A route feature stores the spatial locations (geography) of the road. These feature classes have an (M) value or measure on their vertices. A route system depicts all roads within or in close proximity to an administrative unit. A road is a motor vehicle travel way over 50 inches wide, unless classified and managed as a trail. This feature is only SPATIAL ROAD DATA, other data (open, closed, jurisdiction, maintenance level) is stored in INFRA. Used to link spatial roads to INFRA data, ROAD NO., BMP, EMP and Calibration. Routed roads are a single spatial line, all have data in INFRA and this data must be attached. Routed roads need to have INFRA data table attached by use of R10 Geospatial Interface (GI) tool and Visualization named Roads with Core Attributes RSW - This creates an output roads layer and adds the following fields from the INFRA database at NITC: name, lanes, service life, system, surface type, jurisdiction, objective maintenance level, operational maintenance level, route status, functional class and primary maintainer. Routed ROADS CAN HAVE OTHER DATA TABLES ATTACHED, (R10 Stream Data Point-RSW, Road Points -RSW, Bridges-RSW, MVUM Roads and Transportation Atlas. A road may be classified or unclassified. Classified roads are roads within the National Forest System lands planned and managed for motor vehicle access including State roads, county roads, private roads, permitted roads, and Forest Service roads. Unclassified roads are roads not intended to be a part of nor managed as a part of the forests transportation system, such as temporary roads, and unplanned, unengineered, unauthorized off-road vehicle tracks and abandoned travel ways. Route measurements and route directions must correspond to those stored in the INFRA Oracle table RTE_BASICS. Associated National Application: INFRA Travel Routes. IWeb Infra Roads webpage http://basenet.fs.fed.us/support/help/roads/. All routed roads are required to have data in INFRA and all roads having data in INFRA are required to be routed.Note: Extracted from GI on August 27,2012

This document explains the attribute fields and domain values used in the October 2024 publication of Vegetation Communities of Martha's Vineyard GIS dataset. This document was written by the Martha's Vineyard Commission with vegetation community and state rank information provided by staff at MassWildlife's Natural Heritage and Endangered Species Program.

The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses. For more information on the NHDPlus dataset see the NHDPlus v2 User Guide.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territories not including Alaska.Geographic Extent: The United States not including Alaska, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: EPA and USGSUpdate Frequency: There is new new data since this 2019 version, so no updates planned in the futurePublication Date: March 13, 2019Prior to publication, the NHDPlus network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the NHDPlus Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, On or Off Network (flowlines only), Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original NHDPlus dataset. No data values -9999 and -9998 were converted to Null values for many of the flowline fields.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute. Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map. Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

This Titanium_vanadium_column_headings.csv file correlates the column headings in the Titanium_vanadium_deposits.csv file with the attribute field names in the Titanium_vanadium_deposits.shp file and provides a brief description of each column heading and attribute field name. Also included with this data release are the following files: Titanium_vanadium_deposits.csv file, which lists the deposits and associated information such as the host intrusion, location, grade, and tonnage data, along with other miscellaneous descriptive data about the deposits; Titanium_vanadium_deposits.shp file, which duplicates the information in the Titanium_vanadium_deposits.csv file in a spatial format for use in a GIS; Titanium_vanadium_deposits_concentrate_grade.csv file, which lists the concentrate grade data for the deposits, when available; and Titanium_vanadium_deposits_references.csv file, which lists the abbreviated and full references that are cited in the Titanium_vanadium_deposits.csv, and Titanium_vanadium_deposits.shp, and Titanium_vanadium_deposits_concentrate_grade.csv files.

*This dataset is authored by ESRI and is being shared as a direct link to the feature service by Pend Oreille County. NHD is a primary hydrologic reference used by our organization.The National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesCoordinate System: Web Mercator Auxiliary Sphere Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American Samoa Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not.Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

Context

The datasets provided include the players data for the Career Mode from FIFA 15 to FIFA 22 ("players_22.csv"). The data allows multiple comparisons for the same players across the last 8 version of the videogame.

Some ideas of possible analysis:

• Historical comparison between Messi and Ronaldo (what skill attributes changed the most during time - compared to real-life stats);

• Ideal budget to create a competitive team (at the level of top n teams in Europe) and at which point the budget does not allow to buy significantly better players for the 11-men lineup. An extra is the same comparison with the Potential attribute for the lineup instead of the Overall attribute;

• Sample analysis of top n% players (e.g. top 5% of the player) to see if some important attributes as Agility or BallControl or Strength have been popular or not acroos the FIFA versions. An example would be seeing that the top 5% players of FIFA 20 are faster (higher Acceleration and Agility) compared to FIFA 15. The trend of attributes is also an important indication of how some attributes are necessary for players to win games (a version with more top 5% players with high BallControl stats would indicate that the game is more focused on the technique rather than the physicial aspect).

Content

• Every player available in FIFA 15, 16, 17, 18, 19, 20, 21, and also FIFA 22

• 100+ attributes

• URL of the scraped players

• URL of the uploaded player faces, club and nation logos

• Player positions, with the role in the club and in the national team

• Player attributes with statistics as Attacking, Skills, Defense, Mentality, GK Skills, etc.

• Player personal data like Nationality, Club, DateOfBirth, Wage, Salary, etc.

Updates from previous FIFA 21 dataset are the following:

• Inclusion of FIFA 22 data

• Inclusion of all female players

• Columns reorder - to increase readability

• Removal of duplicate GK attribute fields

• The field defending marking has been renamed defending marking awareness and includes both the marking (old attribute name - up to FIFA 19) and defensive awareness values (new attribute name - from FIFA 20)

• All data from FIFA 15 was re-scraped, as one Kaggle user noted in this discussion that sofifa updated some historical player market values over time

Acknowledgements

Data has been scraped from the publicly available website sofifa.com.

A custom Python Tool Box exclusively for ESRI ArcMap Application. This toolbox contains two tools: 1. Duplicate Value Calculator : - to search duplicate values in a specified Attribute Field of Table /FeautureClass and populate user defined text for such records in another specified Attribute Field of same Table/FeatureClass. If no Attribute Field is selected to populate text, a default Attribute Field will be added with Name as "DUPLICATE_{Name of Field for Search Duplicate values}".

Further, User can imply SQL Expression to limit the records to be searched as per requirement.

Caution : This Tool modifies the SCHEMA of selected Table/FeatureClass if no Attribute Field is selected to populate text for duplicate values. So preconsider to choose both Attribute Fields - One for Duplicate Search and other for Text against duplicate value if You are concerned about to add new field to Your Table/FeatureClass.

1. Delete Rows : - to delete Rows from input Table/FeatureClass. Put an SQL Expression for records filter, otherwise all rows will be deleted.

Model Methods:1. Extracts layer areas only within the study area. 2. Assigns a score from 0 (lowest) to 3 (highest) to each attribute as described in the attribute selection column. 4. Converts layer from raster to polygon. 5. Renames the attribute field with rankings from GRIDCODE to descriptive scoring field name.

This dataset is a supplement to the statewide dataset of Real Property Assessments, at https://opendata.maryland.gov/d/ed4q-f8tm, which shows all properties in the state and assessment data from SDAT and MDP.

This dataset is a compilation of tax parcel polygon and point layers from the seven Twin Cities, Minnesota metropolitan area counties of Anoka, Carver, Dakota, Hennepin, Ramsey, Scott and Washington. The seven counties were assembled into a common coordinate system. No attempt has been made to edgematch or rubbersheet between counties. A standard set of attribute fields is included for each county. (See section 5 of the metadata). The attributes are the same for the polygon and points layers. Not all attributes are populated for all counties.

The polygon layer contains one record for each real estate/tax parcel polygon within each county's parcel dataset. Some counties will polygons for each individual condominium, and others do not. (See Completeness in Section 2 of the metadata for more information.) The points layer includes the same attribute fields as the polygon dataset. The points are intended to provide information in situations where multiple tax parcels are represented by a single polygon. The primary example of this is the condominium. Condominiums, by definition, are legally owned as individual, taxed real estate units. Records for condominiums may not show up in the polygon dataset. The points for the point dataset often will be randomly placed or stacked within the parcel polygon with which they are associated.

The polygon layer is broken into individual county shape files. The points layer is one file for the entire metro area.

In many places a one-to-one relationship does not exist between these parcel polygons or points and the actual buildings or occupancy units that lie within them. There may be many buildings on one parcel and there may be many occupancy units (e.g. apartments, stores or offices) within each building. Additionally, no information exists within this dataset about residents of parcels. Parcel owner and taxpayer information exists for many, but not all counties.

Polygon and point counts for each county are as follows (based on the October 2008 dataset unless otherwise noted):

polygons / points
Anoka 129139 / 129138
Carver 38134 / 38133
Dakota 135925 / 150294
Hennepin 422976 / 446623
Ramsey 149169 / 168233
Scott 55191 / 55191
Washington 98915 / 103915

This is a MetroGIS Regionally Endorsed dataset.

Each of the seven Metro Area counties has entered into a multiparty agreement with the Metropolitan Council to assemble and distribute the parcel data for each county as a regional (seven county) parcel dataset.

A standard set of attribute fields is included for each county. The attributes are identical for the point and polygon datasets. Not all attributes fields are populated by each county. Detailed information about the attributes can be found in the MetroGIS Regional Parcels Attributes 2008 document.

Additional information may be available in the individual metadata for each county at the links listed below. Also, any questions or comments about suspected errors or omissions in this dataset can be addressed to the contact person listed in the individual county metadata.

Anoka = http://www.anokacounty.us/315/GIS

Caver = http://www.co.carver.mn.us/GIS

Dakota = http://www.co.dakota.mn.us/homeproperty/propertymaps/pages/default.aspx

Hennepin: http://www.hennepin.us/gisopendata

Ramsey = https://www.ramseycounty.us/your-government/open-government/research-data

Scott = http://www.scottcountymn.gov/1183/GIS-Data-and-Maps

Washington = http://www.co.washington.mn.us/index.aspx?NID=1606

The National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesGeographic Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSUpdate Frequency: AnnualPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not. Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Abstract

Tetrapods (amphibians, reptiles, birds and mammals) are model systems for global biodiversity science, but continuing data gaps, limited data standardisation, and ongoing flux in taxonomic nomenclature constrain integrative research on this group and potentially cause biassed inference. We combined and harmonised taxonomic, spatial, phylogenetic, and attribute data with phylogeny-based multiple imputation to provide a comprehensive data resource (TetrapodTraits 1.0.0) that includes values, predictions, and sources for body size, activity time, micro- and macrohabitat, ecosystem, threat status, biogeography, insularity, environmental preferences and human influence, for all 33,281 tetrapod species covered in recent fully sampled phylogenies. We assess gaps and biases across taxa and space, finding that shared data missing in attribute values increased with taxon-level completeness and richness across clades. Prediction of missing attribute values using multiple imputation revealed substantial changes in estimated macroecological patterns. These results highlight biases incurred by non-random missingness and strategies to best address them. While there is an obvious need for further data collection and updates, our phylogeny-informed database of tetrapod traits can support a more comprehensive representation of tetrapod species and their attributes in ecology, evolution, and conservation research.

Additional Information: This work is output of the VertLife project. To flag erros, provide updates, or leave other comments, please go to vertlife.org. We aim to develop the database into a living resource at vertlife.org and your feedback is essential to improve data quality and support community use.

Version 1.0.1 (25 May 2024). This minor release addresses a spelling error in the file Tetrapod_360.csv. The error involves replacing white-space characters with underscore characters in the field Scientific.Name to match the spelling used in the file TetrapodTraits_1.0.0.csv. These corrections affect only 102 species considered extinct and 13 domestic species (Bos_frontalis, Bos_grunniens, Bos_indicus, Bos_taurus, Camelus_bactrianus, Camelus_dromedarius, Capra_hircus, Cavia_porcellus, Equus_caballus, Felis_catus, Lama_glama, Ovis_aries, Vicugna_pacos). All extinct and domestic species in TetrapodTraits have their binomial names separated by underscore symbols instead of white space. Additionally, we have added the file GridCellShapefile.zip, which contains the shapefile required to map species presence across the 110 × 110 km equal area grid cells (this file was previously provided through an External Source here).

Version 1.0.0 (19 April 2024). TetrapodTraits, the full phylogenetically coherent database we developed, is being made publicly available to support a range of research applications in ecology, evolution, and conservation and to help minimise the impacts of biassed data in this model system. The database includes 24 species-level attributes linked to their respective sources across 33,281 tetrapod species. Specific fields clearly label data sources and imputations in the TetrapodTraits, while additional tables record the 10K values per missing entry per species.

Taxonomy – includes 8 attributes that inform scientific names and respective higher-level taxonomic ranks, authority name, and year of species description. Field names: Scientific.Name, Genus, Family, Suborder, Order, Class, Authority, and YearOfDescription.

Phylogenetic tree – includes 2 attributes that notify which fully-sampled phylogeny contains the species, along with whether the species placement was imputed or not in the phylogeny. Field names: TreeTaxon, TreeImputed.

Body size – includes 7 attributes that inform length, mass, and data sources on species sizes, and details on the imputation of species length or mass. Field names: BodyLength_mm, LengthMeasure, ImputedLength, SourceBodyLength, BodyMass_g, ImputedMass, SourceBodyMass.

Activity time – includes 5 attributes that describe period of activity (e.g., diurnal, fossorial) as dummy (binary) variables, data sources, details on the imputation of species activity time, and a nocturnality score. Field names: Diu, Noc, ImputedActTime, SourceActTime, Nocturnality.

Microhabitat – includes 8 attributes covering habitat use (e.g., fossorial, terrestrial, aquatic, arboreal, aerial) as dummy (binary) variables, data sources, details on the imputation of microhabitat, and a verticality score. Field names: Fos, Ter, Aqu, Arb, Aer, ImputedHabitat, SourceHabitat, Verticality.

Macrohabitat – includes 19 attributes that reflect major habitat types according to the IUCN classification, the sum of major habitats, data source, and details on the imputation of macrohabitat. Field names: MajorHabitat_1 to MajorHabitat_10, MajorHabitat_12 to MajorHabitat_17, MajorHabitatSum, ImputedMajorHabitat, SourceMajorHabitat. MajorHabitat_11, representing the marine deep ocean floor (unoccupied by any species in our database), is not included here.

Ecosystem – includes 6 attributes covering species ecosystem (e.g., terrestrial, freshwater, marine) as dummy (binary) variables, the sum of ecosystem types, data sources, and details on the imputation of ecosystem. Field names: EcoTer, EcoFresh, EcoMar, EcosystemSum, ImputedEcosystem, SourceEcosystem.

Threat status – includes 3 attributes that inform the assessed threat statuses according to IUCN red list and related literature. Field names: IUCN_Binomial, AssessedStatus, SourceStatus.

RangeSize – the number of 110×110 grid cells covered by the species range map. Data derived from MOL.

Latitude – coordinate centroid of the species range map.

Longitude – coordinate centroid of the species range map.

Biogeography – includes 8 attributes that present the proportion of species range within each WWF biogeographical realm. Field names: Afrotropic, Australasia, IndoMalay, Nearctic, Neotropic, Oceania, Palearctic, Antarctic.

Insularity – includes 2 attributes that notify if a species is insular endemic (binary, 1 = yes, 0 = no), followed by the respective data source. Field names: Insularity, SourceInsularity.

AnnuMeanTemp – Average within-range annual mean temperature (Celsius degree). Data derived from CHELSA v. 1.2.

AnnuPrecip – Average within-range annual precipitation (mm). Data derived from CHELSA v. 1.2.

TempSeasonality – Average within-range temperature seasonality (Standard deviation × 100). Data derived from CHELSA v. 1.2.

PrecipSeasonality – Average within-range precipitation seasonality (Coefficient of Variation). Data derived from CHELSA v. 1.2.

Elevation – Average within-range elevation (metres). Data derived from topographic layers in EarthEnv.

ETA50K – Average within-range estimated time to travel to cities with a population >50K in the year 2015. Data from Nelson et al. (2019).

HumanDensity – Average within-range human population density in 2017. Data derived from HYDE v. 3.2.

PropUrbanArea – Proportion of species range map covered by built-up area, such as towns, cities, etc. at year 2017. Data derived from HYDE v. 3.2.

PropCroplandArea – Proportion of species range map covered by cropland area, identical to FAO's category 'Arable land and permanent crops' at year 2017. Data derived from HYDE v. 3.2.

PropPastureArea – Proportion of species range map covered by cropland, defined as Grazing land with an aridity index > 0.5, assumed to be more intensively managed (converted in climate models) at year 2017. Data derived from HYDE v. 3.2.

PropRangelandArea – Proportion of species range map covered by rangeland, defined as Grazing land with an aridity index < 0.5, assumed to be less or not managed (not converted in climate models) at year 2017. Data derived from HYDE v. 3.2.

File content

All files use UTF-8 encoding.

ImputedSets.zip – the phylogenetic multiple imputation framework applied to the TetrapodTraits database produced 10,000 imputed values per missing data entry (= 100 phylogenetic trees x 10 validation-folds x 10 multiple imputations). These imputations were specifically developed for four fundamental natural history traits: Body length, Body mass, Activity time, and Microhabitat. To facilitate the evaluation of each imputed value in a user-friendly format, we offer 10,000 tables containing both observed and imputed data for the 33,281 species in the TetrapodTraits database. Each table encompasses information about the four targeted natural history traits, along with designated fields (e.g., ImputedMass) that clearly indicate whether the trait value provided (e.g., BodyMass_g) corresponds to observed (e.g., ImputedMass = 0) or imputed (e.g., ImputedMass = 1) data. Given that the complete set of 10,000 tables necessitates nearly 17GB of storage space, we have organized sets of 1,000 tables into separate zip files to streamline the download process.

ImputedSets_1K.zip, imputations for trees 1 to 10.

ImputedSets_2K.zip, imputations for trees 11 to 20.

ImputedSets_3K.zip, imputations for trees 21 to 30.

ImputedSets_4K.zip, imputations for trees 31 to 40.

ImputedSets_5K.zip, imputations for trees 41 to 50.

ImputedSets_6K.zip, imputations for trees 51 to 60.

ImputedSets_7K.zip, imputations for trees 61 to 70.

ImputedSets_8K.zip, imputations for trees 71 to 80.

ImputedSets_9K.zip, imputations for trees 81 to 90.

ImputedSets_10K.zip, imputations for trees 91 to 100.

TetrapodTraits_1.0.0.csv – the complete TetrapodTraits database, with missing data entries in natural history traits (body length, body mass, activity time, and microhabitat) replaced by the average across the 10K imputed values obtained through phylogenetic multiple imputation. Please note that imputed microhabitat (attribute fields: Fos, Ter, Aqu, Arb, Aer) and imputed activity time (attribute fields: Diu, Noc) are continuous variables within the 0-1 range interval. At the user's

Physical and vegetation attributes (mean + SE) of uncleared forests and fields with different lengths of abandonment (N = 55).

Visual Genome Attribute Ranking (VGARank) is a modified versionof the Visual Genome (VG) dataset designed to evaluate a model’s ability to recognize visual attributes. It is unique in that it 1) is an open-vocabulary ranking task, instead of a fixed vocabulary domain classification task, and 2) has two variants, VGARank-A or VGARank-O to directly compare a model's performance on attribute recognition vs object recognition. This allows us to investigate how knowledge from pretraining differs between attribute concepts and object concepts. For VGARank-A, each ranking problem is formulated with respect to one object, with N ground truth attributes (usually 1-2) taken from Visual Genome’s annotations and (50−N) false attributes selected as those that are often associated with the given object but are not true for given bounding box instance. VGARank-O mirrors this design, but is formulated with respect to an attribute present in the bounding box instance. We obtain a dataset with 770,721 ranking problems fortraining, 7,997 for validation, and 32,299 for testing. Further details regarding dataset construction can be found in the supplementary materials of the paper.

Task Definition

Both the VGARank-Attribute and VGARank-Object tasks are defined as ranking tasks, where the objective is to rank ground truth pairs higher than false pairs. ForVGA-A, the ground truth is the object and attribute in the original VG annotation, while false pairing are created by selecting attributes that are often associated with the ground truth object but are not present in the current instance. For example, for “car is red”, false pairing attributes may be “car is blue” and “car is yellow”, which can be true statements for some cars but not for the one in question within the bounding box.

Dataset Structure

The VGARank dataset adds two fields, attribute_prompts (VGARank-Attribute) and object_prompts (VGARank-Object) to the Visual Genome dataset at the instance level (one bounding box in an image, which means multiple instances could exist for a single image). Below is an example from the dataset, where the two additional fields are added to the existing annotations for one instance bounding box.

 {
  "image_id": 2388999,
  "attributes": [
   {
    "synsets": [],
    "h": 44,
    "object_id": 4504988,
    "names": ["fence"],
    "w": 60,
    "attributes": ["black"],
    "y": 316,
    "x": 423,
    "attribute_prompts": [
     ["fence", "brick", 0],
     ["fence", "yellow", 0],
     ["fence", "barbed", 0],
      …
    ],
    "object_prompts": [
     ["black stripe", "black", 0],
     ["black shoe", "black", 0],
     ["label", "black", 0],
       …
    ],
    "norm_w": 0.12,
    "norm_h": 0.12021857923497267,
    "norm_x": 0.846,
    "norm_y": 0.8633879781420765
   },
  ]
 }

attribute_prompts: A list of 3-tuples [object, attribute, boolean] representing a list of attribute-centered questions in the context of the current bounding box. The first two elements is a pair of object and attribute under test, each with a boolean of whether the pair is true for the given instance bounding box. For attribute-centered questions, the object is fixed while the attribute is varied across the total of 50 question-tuples in the list.

object_prompts: Exactly the same as attribute_prompts, except the role of attributes and objects are swapped: now the attribute is fixed while the object is varied across 50 questions.

Details the location and attributes of each coalfield in Victoria. Spatial accuracy defined as attribute "Loc_Acc". This dataset has 3 lookup tables: COALINVFLDS_BLACK and COALINVFLDS_BROWN. Note that 1 field can have many seams. Also COALINVFLDS_WATERUSE LUT joins to this dataset. For all COALINV data the LUT table COALINV_KEY contains the key info for each dataset. Data is from the "Victorian Coal - A 2006 Inventory of Resources" Available via the online store On Line store

Model Methods

The map shows the field capacity of the soils based on 10 dm tread depth (FK10). Derivation based on guide profiles of the soil overview map in scale 1:100,000 (BÜK 100). Classification of FK10 according to the following classification: 130- 260 l/m3 = low, 260 - 390 l/m3 = medium, 390 - 520 l/m3 = high. Attribute fields: VALUE = coding of FK10 (2 = low, 3 = medium, 4 = high, 0 = settlement area and unrated areas). Data was imported into the GDZ and modelled there as values of a multi-feature class, which consists of the spatial feature class GDZ2010.A_gybzst and the business table with the values (GDZ2010.gybzst); then the values for the field capacity parameter for the Saarland viewing room were exported to the filegeodatabase GDZ_GDB. Attribute description s. Access URL

San Francisco International Airport data on Landings Statistics. Airport data is seasonal in nature, therefore any comparative analyses should be done on a period-over-period basis (i.e. January 2010 vs. January 2009) as opposed to period-to-period (i.e. January 2010 vs. February 2010). It is also important to note that fact and attribute field relationships are not always 1-to-1. For example, Aircraft Landings belonging to United Airlines will appear in multiple attribute fields and are additive, which provides flexibility for the user to derive categorical Aircraft Landings Statistics as desired.

This dataset includes all 7 metro counties that have made their parcel data freely available without a license or fees.

This dataset is a compilation of tax parcel polygon and point layers assembled into a common coordinate systems from Twin Cities, Minnesota metropolitan area counties. No attempt has been made to edgematch or rubbersheet between counties. A standard set of attribute fields is included for each county. (See section 5 of the metadata). The attributes are the same for the polygon and points layers. Not all attributes are populated for all counties. Summary attribute information is in the Attributes Overview. Detailed information about the attributes can be found in the MetroGIS Regional Parcels Attributes document.

The polygon layer contains one record for each real estate/tax parcel polygon within each county's parcel dataset. Some counties have polygons for each individual condominium, and others do not. (See Completeness in Section 2 of the metadata for more information.) The points layer includes the same attribute fields as the polygon dataset. The points are intended to provide information in situations where multiple tax parcels are represented by a single polygon. One primary example of this is the condominium, though some counties stacked polygons for condos. Condominiums, by definition, are legally owned as individual, taxed real estate units. Records for condominiums may not show up in the polygon dataset. The points for the point dataset often will be randomly placed or stacked within the parcel polygon with which they are associated.

The polygon layer is broken into individual county shape files. The points layer is provided as both individual county files and as one file for the entire metro area.

In many places a one-to-one relationship does not exist between these parcel polygons or points and the actual buildings or occupancy units that lie within them. There may be many buildings on one parcel and there may be many occupancy units (e.g. apartments, stores or offices) within each building. Additionally, no information exists within this dataset about residents of parcels. Parcel owner and taxpayer information exists for many, but not all counties.

This is a MetroGIS Regionally Endorsed dataset.

Additional information may be available from each county at the links listed below. Also, any questions or comments about suspected errors or omissions in this dataset can be addressed to the contact person at each individual county.

Anoka = http://www.anokacounty.us/315/GIS
Caver = http://www.co.carver.mn.us/GIS
Dakota = http://www.co.dakota.mn.us/homeproperty/propertymaps/pages/default.aspx
Hennepin = http://www.hennepin.us/gisopendata
Ramsey = https://www.ramseycounty.us/your-government/open-government/research-data
Scott = http://opendata.gis.co.scott.mn.us/
Washington: http://www.co.washington.mn.us/index.aspx?NID=1606

Wetlands are areas where water is present at or near the surface of the soil during at least part of the year. Wetlands provide habitat for many species of plants and animals that are adapted to living in wet habitats. Wetlands form characteristic soils, absorb pollutants and excess nutrients from aquatic systems, help buffer the effects of high flows, and recharge groundwater. Data on the distribution and type of wetland play an important role in land use planning and several federal and state laws require that wetlands be considered during the planning process.The National Wetlands Inventory (NWI) was designed to assist land managers in wetland conservation efforts. The NWI is managed by the US Fish and Wildlife Service.Dataset SummaryPhenomenon Mapped: WetlandsGeographic Extent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, American Samoa, and the Northern Mariana IslandsProjection: Web Mercator Auxiliary SphereVisible Scale: This layer preforms well between scales of 1:1,000,000 to 1:1,000. An imagery layer created from this dataset is also available which you can also use to quickly draw wetlands at smaller scales.Source: U.S. Fish and Wildlife ServiceUpdate Frequency: AnnualPublication Date: October 26, 2024This layer was created from the October 26, 2024 version of the NWI. The features were converted from multi-part to a single part using the Multipart To Singlepart tool. Features with more than 50,000 vertices were split with the Dice tool. The Repair Geometry tool was run on the features, using the OGC option.The layer is published with a related table that contains text fields created by Esri for use in the layer's pop-up. Fields in the table are:Popup Header - this field contains a text string that is used to create the header in the default pop-up System Text - this field contains a text string that is used to create the system description text in the default pop-upClass Text - this field contains a text string that is used to create the class description text in the default pop-upModifier Text - this field contains a text string that is used to create the modifier description text in the default pop-upSpecies Text - this field contains a text string that is used to create the species description text in the default pop-upCodes, names, and text fields were derived from the publication Classification of Wetlands and Deepwater Habitats of the United States.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but an imagery layer created from the same data can be used at smaller scales to produce a webmap that displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter for System Name = 'Palustrine' to create a map of palustrine wetlands only.Add labels and set their propertiesCustomize the pop-upArcGIS ProAdd this layer to a 2d or 3d mapUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.