minimum of one, to a maximum of two choices from the following list: delineations of large topographic features, delineations of hard vs. soft bottom, models of habitat suitability for key taxa or communities, delineations of substrate type (e.g. sand, mud, coral, rock), models of presence/absence or density of corals, identification of coral species and their local environments, documentation of individual specimen condition. Coin values were summarized and mapped to identify high priority areas, reasons for those priorities, and information needs. This ESRI shapefile contains the 2.6 km<sup>2</sup> grid cells used in this prioritization and their associated coin values overall, as well as by management use and map product requirement. Other summary values include the number of participants, number of participating groups, number of management uses, and number of map product requirements. Additionally, coins for microscale (identification of coral species and their local environments and documentation of individual specimen condition), mesoscale (delineations of substrate type, models of presence/absence/density of corals), and regional (delineations of topographic features, delineations of hard vs. soft bottom, models of habitat suitability) requirements were summarized. Also included is a ranking of each grid cell based on the total number of coins, management uses, and participating groups allocating coins in the respective cell. For a complete description of the process and analysis see: Kraus et al. 2023, in prep.</p> <p> </p>
Fig. 9. – Distribution maps. Brachiaria subrostrata A in Revision of some Malagasy forage grasses and their relatives within Brachiaria, Echinochloa, Moorochloa, and Urochloa
Fig. 9. – Distribution maps. Brachiaria subrostrata A. Camus (stars), B. tsiafajavonensis A. Camus (triangles), B. umbellata (Trin.) Clayton (circles), and Echinochloa hubbardii (A. Camus) Voronts. (squares).
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.