This dataset describes mean flow path distance, in meters, to flowlines from every NHDPlus version 2.1 (NHDv2) flowline in every catchment. A National flow distance raster utilizes NHDPlus Version 2.1 flow direction rasters to compute a national flow path distance raster. For the purposes of this dataset, NHDPlus Version 2 catchments were overlaid to compute a mean flow path distance to flowline for each flowline catchment.

Each record contains information for one unique version of a UPC code.

The UPC codes for some products have additional attributes. There are 19 total extra attributes (each with a “code” and “description” field), however, many products do not have any value listed in these fields.

In the extra attributes, NielsenIQ uses two different values to indicate missing data: “0” and blank (‘’). We have preserved these original values, but both have the same meaning.

In this file, the description associated with the integer code values for each attribute can vary by product_module. In order to determine the description of a particular code for an attribute, not only the code itself is needed but also the product_module. Additionally, the descriptions themselves can vary slightly by year. Ultimately, the product_module, the panel_year and the attribute code value are all needed to determine a code’s meaning.

Note to researchers regarding flavor data for 2010: It is a known issue that the flavor code and flavor description are missing from the 2010 extra attributes file. We have a file available with these missing data. Refer to the spreadsheet “Latest_Flavor_2010.csv” located here: HMS/OpenIssues_SupplementFiles/ExtraAttributes_FlavorCode. This is an interim solution, as we may eventually rework the files to include the data.

This data set contains a collection of attributes associated with CloudSat identified echo objects (or contiguous regions of radar/dBZ echo) from15 June 2006 till 17 January 2013. CloudSat is a NASA satellite that carries a 94 GHz (3 mm) nadir pointing cloud profiling radar (CPR). CloudSat makes approximately 14 orbits per day with an equator passing time of 0130 and 1330 local time. Echo objects were identified using CloudSat's 2B-GEOPROF product that includes 2D arrays (alongtrack x vertical) of the radar reflectivity factor and gaseous attenuation correction. Also included in the product is a "cloud mask" with values ranging between 0 and 40 with higher values indicating a greater likelihood of cloud detection. An EO was defined as a contiguous region of cloud mask greater than or eaqual to 20, consisting of at least three pixels with their edges and not merely their corners touching. Each echo object (EO) is assigned multiple attributes. The geographic attributes include minimum, mean, and maximum latitude and longitude, minimum and maximium location along the CloudSat orbit track, and the underlying surface altitude and land mask data, which allows the EOs to be catagorized as occuring over land, sea, or the coast. The geometric attributes include top, mean, and bottom height, width, and the total number of pixels within the EO. Attributes describing the internal structure of the EO are also available including the number of pixels and cells (i.e., group of pixels) greater than 0 dBZ and -17 dBZ. Finally, the time of day of occurance was also recorded to compare the statistics of EOs ocurring during the daytime versus nighttime. In total, we identified 15,181,193 EOs from 15 June 2006 to 17 January 2013. After 17 April 2011, data were only collected during the day due to a battery failure onboard CloudSat. Each attribute is organized as a 1D array where the size of the array corresponds to the number of EOs. This organization allows subsets of EOs to be easily identified using simple "where" statements when writing code. The attributes were used to identify cloud types and analyze global cloud climatology according to season, surface type, and region (i.e., Riley 2009; Riley and Mapes 2009). The varability of EOs across the MJO was also analyzed (Riley et al. 2011). Methods Data:

Raw files were downloaded from ftp1.cloudsat.cira.colostate.edu in directory 2B-GEOPROF.R04 Processed files are in netcdf format

Processing:

Data were processed and analyzed using IDL. See CloudSat_code_README.txt for details The initial processing was done while I was a graduate student at the Univerisity of Miami working on my masters from 2006-2009 Code is available at https://github.com/erileydellaripa/CYGNSS_code

Data file description:

Once the tar.gz file is unpacked, the EO attributes are provided in the EO_masterlistYYYY.nc files, where YYYY corresponds to the different years. I transferred the EO attributes from IDL .save files to netcdf files for sharing. A description of each EO attribute is provide in the README.md and if you do an ncdump -h in a terminal window.

The attributes are organized in 1D arrays, where the element of each array corresponds to a unique EO and the total size of the array corresponds to the total number of EOs identified.

Data are processed from the start of CloudSat 15 June 2006 till 17 January 2013 for the EO attributes.

In total, there are 15,181,193 EOs.

There was a battery failure 17 April 2011. CloudSat resumed collecting data 27 October 2011, but only during the day.

References:

Riley, E. M., B. E. Mapes, and S. N. Tulich, 2011: Clouds Associated with the Madden-Julian Oscillation: A New Perspective from CloudSat. J. Atmos. Sci., 68, 3032-3051, https://doi.org/10.1175/JAS-D-11-030.1.

Riley, E. M., and B. E. Mapes, 2009: Unexpected peak near -15°C in CloudSat echo top climatology. Geophys. Res. Lett., 36, L09819, https://doi.org/10.1029/2009GL037558.

Riley, E. M., 2009: A global survey of clouds by CloudSat. M.S. thesis, Division of Meteorology and Physical Oceanography, University of Miami, 134 pp, https://scholarship.miami.edu/esploro/outputs/991031447848002976.

This tabular data set represents mean annual water balance variables as described in Wolock and McCabe (2017) for the period 2000-2014, detrended to 2012, compiled for the NHDPlus version 2.1 data suite (NHDPlusV2) for the conterminous United States. The detrended variables (DT_XXX where XXX is the variable) included are: actual evapotranspiration (DT_AET) , potential evapotranspiration (PET), precipitation (DT_PPT), runoff (DT_RUN) , percent of annual precipitation as snow (DT_SNO) , soil moisture storage (DT_STO), and temperature (DT_TAV). Linkage of these data with NHDPlusV2 is achieved through the common unique identifier COMID. The values are estimated both for: 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. The reach catchment information characterizes data at the local scale, whereas the catchments accumulated through the river network characterize cumulative upstream conditions. The network-accumulated values are ...

Attribute reclassification for fixed amplitude and varying Cmin.

Building polygons were created in February 2013 by Geoscience Australia by manually digitising the outline of each building off the 2011 orthophotography. Digitisation was done from scratch off the 2011 orthophotography within Quantum GIS. Using the ArcMap 'zonal statistics' tool the minimum, mean and maximum heights were found for each building polygon from the 2011 digital elevation model and the 2011 digital surface model (DSM). This information was then joined to the building polygon attribute table. To find the building height from ground to roof, the difference between the Mean DSM and mean DEM was calculated and added as a field to the attribute table. To find the maximum height of each building the difference between the Maximum DSM and Mean DEM was calculated. Polygon area, perimeter, and x and y coordinates of each building were also attached as attributes. Accuracy is high as the layer was based on the 2011 orthophotography. Error may have been introduced through the digitisation process. Building lean in the orthophotography may also contribute to polygons which are slightly inaccurately placed. Height attribute accuracy is inaccurate for building polygons which have tree cover above them, as the tree elevation would influence the digital surface model. Particularly the Max_height field may include tree heights rather than building heights in some cases. Attribute accuracy could be improved by using the raw 2011 lidar data (.las files) which are classified at 'buildings' to attach heights. This method was tested and was extremely time consuming - only the height_max field was significantly improved. Disclaimer

This data set represents the average annual R-factor, rainfall-runoff erosivity measure, compiled for every catchment of NHDPlus for the conterminous United States. The source data are from Christopher Daly of the Spatial Climate Analysis Service, Oregon State University, and George Taylor of the Oregon Climate Service, Oregon State University (2002), who developed spatially distributed estimates of R-factor for the period 1971-2000 for the conterminous United States.

The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and t ...

This dataset supports the manuscript "Forest attribute maps: a support for small area estimation of forest disturbances" published in Annals of Forest Sciences (DOI: 10.1186/s13595-025-01293-8). It contains two files saved in parquet format, which can be read using library arrow in R.

Due of restrictions associated with the use of NFI data, the datasets do not contain any spatial information and map data are restricted to the pixels describing the areas impacted by bark-beetle outbreaks such as detected using the FORDEAD approach. Additional data are available from the authors upon reasonable request and with permission of IGN.

File plot.parquet contains growing stock volume and basal area from NFI surveys, as well as the auxiliary data used to compute the forest attribute maps. The auxiliary data were extracted at a 30 m resolution.

The file includes the following attributes:

• "nfi_year" : year of the NFI field survey
• "forest_type" : dominant forest type i.e. conifer or harwood
• "specific_model" : binary field indicating if the plot have been used (1) or not (0) to model forest attributes with the specific model (spruce and fir stands only)
• "growing_stock_volume_in_m3_per_ha" : plot level nfi growing stock volume in cubic meter per hectare
• "basal_area_in_m2_per_ha" : plot level nfi basal area in squared meter per hectare
• "total_inner_canopy_volume" : volume of canopy dervied from a lidar canopy height model (see table 7 of the manuscript for computational details)
• "gap_area" : surface of the lidar canopy height model below 5 m (see table 7 of the manuscript for computational details)
• "second_cummulative_percentage_of_returns" : cumulative percentage of return in the 2^th layer according to Woods et al. (2008); https://doi.org/10.5558/tfc84827-6)
• "near_infrared_band8" : mean value of the mean infrared band of Sentinel.

The file map_bark_beetle.parquet contains auxiliary data over FORDEAD polygons in the area of interest, computed at 30 m resolution.

The file included the following attributes:

• "rowid" : line index of the map
• "columnid" : column index of the map
• "total_inner_canopy_volume" : volume of canopy derived from a lidar canopy height model (see table 7 of the manuscript for computational details)
• "gap_area" : surface of the lidar canopy height model below 5 m (see table 7 of the manuscript for computational details)
• "second_cummulative_percentage_of_returns" : cumulative percentage of return in the 2^th layer according to Woods et al. (2008); https://doi.org/10.5558/tfc84827-6)
• "near_infrared_band8" : mean value of the mean infrared band of Sentinel.
• "proportion_of_bark_beetle_area_in_m2_in_xxx_in_class_y" : proportion of the pixel detected by FORDEAD method as an anomalie in Sentinel data for the year "xxxx" and a given intensity class (y). The attribute is dennoted prop_outbreak in the manuscript.

Overview The Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. The current edition is version 11.4 (published 24 February 2025). The 11.4 release contains updated boundary lines and data refinements designed to extend the functionality of the dataset. These data and generalized derivatives are the only international boundary lines approved for U.S. Government use. The contents of this dataset reflect U.S. Government policy on international boundary alignment, political recognition, and dispute status. They do not necessarily reflect de facto limits of control. National Geospatial Data Asset This dataset is a National Geospatial Data Asset (NGDAID 194) managed by the Department of State. It is a part of the International Boundaries Theme created by the Federal Geographic Data Committee. Dataset Source Details Sources for these data include treaties, relevant maps, and data from boundary commissions, as well as national mapping agencies. Where available and applicable, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery process includes analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground. Cartographic Visualization The LSIB is a geospatial dataset that, when used for cartographic purposes, requires additional styling. The LSIB download package contains example style files for commonly used software applications. The attribute table also contains embedded information to guide the cartographic representation. Additional discussion of these considerations can be found in the Use of Core Attributes in Cartographic Visualization section below. Additional cartographic information pertaining to the depiction and description of international boundaries or areas of special sovereignty can be found in Guidance Bulletins published by the Office of the Geographer and Global Issues: https://data.geodata.state.gov/guidance/index.html Contact Direct inquiries to internationalboundaries@state.gov. Direct download: https://data.geodata.state.gov/LSIB.zip Attribute Structure The dataset uses the following attributes divided into two categories: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | Core CC1_GENC3 | Extension CC1_WPID | Extension COUNTRY1 | Core CC2 | Core CC2_GENC3 | Extension CC2_WPID | Extension COUNTRY2 | Core RANK | Core LABEL | Core STATUS | Core NOTES | Core LSIB_ID | Extension ANTECIDS | Extension PREVIDS | Extension PARENTID | Extension PARENTSEG | Extension These attributes have external data sources that update separately from the LSIB: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | GENC CC1_GENC3 | GENC CC1_WPID | World Polygons COUNTRY1 | DoS Lists CC2 | GENC CC2_GENC3 | GENC CC2_WPID | World Polygons COUNTRY2 | DoS Lists LSIB_ID | BASE ANTECIDS | BASE PREVIDS | BASE PARENTID | BASE PARENTSEG | BASE The core attributes listed above describe the boundary lines contained within the LSIB dataset. Removal of core attributes from the dataset will change the meaning of the lines. An attribute status of “Extension” represents a field containing data interoperability information. Other attributes not listed above include “FID”, “Shape_length” and “Shape.” These are components of the shapefile format and do not form an intrinsic part of the LSIB. Core Attributes The eight core attributes listed above contain unique information which, when combined with the line geometry, comprise the LSIB dataset. These Core Attributes are further divided into Country Code and Name Fields and Descriptive Fields. County Code and Country Name Fields “CC1” and “CC2” fields are machine readable fields that contain political entity codes. These are two-character codes derived from the Geopolitical Entities, Names, and Codes Standard (GENC), Edition 3 Update 18. “CC1_GENC3” and “CC2_GENC3” fields contain the corresponding three-character GENC codes and are extension attributes discussed below. The codes “Q2” or “QX2” denote a line in the LSIB representing a boundary associated with areas not contained within the GENC standard. The “COUNTRY1” and “COUNTRY2” fields contain the names of corresponding political entities. These fields contain names approved by the U.S. Board on Geographic Names (BGN) as incorporated in the ‘"Independent States in the World" and "Dependencies and Areas of Special Sovereignty" lists maintained by the Department of State. To ensure maximum compatibility, names are presented without diacritics and certain names are rendered using common cartographic abbreviations. Names for lines associated with the code "Q2" are descriptive and not necessarily BGN-approved. Names rendered in all CAPITAL LETTERS denote independent states. Names rendered in normal text represent dependencies, areas of special sovereignty, or are otherwise presented for the convenience of the user. Descriptive Fields The following text fields are a part of the core attributes of the LSIB dataset and do not update from external sources. They provide additional information about each of the lines and are as follows: ATTRIBUTE NAME | CONTAINS NULLS RANK | No STATUS | No LABEL | Yes NOTES | Yes Neither the "RANK" nor "STATUS" fields contain null values; the "LABEL" and "NOTES" fields do. The "RANK" field is a numeric expression of the "STATUS" field. Combined with the line geometry, these fields encode the views of the United States Government on the political status of the boundary line. ATTRIBUTE NAME | | VALUE | RANK | 1 | 2 | 3 STATUS | International Boundary | Other Line of International Separation | Special Line A value of “1” in the “RANK” field corresponds to an "International Boundary" value in the “STATUS” field. Values of ”2” and “3” correspond to “Other Line of International Separation” and “Special Line,” respectively. The “LABEL” field contains required text to describe the line segment on all finished cartographic products, including but not limited to print and interactive maps. The “NOTES” field contains an explanation of special circumstances modifying the lines. This information can pertain to the origins of the boundary lines, limitations regarding the purpose of the lines, or the original source of the line. Use of Core Attributes in Cartographic Visualization Several of the Core Attributes provide information required for the proper cartographic representation of the LSIB dataset. The cartographic usage of the LSIB requires a visual differentiation between the three categories of boundary lines. Specifically, this differentiation must be between: International Boundaries (Rank 1); Other Lines of International Separation (Rank 2); and Special Lines (Rank 3). Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary. Please consult the style files in the download package for examples of this depiction. The requirement to incorporate the contents of the "LABEL" field on cartographic products is scale dependent. If a label is legible at the scale of a given static product, a proper use of this dataset would encourage the application of that label. Using the contents of the "COUNTRY1" and "COUNTRY2" fields in the generation of a line segment label is not required. The "STATUS" field contains the preferred description for the three LSIB line types when they are incorporated into a map legend but is otherwise not to be used for labeling. Use of the “CC1,” “CC1_GENC3,” “CC2,” “CC2_GENC3,” “RANK,” or “NOTES” fields for cartographic labeling purposes is prohibited. Extension Attributes Certain elements of the attributes within the LSIB dataset extend data functionality to make the data more interoperable or to provide clearer linkages to other datasets. The fields “CC1_GENC3” and “CC2_GENC” contain the corresponding three-character GENC code to the “CC1” and “CC2” attributes. The code “QX2” is the three-character counterpart of the code “Q2,” which denotes a line in the LSIB representing a boundary associated with a geographic area not contained within the GENC standard. To allow for linkage between individual lines in the LSIB and World Polygons dataset, the “CC1_WPID” and “CC2_WPID” fields contain a Universally Unique Identifier (UUID), version 4, which provides a stable description of each geographic entity in a boundary pair relationship. Each UUID corresponds to a geographic entity listed in the World Polygons dataset. These fields allow for linkage between individual lines in the LSIB and the overall World Polygons dataset. Five additional fields in the LSIB expand on the UUID concept and either describe features that have changed across space and time or indicate relationships between previous versions of the feature. The “LSIB_ID” attribute is a UUID value that defines a specific instance of a feature. Any change to the feature in a lineset requires a new “LSIB_ID.” The “ANTECIDS,” or antecedent ID, is a UUID that references line geometries from which a given line is descended in time. It is used when there is a feature that is entirely new, not when there is a new version of a previous feature. This is generally used to reference countries that have dissolved. The “PREVIDS,” or Previous ID, is a UUID field that contains old versions of a line. This is an additive field, that houses all Previous IDs. A new version of a feature is defined by any change to the

Attribute Amnesia refers to the phenomenon that subjects fail to report some features (e.g., colour) of a stimulus after they have been asked to repetitively report some other features (e.g., numeric parity) of the same stimulus. This effect has shown strong replicability in the multi-stimuli design, in which there is normally one target among three distractor stimuli. Recently, Wang et al. (2021) furtherly showed the robustness of this effect when there was only one stimulus on the screen. As the main experiment of the current project will be conducted online, we aim to pre-register a replication attempt of Experiment 1 of Wang et al. (2021) in the online-experiment setting. This study shows that one can indeed replicate previous in-lab experiments on attribute amnesia in the online setting, demonstrating convincing online data quality for serious investigations. The functions of Webcam-based eyetracking from Labvanced also make rigorous environmental control and recording saccadic dat..., A short textual report on the study including introduction, method, results and conclusion information is attached to this deposition (see PILOTEXP1.pdf in supplemental material)., , # Replicating Attribute Amnesia Effect in the Single-Stimulus Design

Description of the data and file structure

Raw behavioral data header meaning (dataset name: AAPTN100)

• Rec_Session_Id: Subject ID
• X: Row number
• Block_Nr: Block number (1)
• Block_Name: Block name (block_1)
• Task_Nr: Task number (1:introduction ; 2: pre-surprise task; 3: surprise task)
• Task_Name: Task name (Instruction: introduction ; PreSurpParityT: pre-surprise task; PostSurpColorParityT: surprise task)
• Trial_Nr: Trial number (1-32; 1-27 pre-surprise trials; 28-32 surprise + post-surprise trials)
• Trial_Id: Trial ID (1-32)
• Condition_Id: Condition ID (1-32)
• CorrectPostCol: Correctness of surprise color task (on 28-32th trials only)
• CorrectPre: correctness of pre-surprise parity task (on all trials)
• RT_Fixation: Fixation frame duration in ms
• RT_ResponsePostCol: RT on surprise color response in ms (on 28-32th trials only)
• RT_ResponsePre: RT on pre-surprise ...

This tabular data set represents 30 year (1971 - 2000) mean annual maximum temperature (Celsius) from 800m PRISM data compiled for two spatial components of the NHDPlus version 2 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data for 30 year (1971 - 2000) mean annual maximum temperature (Celsius) from 800m PRISM data was produced by the PRISM Group at Oregon State University. Units are degrees Celsius. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).

The New York State and National Register listings geographic dataset for Onondaga County was originally developed by the New York State Historic Preservation Office (NYSHPO) in May, 2004. Shortly after its production, this dataset was delivered to the Syracuse-Onondaga County Planning Agency (SOCPA) for use in the Agency's GIS database. Minor annual edits to the file have been conducted by SOCPA staff to ensure completeness and positional agreement with Onondaga County's parcel file and NYS orthophotography. The resulting file uses polygon features to represent both NYS/NR sites and NYS/NR districts in an ESRI shapefile format. All features and attributes were retained from the original NYSHPO dataset. Two additional attributes were created by SOCPA staff, one to classify polygon features as either sites or districts for cartographic display and one to identify the municipality.Data Dictionary:Attribute:Attribute Label: RESNAMEAttribute Definition: Feature NameAttribute Definition Source: NYSHPOAttribute:Attribute Label: ADDRESSAttribute Definition: Physical addressAttribute Definition Source: NYSHPOAttribute:Attribute Label: SRDATEAttribute Definition: unknownAttribute:Attribute Label: CNTYFIPSAttribute Definition: County FIPS codeAttribute Definition Source: NYSHPOAttribute:Attribute Label: COUNTYAttribute Definition: General location of featureAttribute Definition Source: NYSHPOAttribute:Attribute Label: CITYAttribute Definition: LocationAttribute Definition Source: NYSHPOAttribute:Attribute Label: CERTCDAttribute Definition: National Register Listings Code DefinitionsAttribute Definition Source: NYSHPOEnumerated Domain Value: FEEnumerated Domain Value Definition: State Register listed; determined eligible for National Register listing by the Keeper of the National RegisterEnumerated Domain Value Definition Source: NYSHPOAttribute Domain Values:Enumerated Domain Value: LIEnumerated Domain Value Definition: State and National Register listedEnumerated Domain Value Definition Source: NYSHPOAttribute Domain Values:Enumerated Domain Value: SREnumerated Domain Value Definition: State Register listed only; in most cases, National Register listing pendingEnumerated Domain Value Definition Source: NYSHPOAttribute Domain Values:Enumerated Domain Value: XXEnumerated Domain Value Definition: Listing in progressEnumerated Domain Value Definition Source: NYSHPOAttribute:Attribute Label: NRDATEAttribute Definition: unknownAttribute:Attribute Label: MUNICIPALIAttribute Definition: Municipality of featureAttribute Definition Source: SOCPAAttribute:Attribute Label: ShapeAttribute Definition: Feature geometry.Attribute Definition Source: ESRIAttribute:Attribute Label: NRNUMBERAttribute:Attribute Label: TYPEAttribute Definition: Differentiates features into districts and sitesAttribute Definition Source: SOCPA

The attribute tables holds the following information:LEED_RATE - Gold, Silver, Platinum based on official LEED certification. Includes all buildings in the SOMA district. Includes LEED buildings that are not owned by PSU.BUILDINGID - Building acronym. Do not use this as a building unique identifier. LONGNAME - Official long name of building.Owned_Leas - Owned means PSU owns, manages and operates building for PSU use. Leased means the building is being leased (Crown Plaza, Pepco and UTS). Partnership means that PSU is in contract or agreement to use the space (CLSB). Other means PSU does not own or operate building. This data may change up to 2-3 times a year. The Campus Planning Office maintains this information. BLDID_AIM - The Building identification number matches the unique identifier of the Asset Information Managment database (AssetWorks, AimCAD, or the work order request system). Photo - Intended to be an attribute hotlink field, data has not been updated to match the new capital projects website address. Housing - Yes means the building is housing, includes private and PSU housing. Non-PSU housing data sourced from the City of Portland buildings database. ShortName - Shortened building name to accomodate labeling. Seismic - Indicates whether a building has had any seismic retrofit. For additional information ask Capital Archivist Bryce Henry.All other attribute data is sourced from the City of Portland buildings GIS database and additional metadata can be found here: http://www.civicapps.org/datasets/building-footprints-portland

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 dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.2839 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.026 and 0.0089 (in million kms), corressponding to 9.1664% and 3.1261% respectively of the total road length in the dataset region. 0.249 million km or 87.7075% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0003 million km of information (corressponding to 0.1046% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

This dataset represents predictions made to individual, local NHDPlusV2 stream segments. Attributes were calculated for every local NHDPlusV2 stream segment. (See Supplementary Info for Glossary of Terms). These predictions were made to provide estimates of reference-condition stream temperatures in support of the 2008-2009 and 2013-2014 (forthcoming) National Rivers and Streams Assessments. These predictions were based on a set of published models (Hill et al. 2013; http://www.journals.uchicago.edu/doi/abs/10.1899/12-009.1). From Hill et al. (2013): "We modeled 3 ecologically important elements of the thermal regime: mean summer, mean winter, and mean annual stream temperature. These models used a set of least-disturbed USGS stations and sites to model stream temperatures from a set of landscape metrics. To build reference-condition models, we used daily mean ST data obtained from several thousand US Geological Survey temperature sites distributed across the conterminous USA and iteratively modeled ST with Random Forests to identify sites in reference condition. These data are summarized to produce local stream segment-level metrics as a continuous data type.

This tabular data set represents estimated the 30 year (1951-1980) mean annual natural groundwater recharge compiled for two spatial components of the NHDPlus version 2 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data for estimated mean annual natural groundwater recharge was produced by the United States Geological Survey (Wolock, 2003). Units are millimeters per year. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate ( ...

This tabular data set represents the average annual R-factor, rainfall-runoff erosivity measure, compiled for every MRB_E2RF1 catchment of selected Major River Basins (MRBs, Crawford and others, 2006). The source data are from Christopher Daly of the Spatial Climate Analysis Service, Oregon State University, and George Taylor of the Oregon Climate Service, Oregon State University (2002).

The ERF1_2 catchments are based on a modified version of the U.S. Environmental Protection Agency's (USEPA) ERF1_2 and include enhancements to support national and regional-scale surface-water quality modeling (Nolan and others, 2002; Brakebill and others, 2011).

Data were compiled for every MRB_E2RF1 catchment for the conterminous United States covering New England and Mid-Atlantic (MRB1), South Atlantic-Gulf and Tennessee (MRB2), the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy (MRB3), the Missouri (MRB4), the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf (MRB ...

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.

This is Version 1 of the Australian pH (Water) product of the Soil and Landscape Grid of Australia.

The map gives a modelled estimate of the spatial distribution of soil pH (1:5 soil water solution) in soils across Australia.

The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. Each product contains six digital soil attribute maps, and their upper and lower confidence limits, representing the soil attribute at six depths: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. These depths are consistent with the specifications of the GlobalSoilMap.net project (https://esoil.io/TERNLandscapes/Public/Pages/SLGA/Resources/GlobalSoilMap_specifications_december_2015_2.pdf). The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).

Detailed information about the Soil and Landscape Grid of Australia can be found at - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/index.html

Attribute Definition: pH of a 1:5 soil water solution Units: None; Period (temporal coverage; approximately): 1950-2021; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 18; Number of pixels with coverage per layer: 2007M (49200 * 40800); Data license : Creative Commons Attribution 4.0 (CC BY); Target data standard: GlobalSoilMap specifications; Format: Cloud Optimised GeoTIFF; Lineage: A full description of the methods used to generate this product can be found at - https://aussoilsdsm.esoil.io/slga-version-2-products/soil-ph-15-water

We used a Random Forest model to fit the relationship between measurements and covariates. The Random Forest model uses the bootstrap resampling approach to iteratively develop the relationships between target variable and predictor variables.

Our modelling also included a repeated (n =50) bootstrap resampling approach but was different in that on each iteration the selected data which were also field data had to be converted to a ‘lab’ measurement. This ‘lab’ measurement was derived by drawing a value at random from the empirical distribution corresponding to the field measurement. In this way, we can incorporate into the modelling, the observed variability that is associated with field measurements, which also provides a seamless way to incorporate both data types.

The process of spatial modelling was relatively standard after the data integration step was done. Models were developed for each specified depth interval: 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm, 100-200cm. Our investigations also revealed there was some benefit to modelling the Random Forest model residuals using variograms. Together models were evaluated using a data set of size 10000 sites, meaning that the number of cases to evaluate models differed with each depth interval as more cases are found at the surface and near surface and drop off with increasing soil depth. We used the prediction interval coverage probability to assess the veracity of the uncertainty quantifications.

Soil pH mapping was output to the ~90m grid resolution in accordance with SLGA specifications.

All processing for the generation of these products was undertaken using the R programming language. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Code - https://github.com/AusSoilsDSM/SLGA Observation data - https://esoil.io/TERNLandscapes/Public/Pages/SoilDataFederator/SoilDataFederator.html Covariate rasters - https://esoil.io/TERNLandscapes/Public/Pages/SLGA/GetData-COGSDataStore.html