This file contains participant response data to Likert scale, open-ended responses and self-reported time taken to complete various tasks related to the extraction exercise. This Excel file also contains: 1) Examples of the Interactive HAWC Visuals that can be created after extracting data into the template. 2) The Initial Post-Extraction Survey Tool ("Survey 1") 3) The Final Post-Pilot Survey Tool ("Survey 2") 4) Survey 2 Results: Willingness to Consider Structured Data During Publication Process (Table 2) 5) Survey 1 Results: Participant Self-Reported Time Spent Performing Various Pilot Tasks (Table 3) 6) Survey 1 Results: Summary of Technical Assistance Provided by Team Members (Table 4) 7) Survey 2 Results: Participant Responses Describing Pilot's Impact on Future Research Activities (Table 5) 8) Survey 1 Results: Initial Survey Likert Scale Results (Table 6) 9) Repeat Extraction: Comparison of the First and Second Data Extraction Experience (Among the Same Participant) 10) Survey 1 Results: Problematic & Easy Fields to Extract. This dataset is associated with the following publication: Wilkins, A., P. Whaley, A. Persad, I. Druwe, J. Lee, M. Taylor, A. Shapiro, N. Blanton, C. Lemeris, and K. Thayer. Assessing author willingness to enter study information into structured data templates as part of the manuscript submission process: A pilot study. Heliyon. Elsevier B.V., Amsterdam, NETHERLANDS, 8(3): 1-9, (2022).

Jointly managed by NASA and the USGS, Landsat is the longest running spaceborne earth imaging and observation program in history. Landsat Collection 2 Level-2 science products, imagery from 1982 to present, are made publicly available by the USGS. The continuity in this scientific record allows for critical and reliable observation and analysis of Earth processes and changes over time. This imagery layer provides global Landsat 4, 5, 7, 8, and 9 imagery. The layer is time-enabled and includes a number of predefined processing templates for visualization and analysis. Key PropertiesGeographic Coverage: Global landmassesTemporal Coverage: August 22, 1982 to presentSpatial Resolution: 30-meterRevisit Time: ~8-daysProduct Level: Collection 2 Level-2 Science Products (Surface Reflectance and Surface Temperature)Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Analysis: Optimized for analysisMultispectral Bands:BandDescriptionWavelength* (µm)Spatial Resolution (m)1Coastal aerosol**0.43 - 0.45302Blue0.45 - 0.52303Green0.52 - 0.60304Red0.63 - 0.69305NIR0.76 - 0.90306SWIR 11.55 - 1.75307SWIR 22.08 - 2.35308Pixel QANA309Surface Temperature (Kelvin)10.4-12.5 30***10Surface Temperature QANA30*This is the max range for each band based on the combined missions. For reference to the distinct ranges for each mission see this document.**Coastal Aerosol is only available from Landsat 8 and 9. For Landsat 4, 5, and 7 this band is simply a place holder and does not contain data.***The thermal band is acquired at 100 or 120 meter resolution and resampled to 30 meters.Learn more about the Quality Assessment (QA) Bands Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and calculations for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer is Natural Color (bands 4,3,2) for Visualization.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent and most cloud free scenes from the Landsat archive are prioritized and dynamically fused into a single mosaicked image layer. To discover and isolate specific images for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Using the "None" processing template option as input to analysis provides all bands with raw pixel values and is recommended for many use cases. Otherwise, only processing templates that include a "for analysis" designation should be used as input to analysis.The appropriate scale and offset, as recommended by USGS, is dynamically applied to the imagery in this layer, providing scientific floating point Surface Reflectance and Surface Temperature pixel values.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer. GeneralIf you are new to Landsat imagery, the Landsat Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide or this Detailed Tutorial. Data Source Landsat imagery is credited to the United States Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). The imagery in this layer is sourced from the Microsoft Planetary Computer Open Data Catalog.

An excel template with data elements and conventions corresponding to the openLCA unit process data model. Includes LCA Commons data and metadata guidelines and definitions Resources in this dataset:Resource Title: READ ME - data dictionary. File Name: lcaCommonsSubmissionGuidelines_FINAL_2014-09-22.pdfResource Title: US Federal LCA Commons Life Cycle Inventory Unit Process Template. File Name: FedLCA_LCI_template_blank EK 7-30-2015.xlsxResource Description: Instructions: This template should be used for life cycle inventory (LCI) unit process development and is associated with an openLCA plugin to import these data into an openLCA database. See www.openLCA.org to download the latest release of openLCA for free, and to access available plugins.

This dataset contains the QALD7-training dataset converted to Neural SPARQL Machine (NSpM) templates to Question-Answering (QA) over DBpedia Knowledgebase.

This Color Infrared imagery layer features recent high-resolution (1-meter or better) aerial imagery for the continental United States, made available by the USDA Farm Production and Conservation Business Center (FPAC). The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental United States. Approximately half of the US is collected each year and each state is typically collected every other year.This imagery layer is updated annually as new imagery is made available. The NAIP program aims to make the imagery available to governmental agencies and to the public within a year of collection. The imagery is published in 4-bands (Red, Green, Blue, and Near Infrared) where available. Additional NAIP renderings include Natural Color and NDVI (Normalized Difference Vegetation Index). Key PropertiesGeographic Coverage: Continental United States (Hawaii and Puerto Rico available for some years)Temporal Coverage: 2010 to 2023Spatial Resolution: 0.3-meter to 1-meterRevisit Time: Typically every other yearSource Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Analysis: Optimized for analysisMultispectral Bands:BandDescriptionSpatial Resolution (m)1Red0.3 - 12Green0.3 - 13Blue0.3 - 14Near Infrared0.3 - 1 Usage Tips and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and calculations for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer is Color Infrared (bands 4,1,2) for Visualization.If natural color visualization is your primary use case for NAIP, you might consider using the NAIP Imagery tile layer for optimal display performance.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent imagery available for a given area is prioritized and dynamically fused into a single mosaicked image layer. To discover and isolate specific images for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Using the "None" processing template option as input to analysis provides all bands with raw pixel values and is recommended for many use cases. Otherwise, only processing templates that include a "for analysis" designation should be used as input to analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific year, year range, state, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer. Data SourceNAIP imagery is credited to the United States Department of Agriculture (USDA) Farm Production and Conservation Business Center (FPAC). All imagery in this layer was is sourced from the NAIP Registry of Open Data on AWS.

The dataset includes gziped NIfTI files (.nii.gz) of MRI T2WI and DTI metrics (FA, MD, RD, and AD) in-vivo data; the ages of the data are 1,3,6,9,12,15,18 months old.
The creation process of these averaged images is referred to the script of FSL software (https://fsl.fmrib.ox.ac.uk/), fslvm_2_template, partially modified; the non-linear registration process was replaced to the script of ANTs (http://stnava.github.io/ANTs/), antsRegsitrationSyN.sh. The details are described in the text “Dataset Description and Image Processing”.
In addition, age-specific DWI templates (.mif files) are provided, which are created using the script of Mrtrix3 (http://www.mrtrix.org/), population_template. You may convert .mif file to NIFTI, extracting b-vector and b-value information by conducting the following Mrtrix3’s command: # mrconvert dwi_input.mif dwi_output.nii -export_grad_fsl bvec bval
Files can be downloaded each image type or as one zip file.

Jointly managed by NASA and the USGS, Landsat is the longest running spaceborne earth imaging and observation program in history. Landsat Collection 2 Level-2 science products, imagery from 1982 to present, are made publicly available by the USGS. The continuity in this scientific record allows for critical and reliable observation and analysis of Earth processes and changes over time.

This imagery layer provides global Landsat 4, 5, 7, 8, and 9 imagery. The layer is time-enabled and includes a number of predefined processing templates for visualization and analysis.



Key Properties


Geographic Coverage: Global landmasses
Temporal Coverage: August 22, 1982 to present
Spatial Resolution: 30-meter
Revisit Time: ~8-days
Product Level: Collection 2 Level-2 Science Products (Surface Reflectance and Surface Temperature)
Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84
Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)
Analysis: Optimized for analysis



    Multispectral Bands:






            Band




            Description




            Wavelength* (µm)




            Spatial Resolution (m)






            1




            Coastal aerosol**




            0.43 - 0.45




            30






            2




            Blue




            0.45 - 0.52




            30






            3




            Green




            0.52 - 0.60




            30






            4




            Red




            0.63 - 0.69




            30






            5




            NIR




            0.76 - 0.90




            30






            6




            SWIR 1




            1.55 - 1.75




            30






            7




            SWIR 2




            2.08 - 2.35




            30






            8




            Pixel QA




            NA




            30






            9




            Surface Temperature (Kelvin)




            10.4-12.5





            30***






            10




            Surface Temperature QA




            NA




            30







  *This is the max range for each band based on the combined missions. For reference to the distinct ranges for each mission see this document.


  **Coastal Aerosol is only available from Landsat 8 and 9. For Landsat 4, 5, and 7 this band is simply a place holder and does not contain data.


  ***The thermal band is acquired at 100 or 120 meter resolution and resampled to 30 meters.


  Learn more about the Quality Assessment (QA) Bands

Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and calculations for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis.VisualizationThe default rendering on this layer is Natural Color (bands 4,3,2) for Visualization.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent and most cloud free scenes from the Landsat archive are prioritized and dynamically fused into a single mosaicked image layer. To discover and isolate specific images for visualization in Map Viewer, try using the Image Collection Explorer.AnalysisOptimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Using the "None" processing template option as input to analysis provides all bands with raw pixel values and is recommended for many use cases. Otherwise, only processing templates that include a "for analysis" designation should be used as input to analysis.The appropriate scale and offset, as recommended by USGS, is dynamically applied to the imagery in this layer, providing scientific floating point Surface Reflectance and Surface Temperature pixel values.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer.GeneralIf you are new to Landsat imagery, the Landsat Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide or this Detailed Tutorial. 


  Data Source

  Landsat imagery is credited to the United States Geological Survey (USGS) and the National Aeronautics and Space

Notice: this is not the latest Heat Island Anomalies image service. For 2023 data visit https://tpl.maps.arcgis.com/home/item.html?id=e89a556263e04cb9b0b4638253ca8d10.This layer contains the relative degrees Fahrenheit difference between any given pixel and the mean heat value for the city in which it is located, for every city in the contiguous United States. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2021, with patching from summer of 2020 where necessary.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter or cooler than the average temperature for that same city as a whole. This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

Symmetric, multi-modal, isotropic, and cropped templates from the Kirby (i.e., MMRR) data set.

This webmap is a subset of Global Landcover 1992 - 2020 Image Layer. You can access the source data from here. This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years 1992-2020. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2020Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafterWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.Land Cover ProcessingTo provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015.Source dataThe datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.phpCitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

The highest result is shown in bold.

Efficiently selecting task-relevant objects during visual search depends on foreknowledge of their defining characteristics, which are represented within attentional templates. These templates bias attentional processing toward template-matching sensory signals and are assumed to become anticipatorily activated prior to search display onset. However, a direct neural signal for such preparatory template activation processes has so far remained elusive. Here, we introduce a new high-definition rapid serial probe presentation paradigm (RSPP–HD), which facilitates high temporal resolution tracking of target template activation processes in real time via monitoring of the N2pc component. In the RSPP–HD procedure, task-irrelevant probe displays are presented in rapid succession throughout the period between task-relevant search displays. The probe and search displays are homologously formed by lateralized “clouds” of colored dots, yielding probes that occur at task-relevant locations without confounding template-guided and salience-driven attentional shifts. Target color probes appearing at times when a corresponding target template is active should attract attention, thereby eliciting an N2pc. In a condition where new probe displays appeared every 50 ms, probe N2pcs were reliably elicited during the final 800 ms prior to search display onset, increasing in amplitude toward the end of this preparation period. Analogous temporal profiles were also observed with longer intervals between probes. These findings show that search template activation processes are transient and that their temporal profile can be reliably monitored at high-sampling frequencies with the RSPP–HD paradigm. This procedure offers a new route to approach various questions regarding the content and temporal dynamics of attentional control processes.

This Normalized Difference Vegetation Index (NDVI) layer features recent high-resolution (1-meter or better) aerial imagery for the continental United States, made available by the USDA Farm Production and Conservation Business Center (FPAC). The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental United States. Approximately half of the US is collected each year and each state is typically collected every other year.This imagery layer is updated annually as new imagery is made available. The NAIP program aims to make the imagery available to governmental agencies and to the public within a year of collection. The imagery is published in 4-bands (Red, Green, Blue, and Near Infrared) where available. Additional NAIP renderings include Natural Color and Color Infrared. Key PropertiesGeographic Coverage: Continental United States (Hawaii and Puerto Rico available for some years)Temporal Coverage: 2010 to 2023Spatial Resolution: 0.3-meter to 1-meterRevisit Time: Typically every other yearSource Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Analysis: Optimized for analysisMultispectral Bands:BandDescriptionSpatial Resolution (m)1Red0.3 - 12Green0.3 - 13Blue0.3 - 14Near Infrared0.3 - 1 Usage Tips and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and calculations for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer is NDVI ((Red - Near Infrared) / (Red + Near Infrared)).If natural color visualization is your primary use case for NAIP, you might consider using the NAIP Imagery tile layer for optimal display performance.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent imagery available for a given area is prioritized and dynamically fused into a single mosaicked image layer. To discover and isolate specific images for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Using the "None" processing template option as input to analysis provides all bands with raw pixel values and is recommended for many use cases. Otherwise, only processing templates that include a "for analysis" designation should be used as input to analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific year, year range, state, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer. Data SourceNAIP imagery is credited to the United States Department of Agriculture (USDA) Farm Production and Conservation Business Center (FPAC). All imagery in this layer was is sourced from the NAIP Registry of Open Data on AWS.

This layer contains the relative heat severity for every pixel for every city in the United States. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summers of 2019 and 2020.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Pete.Aniello@tpl.org with feedback.Terms of UseYou understand and agree, and will advise any third party to whom you give any or all of the data, that The Trust for Public Land is neither responsible nor liable for any viruses or other contamination of your system arising from use of The Trust for Public Land’s data nor for any delays, inaccuracies, errors or omissions arising out of the use of the data. The Trust for Public Land’s data is distributed and transmitted "as is" without warranties of any kind, either express or implied, including without limitation, warranties of title or implied warranties of merchantability or fitness for a particular purpose. The Trust for Public Land is not responsible for any claim of loss of profit or any special, direct, indirect, incidental, consequential, and/or punitive damages that may arise from the use of the data. If you or any person to whom you make the data available are downloading or using the data for any visual output, attribution for same will be given in the following format: "This [document, map, diagram, report, etc.] was produced using data, in whole or in part, provided by The Trust for Public Land."

This dataset presents the assessment tool used to analyze 20 Data Management Plan (DMP) templates on the Argos platform, along with the pre-print of the manuscript for an article that is about to be published in the Journal Biblios of the University of Pittsburgh. The main objective of this study was to investigate the need to implement a DMP at Universidad Centroamericana José Simeón Cañas (UCA) to improve accessibility, discovery, and reuse of research. Using a qualitative case study methodology, we worked with 10 selected research groups to evaluate and adapt a base model for the DMP. The results indicated a significant improvement in research data management and a positive perception from users regarding the processing and organization of their data. This set includes the DMP format generated for UCA, as well as recommendations for other institutions interested in adopting similar data management practices, contributing to the continued growth of scholarly output and the ethical and..., Method: A qualitative case study methodology was employed, which included participant observation of researchers and administrative staff from various 2024 research groups, along with an analysis of documentation and LibGuides. A benchmarking process was also conducted, comparing 20 PGDI templates to extract the best structure and practices from various research institutions. Content analysis: This method was used to examine a set of 20 PGDI templates from the ARGOS initiative, a platform developed by OpenAIRE and EUDAT for planning and managing research data. A systematic review of the structure and content of each of these templates was conducted, assessing the clarity, consistency, and adequacy of the information presented. Through this content analysis, key elements were identified that needed to be incorporated or improved in the base template provided to UCA research groups. This process allowed us to highlight best practices and identify areas that required additional attention, ..., , # Data from: Data management plan (DMP): Towards more efficient scientific management at the Universidad Centroamericana José Simeón Cañas

https://doi.org/10.5061/dryad.1zcrjdg25

Description of the Data and File Structure

README for the Dataset: Implementation of a Data Management Plan (DMP)

Dataset Description

This dataset includes the evaluation instrument used to analyze 20 Data Management Plan (DMP) templates on the Argos platform. Additionally, the pre-print of the manuscript of the article that is set to be published in the Journal Biblios at the University of Pittsburgh has been attached. Furthermore, the format of the Data Management Plan generated for the Universidad Centroamericana José Simeón Cañas (UCA), developed from this research, is included.

Objective

The primary objective of this study was to investigate the need to implement a Data Management Plan (DMP) to improve the accessibility, discoverability...

This layer is a subset of Global Landcover 1992- 2020 Layer. This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years 1992-2020. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2020Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafterWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.Land Cover ProcessingTo provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015.Source dataThe datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.phpCitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2024 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2024. Key Properties Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryAnalysis: Optimized for analysisClass Definitions: ValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Usage Information and Best PracticesProcessing TemplatesThis layer includes a number of preconfigured processing templates (raster function templates) to provide on-the-fly data rendering and class isolation for visualization and analysis. Each processing template includes labels and descriptions to characterize the intended usage. This may include for visualization, for analysis, or for both visualization and analysis. VisualizationThe default rendering on this layer displays all classes.There are a number of on-the-fly renderings/processing templates designed specifically for data visualization.By default, the most recent year is displayed. To discover and isolate specific years for visualization in Map Viewer, try using the Image Collection Explorer. AnalysisIn order to leverage the optimization for analysis, the capability must be enabled by your ArcGIS organization administrator. More information on enabling this feature can be found in the ‘Regional data hosting’ section of this help doc.Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See this group for a complete list of imagery layers optimized for analysis.Prior to running analysis, users should always provide some form of data selection with either a layer filter (e.g. for a specific date range, cloud cover percent, mission, etc.) or by selecting specific images. To discover and isolate specific images for analysis in Map Viewer, try using the Image Collection Explorer.Zonal Statistics is a common tool used for understanding the composition of a specified area by reporting the total estimates for each of the classes. GeneralIf you are new to Sentinel-2 LULC, the Sentinel-2 Land Cover Explorer provides a good introductory user experience for working with this imagery layer. For more information, see this Quick Start Guide.Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

An excel template with data elements and conventions corresponding to the openLCA unit process data model. Includes LCA Commons data and metadata guidelines and definitions

A group of advisory documents and templates for the creation of PaR submissions to REF using Figshare, using REF criteria specifically for peer review.

Representations of task-relevant object attributes (attentional templates) control the adaptive selectivity of visual processing. Previous studies have demonstrated that templates involved in the guidance of attention during visual search are activated in a preparatory fashion prior to the arrival of visual search displays. The current study investigated whether such proactive mechanisms are also triggered in non-search tasks, where attentional templates do not mediate the guidance of attention towards targets amongst distractors but are still necessary for subsequent target recognition processes. Participants either searched for colour-defined targets among multiple distractors or performed two other non-search tasks where imperative stimuli appeared without competing distractors (a colour-based Go/NoGo task, and a shape discrimination task where target colour was constant and could thus be ignored). Preparatory activation of colour-selective templates was tracked by measuring N2pc components (markers of attention allocation) to task-irrelevant colour singleton probes flashed every 200 ms during the interval between target displays. As expected, N2pcs were triggered by target-coloured probes in the search task, indicating that a corresponding guidance template was triggered proactively. Critically, clear probe N2pcs were also observed in the Go/NoGo task, and even in the shape discrimination task in an attenuated fashion. These findings demonstrate that the preparatory activation of feature-selective attentional task settings is not uniquely associated with the guidance of visual search but is also present in other types of visual selection tasks where guidance is not required.