Comparison of the ZC2 linkage map with the peach physical map v 2.0.

Comparison of the peach consensus map with the peach genome sequence v2.0.

EFSF Base map for use in the EFSF RAMP Story Map. While this map includes the MA, MPC, IRA, and Frank Church River of No Return Wilderness (FCRONW) feature layers, all except the MA and FCRNORW have been turned off, so only one administrative feature layer will be displayed per map.The 2003 Payette Land and Resource Management Plan (“Forest Plan”; USDA 2003) provides management direction for resources on the Forest and assigns different components, such as desired conditions, standards, and guidelines, for management areas (MAs). The Forest Plan divides the Forest into 16 different MAs, based on administrative and watershed boundaries.

MAs that fall within the project area boundary are:

• MA 12. South Fork almon River

• MA 13. Big creek/stibnite

This dataset contains the boundaries according to the stage of development of the PPRN. The characteristic of a perimeter is to be the consequence of an official act and to produce its effects from a specified date. This may include: — prescribed scope contained in the prescription order of a PPR (natural or technological); — scope of risk exposure that corresponds to the scope regulated by the approved RPP. This approved perimeter is a utility easement (PM1 for PPRNs and PM3 for PPRTs); — scope of study which corresponds to the envelope in which the hazards were studied.

The improved SNP-based genetic linkage map of ‘Zin Dai’ × ‘Crimson Lady’ (ZC2) progeny.

English: The 2012 Forest Cover and Land Use was made by the Ministry of the Environment (MIAMBIENTE) of Panama, within the framework of the ONUREDD Program, with technical and financial support from the Food and Agriculture Organization of the United Nations, FAO . It was developed at a scale of 1:50,000 with a multipurpose approach to identify and quantify the different types of forests, agricultural, livestock and cultural uses categories. For this map, 310 RapidEye scenes were used, covering the entire national territory of Panama, with dates from 2011 to 2012. The 2012 map is the most recent land cover and land use mapping product in Panama, obtained using high-resolution satellite images, analytical processing and rigorous interpretation, with extensive field verification, unit mapping from one hectare, and with a more coherent classification system. Therefore, the 2012 map should be used as the most recent and best information available on the coverage and use of land in the country, not only for MIAMBIENTE, but for other public and private institutions and for society in general. The Final Report PDF document explaining all work done to generate this layer, can be found here "Informe Final" (spanish only.) Leyend:

Español: El Mapa de Cobertura y Uso de la Tierra 2012 fue realizado por el Ministerio de Ambiente (MIAMBIENTE) de Panamá, en el marco del Programa ONUREDD, con apoyo técnico y financiero de la Organización de las Naciones Unidas para la Alimentación y la Agricultura, FAO. Se elaboró a escala 1:50,000 con un enfoque multipropósito para identificar y cuantificar los diferentes tipos de bosques, categorías agrícolas, ganaderas y usos culturales. Para este mapa se utilizaron 310 escenas RapidEye, cubriendo todo el territorio nacional de Panamá, con fechas del 2011 al 2012. El mapa 2012 es el producto cartográfico de cobertura y uso de la tierra más reciente en Panamá, obtenido utilizando imágenes de satélite de alta resolución, un procesamiento analítico e interpretación riguroso, con amplia comprobación de campo, mapeo de unidades a partir de una hectárea, y con un sistema de clasificación más coherente. Por lo anterior, el mapa 2012 debe ser utilizado como la más reciente y mejor información disponible de la cobertura y uso de la tierra en el país, no solo para MIAMBIENTE, sino para otras instituciones públicas y privadas y para la sociedad en general. El documento en PDF titulado Reporte Final, que contiene todos los detalles de la generación de esta capa, puede ser descargado en "Reporte Final". Leyenda del Mapa: To download this layers as a Shapefile, please use the following link:(Para descargar esta capa como un Shapefile, por favor utilizar el siguiente enlace:)Cobertura Boscosa y Uso de la Tierra 2012To download this layer as a Tif file, please use the following link: (Para descargar esta capa como un Archivo Tif, utilice el siguiente link: Cobertura Boscosa y Uso de la Tierra 2012 - Raster

Resource Mapping data was collected from field survey and all points such as markets, atms, schools were located and appropriate tags were given.

Data was uploaded on Google sheets and addons of Fusion Mas and point map were installed and addons were run to form virtual maps in their own particular webpages.

Source link of those webpages are determined and were added in a iframe in src link.

In web html design a table was made and all three iframe are added in table.

The final html was added as html element in sites.google.com to create a custom website.

The website link: www.sites.google.com/site/pranavrsmap

Webpage and Sheets are the most important data here. Other data are optional and are uploaded for your Geospatial Location research

In 2023, Google Maps was the most downloaded map and navigation app in the United States, despite being a standard pre-installed app on Android smartphones. Waze followed, with 9.89 million downloads in the examined period. The app, which comes with maps and the possibility to access information on traffic via users reports, was developed in 2006 by the homonymous Waze company, acquired by Google in 2013.

Usage of navigation apps in the U.S. As of 2021, less than two in 10 U.S. adults were using a voice assistant in their cars, in order to place voice calls or follow voice directions to a destination. Navigation apps generally offer the possibility for users to download maps to access when offline. Native iOS app Apple Maps, which does not offer this possibility, was by far the navigation app with the highest data consumption, while Google-owned Waze used only 0.23 MB per 20 minutes.

Usage of navigation apps worldwide In July 2022, Google Maps was the second most popular Google-owned mobile app, with 13.35 million downloads from global users during the examined month. In China, the Gaode Map app, which is operated along with other navigation services by the Alibaba owned AutoNavi, had approximately 730 million monthly active users as of September 2022.

El Mapa de la Ecored -Lima es un proyecto de Ficus Perú Desarrollo Ambiental en coordinación con el USDA Forest Service, siguiendo el Stewardship Mapping and Assessment Project (STEW-MAP), el mismo que es un programa nacional de investigación del Servicio Forestal del USDA diseñado para responder a las siguientes preguntas: ¿Qué grupos de gestión medioambiental trabajan en las distintas áreas? ¿Dónde, por qué, cómo y con qué efectos? STEW-MAP define un "grupo de gestión" como una organización o grupo cívico que trabaja para conservar, gestionar, supervisar, defender y/o educar al público sobre su entorno local. Los datos de STEW-MAP se comparten sin incluir información personal identificable de los encuestados. Este conjunto de datos incluye los resultados de la encuesta STEW-MAP de la ciudad de Lima el 2022. La información de contacto se proporciona a nivel de organización (por ejemplo, grupo cívico de administración ambiental). Esta versión pública del conjunto de datos solo incluye los grupos de la base de datos que aceptaron aparecer en la lista pública. Para más información sobre STEW-MAP, incluidos los métodos, véase https://www.fs.usda.gov/research/projects/stew-map.The Stewardship Mapping and Assessment Project (STEW-MAP) is a national USDA Forest Service research program designed to answer the questions: Which environmental stewardship groups are working across landscapes? Where, why, how, and to what effect? STEW-MAP defines a “stewardship group” as a civic organization or group that works to conserve, manage, monitor, advocate for, and/or educate the public about their local environments. STEW-MAP data are shared without the inclusion of personally identifiable information of individual respondents. This dataset includes results from the 2022 Lima ECORED STEW-MAP survey. Contact information is provided at the organizational (e.g., civic environmental stewardship group) level. This public version of the dataset only includes those groups in the database that agreed to be publicly listed. For more information on STEW-MAP, including methods, see https://www.fs.usda.gov/research/projects/stew-map.

Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. This product is a digest in which the fields chosen are those most likely to contain valid information.

Perimeter of the Natural Risk Prevention Plan Withdrawal of the Argiles of the commune of Mas-d’Auvignon in the department of Gers. This dataset contains the boundaries at the different stages of the development of the RPP. The characteristic of these perimeters is to be the consequence of an official act and to produce their effects from a specified date. This is the:- prescribed perimeter set out in a PPR’s prescription order;- risk exposure perimeter that corresponds to the perimeter regulated by the approved RPP, this approved perimeter is a utility easement;- study scope that corresponds to the envelope in which the hazards were studied.

Distribution map of Cornelian cherry (Cornus mas)These maps were produced by combining numerous and heterogeneous data collected from atlas monographs providing complete species distribution maps, from national to regional atlases, occurrence geo-databases, scientific and grey literature.The maps were created using ESRI shapefiles (*.shp, *.shx, *.dbf, *.prj files) archived in the ZIP file. Species range is mapped with polygon features (name suffix "plg"), which define continuous areas of occupancy of the species, and with point features (name suffix "pnt"), which identify more fragmented and isolated populations. If synanthropic occurrences are reported outside the species natural range, additional point and/or polygon shapefiles are also present (suffix "syn"). Polygon borders delimiting species ranges are generalized across the mainland and sea boundaries. This offers the possibility to mask sea areas or to clip and extract the terrestrial range parts using GIS data layers of the users' choice. An additional version of polygon ranges are clipped with a coastline (name suffix "clip"), which have been derived from Natural Earth dataset "Admin 0 - Countries" 1:50M version 4.1.0 (https://www.naturalearthdata.com). Please cite as:Caudullo, G., Welk, E., San-Miguel-Ayanz, J., 2017. Chorological maps for the main European woody species. Data in Brief 12, 662-666. DOI: doi.org/10.1016/j.dib.2017.05.007 Additional information and used references are on 'supplementary materials' document:https://doi.org/10.6084/m9.figshare.5091901Chorological maps are part of the "European Atlas of Forest Tree Species" project:https://w3id.org/mtv/FISE-Comm/v01

Este mapa interactivo se hizo en el marco del proyecto HGIS de las Indias, financiado por el fondo científico de la República de Austria (FWF), project number P 26379-G18. Es un ensayo de reconstruir las trazas de los caminos coloniales existentes en el istmo de Panamá en el período colonial. Los caminos, tenían una función esencial para la comunicación de Europa con el Perú, y sobre todo para el flujo mercantil y la remisión de la plata peruana. Nombre de Dios - y a partir de 1597 Portobelo - eran los lugares de destino de los galeones de Tierra Firme que anualmente se despacharon desde España. Relacionado con esto, se organizaron las célebres ferias de Portobelo para el intercambio mercantil. El saqueo de Portobelo por Edward Vernon en 1739 puso fin abrupto a las ferias. Todo el sistema de galeones se interrumpió por décadas y - reinstalado por un corto período - ya no volvería a tener la misma importancia que antes. Aún así, los caminos por el istmo seguían teniendo cierta importancia para el sistema imperial. También en el sur, el punto final de los caminos cambió porque tras un saqueo por el pirata Henry Morgan en 1671 la ciudad de Panamá se trasladó en 1673 a otro sitio más al suroeste. De Portobelo había un camino directo - el camino real - a la ciudad de Panamá, aunque para el comercio era más importante la vía que primero siguió por la costa a la boca del río Chagres y de allá río arriba hasta el pueblo de Cruces, desde donde había un camino de tierra a Panamá: el camino de Cruces. Una variante de esta ruta era el camino de Gorgona, abierto en 1735. Hay esfuerzos serios de Panamá para incluir los caminos y los sitios principales de los caminos en el patrimonio cultural de la UNESCO, en el que ya se han incluido algunas partes: Las fortificaciones de Chagres y Portobelo (en peligro) así como el sitio de Panamá Vieja y casco histórico de Panamá.La reconstrucción se dificulta por la existencia del Canal de Panamá - que inhundió gran parte del río Chagres, inclusive lugares en su ribera, como Gorgona - y el embalse de Alajuela (o Lake Madden) que tenía el mismo efecto en la zona del camino real.El mapa interactivo incluye un par de mapas originales existentes en el Archivo General de Indias, la Bibilioteca Nacional de España, la Library of Congress y la colección de la Universidad de Harvard, que georreferenciamos para alinear lo representado en ellos con la geografía actual. Es una posibilidad para ver el grado de distorciones existentes en tales mapas. Sobre estos mapas georreferenciados digitalizamos los caminos (con líneas vectoriales). Usamos también un mapa de principios del siglo XX, que presenta la situación poco antes de la construcción del canal, para tener una fuente fiable para el curso original del río Chagres. Además, incluimos las rutas reconstruidas por Christian Strassnig, quien localizó restos manifestos de los caminos recorriendo su curso por tierra y localizándolos con GPS en un proyecto para el rescate del patrimonio histórico. Luego, el mapa incluye las poblaciones, haciendas y fortificaciones existentes en la zona istímica según los datos de HGIS de las Indias; y una reconstrucción de la ruta seguida por los correos terrestres en el camino real según un itinerario de 1770, documento que también incluye los puntos de referencia en el camino de Cruces, con indicación de las leguas entre los puntos. También esta información proviene de HGIS de las Indias. http://whc.unesco.org/en/tentativelists/6205/https://caminorealproject.wordpress.com/https://caminorealdecruces.wordpress.com/http://caminodecrucespanama.blogspot.co.at/https://www.cultour.info/https://www.hgis-indias.net/Jesús Sanjurjo Ramos, "Caminos transístmicos y ferias de Panamá, siglos XVI-XVIII": Anales del museo de América XX (2012), pp. 260-271.

Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. While database records describe mineral resources worldwide, the compilation of information was intended to cover the United States completely, and its coverage of resources in other countries is incomplete. The content of MRDS records was drawn from reports previously published or made available to USGS researchers. Some of those original source materials are no longer available. The information contained in MRDS was intended to reflect the reports used as sources and is current only as of the date of those source reports. Consequently MRDS does not reflect up-to-date changes to the operating status of mines, ownership, land status, production figures and estimates of reserves and resources, or the nature, size, and extent of workings. Information on the geological characteristics of the mineral resource are likely to remain correct, but aspects involving human activity are likely to be out of date.

From the site: "MRDS is a collection of reports describing metallic and nonmetallic mineral resources throughout the world. Included are deposit name, location, commodity, deposit description, geologic characteristics, production, reserves, resources, and references. It subsumes the original MRDS and MAS/MILS.

MRDS is large and complex. This service provides a subset of the database comprised of those data fields deemed most useful and which most frequently contain some information, but full reports of most records are available as well."

The POUM is the urban planning instrument of the municipality that directs the development of the Bescanó territory. The aspects of the municipality defined by the POUM are classified in this planning figure in nine points: Memory (descriptive, justification and social), Resolutions through administrative procedures (agreements), Judicial resolutions (sentences), Other documents (inventories, citizen participation, supporting reports, etc), environmental documentation, agreement, regulations, information plans (fifty-one orthophoto, altimetry, road and hydraulic network files, environmental risks, ground covers, etc.), management plans (ninety classification files of the land, general structure of the territory, cataloging plan, urban land, developable and undeveloped, etc.) and Catalog (protected assets and farmhouses and rural houses).

description: ABSTRACT: MAS images, along with other remotely sensed data, were collected to provide spatially extensive information over the primary study areas. This information includes biophysical parameter maps such as surface reflectance and temperature. Collection of the MAS images occurred over the study areas during the 1994 field campaigns.; abstract: ABSTRACT: MAS images, along with other remotely sensed data, were collected to provide spatially extensive information over the primary study areas. This information includes biophysical parameter maps such as surface reflectance and temperature. Collection of the MAS images occurred over the study areas during the 1994 field campaigns.

ENGLISH

This dataset contains a generalized vegetation map of the Sierra Nevada Protected Area (SE Spain), derived from Andalusia's official 1:10,000-scale vegetation map. The original vegetation units were thematically reclassified into 23 simplified vegetation types based on compositional, structural and functional characteristics, including dominant species, percentage cover of trees, shrubs, herbaceous vegetation and bare soil, and prevailing plant community types. The spatial precision of the original map was preserved, while the thematic content was simplified to facilitate landscape-level ecological analysis. The resulting map provides a coherent and ecologically meaningful overview of vegetation patterns in the region, supporting biodiversity monitoring, land-use planning, ecological research, and conservation efforts.

The final typology consists of 23 structural-functional land cover types, of which 20 represent distinct vegetation units, and three correspond to ecologically relevant non-vegetated areas (e.g. bare soil, anthropic surfaces, water bodies). This map offers a management-oriented representation of vegetation, bridging the gap between detailed floristic maps and broader land cover datasets. It enhances spatial analysis capabilities for conservation planning, restoration, and landscape-scale ecological monitoring. The dataset includes a shapefile (EPSG:25830) with all mapped polygons and associated attributes, including vegetation type (in English and Spanish), dominant floristic community (phytosociological association), land use, bioclimatic belt, structural information, and monthly NDVI values.

In addition to an ID of polygon and the name of vegetation types in English and Spanish, this dataset retains several key fields from the official vegetation map of Sierra Nevada to support common analytical workflows. These fields include D-ARBOL1_SP (Dominant tree species), D-COM1 (Phytosociological association), D_USO (Land use), COMENTARIO (Short description of the unit), and D_PISO (Bioclimatic belt). It also includes the OBJECTID field, which enables direct joining with the official vegetation map of Sierra Nevada for spatial analyses and visualization.

ESPAÑOL

Este conjunto de datos contiene un mapa generalizado de la vegetación del Espacio Natural de Sierra Nevada (SE de España), derivado del mapa oficial de vegetación de Andalucía a escala 1:10.000. Las unidades originales de vegetación fueron reclasificadas temáticamente en 23 tipos simplificados de vegetación, basándose en características composicionales, estructurales y funcionales, incluyendo especies dominantes, porcentaje de cobertura de árboles, arbustos, vegetación herbácea y suelo desnudo, así como los tipos predominantes de comunidades vegetales. Se preservó la precisión espacial del mapa original, mientras que su contenido temático fue simplificado para facilitar el análisis ecológico a escala de paisaje. El mapa resultante proporciona una visión coherente y ecológicamente significativa de los patrones de vegetación en la región, apoyando el seguimiento de la biodiversidad, la planificación del uso del suelo, la investigación ecológica y las acciones de conservación.

La tipología final consta de 23 tipos de cobertura estructural-funcional del territorio (tanto en inglés como en español), de los cuales 20 representan unidades de vegetación distintas y tres corresponden a áreas no vegetadas pero ecológicamente relevantes (por ejemplo, suelo desnudo, superficies antrópicas o masas de agua). Este mapa ofrece una representación orientada a la gestión de la vegetación, sirviendo de puente entre los mapas florísticos detallados y los conjuntos de datos de cobertura terrestre más generales. Mejora la capacidad de análisis espacial para la planificación de la conservación, la restauración y el seguimiento ecológico a escala de paisaje. El conjunto de datos incluye un shapefile (EPSG:25830) con todos los polígonos cartografiados y sus atributos asociados, incluyendo el tipo de vegetación (en inglés y español), comunidad florística dominante (asociación fitosociológica), uso del suelo, piso bioclimático, información estructural y valores mensuales de NDVI.

Además de un ID de polígono y el nombre en inglés y en español del tipo de vegetación, este conjunto de datos conserva varios campos clave del mapa oficial de vegetación de Sierra Nevada para facilitar los análisis más comunes. Estos campos incluyen D-ARBOL1_SP (especie arbórea dominante), D-COM1 (asociación fitosociológica), D_USO (uso del suelo), COMENTARIO (descripción breve de la unidad) y D_PISO (piso bioclimático). Incluye también el campo OBJECTID, que permite vincular directamente este conjunto de datos con el mapa oficial de vegetación de Sierra Nevada para su análisis espacial y visualización.

Find local risk levels for Eastern Equine Encephalitis (EEE) and West Nile Virus (WNV) based on seasonal testing from June to October.

This dataset contains Management Area (MA) polygons on the Lolo National Forest. MAs represent specific criteria and constraints as described in the 1986 Lolo National Forest Plan chapters 2 and 3. The Lolo National Forest consists of 28 multiple-use management areas with a broad range of emphasis, intensities, practices, standards, and guidelines. The following approved MA amendments to the Forest Plan have been incorporated into this data set (amendments #4, 5a, 6, 7, 8, 13, 15, 17, 19, 20, 22, 29, 34, 35, 37, 42, 43, 46 and 49). Old growth polygons from Superior North old growth analysis and INFISH RHCA polygon areas, which defines riparian acres, have been appended to this data set. In 2023 management areas were allocated on aproxiamtely acres of acquired lands located on Seeley Lake, Missoula and Ninemile Ranger Districts. Important Attributes: MA_CODE: numeric value of the MAMA_DESCRIPTION: a brief description of MA objective. VQO: Visual Quality Objective includes different degrees of excellence based on physical and sociological characteristics of an area.SUITABILITY: suitable/unsuitable for timber production.MA_CATEGORY: management area prescription categories and are used to display management area direction. The categories range along a continuum from little modification by humans to extensive use and modification by humans. Initially this dataset was developed using an Altek 3100 digitizer and overlay maps drawn on a scale of 1:24,000. The Forest Plan map which is a 1:126,720 map was used to identify Management Area 4. Digitizing from this map was also done when there was a discrepancy between the Forest Plan map and the overlays.