Sage-Grouse habitat areas divided into proposed management categories within Nevada and California project study boundaries.MANAGEMENT CATEGORY DETERMINATION The process for category determination was directed by the Nevada Sagebrush Ecosystem Technical team. Sage-grouse habitat was determined from a statewide resource selection function model and first categorized into 4 classes: high, moderate, low, and non-habitat. The standard deviations (SD) from a normal distribution of RSF values created from a set of validation points (10% of the entire telemetry dataset) were used to categorize habitat ‘quality’ classes. High quality habitat comprised pixels with RSF values < 0.5 SD, Moderate > 0.5 and < 1.0 SD, Low < 1.0 and > 1.5, Non-Habitat > 1.5 SD. Proposed Habitat Management Categories were then defined and calculated as follows.1) Core habitat: Defined as the intersection between all suitable habitat (high, moderate, and low) and the 85% Space Use Index (SUI). 2) Priority habitat: Defined as all high quality falling outside the 85% SUI and all non-habitat falling within the 85% SUI. 3) General habitat: Defined as moderate and low quality habitat falling outside the 85% SUI. 4) Non habitat. Defined as non-habitat falling outside the 85% SUI. SPACE USE INDEX CALCULATIONLek coordinates and associated trend count data were obtained from the 2013 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/10/2013). We queried the database for leks with a ‘LEKSTATUS’ field classified as ‘Active’ or ‘Pending’. Active leks comprised leks with breeding males observed within the last 5 years. Pending leks comprised leks without consistent breeding activity during the prior 3 – 5 surveys or had not been surveyed during the past 5 years; these leks typically trended towards ‘inactive’. A sage-grouse management area (SGMA) was calculated by buffering Population Management Units developed by NDOW by 10km. This included leks from the Buffalo-Skedaddle PMU that straddles the northeastern California – Nevada border, but excluded leks for the Bi-State Distinct Population Segment. The 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending each lek was calculated. The final dataset comprised 907 leks. Utilization distributions describing the probability of lek occurrence were calculated using fixed kernel density estimators (Silverman 1986) with bandwidths estimated from likelihood based cross-validation (CVh) (Horne and Garton 2006). UDs were weighted by the 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending leks. UDs and bandwidths were calculated using Geospatial Modelling Environment (Beyer 2012) and the ‘ks’ package (Duong 2012) in Program R. Grid cell size was 30m. The resulting raster was clipped by the SGMA polygon, and values were re-scaled between zero and one by dividing by the maximum pixel value.The non-linear effect of distance to lek on the probability of grouse spatial use was estimated using the inverse of the utilization distribution curves described by Coates et al. (2013), where essentially the highest probability of grouse spatial use occurs near leks and then declines precipitously as a non-linear function. Euclidean distance was first calculated in ArcGIS, reclassified into 30-m distance bins (ranging from 0 – 30,000m), and bins reclassified according to the non-linear curve in Coates et al. (2013). The resulting raster was clipped by the SGMA polygon, and re-scaled between zero and one by dividing by the maximum pixel value.A Spatial Use Index (SUI) was calculated taking the average of the lek utilization distribution and non-linear distance to lek rasters in ArcGIS, and re-scaled between zero and 1 by dividing by the maximum pixel value.The volume of the SUI at cumulative 5% increments (isopleths) was extracted in Geospatial Modelling Environment (Beyer 2012) with the command ‘isopleth’. Interior polygons (i.e., donuts’ > 1.2 km2) representing no probability of use within a larger polygon of use were erased from each isopleth. The relationship between percent land area within each isopleth and isopleth volume (VanderWal and Rodgers 2012) indicated statistically concentrated use at the 70% isopleth. The 85% isopleth, which provided greater spatial connectivity and consistency with previously used agency standards (e.g., Doherty et al. 2010), was ultimately recommended by the Sagebrush Ecosystem Technical Team. The 85% SUI isopleth was clipped by the SGMA clipped by the Nevada state boundary, which only included habitat within the state of Nevada.Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online)REFERENCES Beyer HL. 2012. Geospatial Modelling Environment (Version 0.7.2.0). http://www.spatialecology.com/gmeCoates PS, Casazza ML, Blomberg EJ, Gardner SC, Espinosa SP, Yee JL, Wiechman L, Halstead BJ. 2013. “Evaluating greater sage-grouse seasonal space use relative to leks: Implications for surface use designations in sagebrush ecosystems.” The Journal of Wildlife Management 77: 1598-1609.Doherty KE, Tack JD, Evans JS, Naugle DE. 2010. Mapping breeding densities of greater sage-grouse: A tool for range-wide conservation planning. Bureau of Land Management. Report Number: L10PG00911. Accessed at: http://www.conservationgateway.org/ConservationByGeography/NorthAmerica/Pages/sagegrouse.aspx# Duong T. 2012. ks: Kernel smoothing. R package version 1.8.10. http://CRAN.R-project.org/package=ksHorne JS, Garton EO. 2006. “Likelihood cross-validation versus least squares cross-validation for choosing the smoothing parameter in kernel home-range analysis.” Journal of Wildlife Management 70: 641-648.Silverman BW. 1986. Density estimation for statistics and data analysis. Chapman & Hall, London, United Kingdom.Vander Wal E, Rodgers AR. 2012. “An individual-based quantitative approach for delineating core areas of animal space use.” Ecological Modelling 224: 48-53.NOTE: This file does not include habitat areas for the Bi-State management area.

This shapefile represents proposed management categories (Core, Priority, General, and Non-Habitat) derived from the intersection of habitat suitability categories and lek space use. Habitat suitability categories were derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern California formed from the multiplicative product of the spring, summer, and winter HSI surfaces. Summary of steps to create Management Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014) as well as additional telemetry location data from field sites in 2014. The dataset was then split according to calendar date into three seasons. Spring included telemetry locations (n = 14,058) from mid-March to June; summer included locations (n = 11,743) from July to mid-October; winter included locations (n = 4862) from November to March. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion using R software (v 3.13) and season using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. For each season, subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell. The three seasonal HSI rasters were then multiplied to create a composite annual HSI. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. HABITAT CATEGORIZATION: Using the same ecoregion boundaries described above, the habitat classification dataset (an independent data set comprising 10% of the total telemetry location sample) was split into locations falling within respective north and south regions. HSI values from the composite and relativized statewide HSI surface were then extracted to each classification dataset location within the north and south region. The distribution of these values were used to identify class break values corresponding to 0.5 (high), 1.0 (moderate), and 1.5 (low) standard deviations (SD) from the mean HSI. These class breaks were used to classify the HSI surface into four discrete categories of habitat suitability: High, Moderate, Low, and Non-Habitat. In terms of percentiles, High habitat comprised greater than 30.9 % of the HSI values, Moderate comprised 15 – 30.9%, Low comprised 6.7 – 15%, and Non-Habitat comprised less than 6.7%.The classified north and south regions were then clipped by the boundary layer and mosaicked to create a statewide categorical surface for habitat selection. Each habitat suitability category was converted to a vector output where gaps within polygons less than 1.2 million square meters were eliminated, polygons within 500 meters of each other were connected to create corridors and polygons less than 1.2 million square meters in one category were incorporated to the adjacent category. The final step was to mask major roads that were buffered by 50m (Census, 2014), lakes (Peterson, 2008) and urban areas, and place those masked areas into the non-habitat category. The existing urban layer (Census 2010) was not sufficient for our needs because it excluded towns with a population lower than 1,500. Hence, we masked smaller towns (populations of 100 to 1500) and development with Census Block polygons (Census 2015) that had at least 50% urban development within their boundaries when viewed with reference imagery (ArcGIS World Imagery Service Layer). SPACE USE INDEX CALCULATION: Updated lek coordinates and associated trend count data were obtained from the 2015 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/20/2015). Leks count data from the California side of the Buffalo-Skedaddle and Modoc PMU's that contributed to the overall space-use model were obtained from the Western Association of Fish and Wildlife Agencies (WAFWA), and included count data up to 2014. We used NDOW data for border leks (n = 12), and WAFWA data for those fully in California and not consistently surveyed by NDOW. We queried the database for leks with a ‘LEKSTATUS’ field classified as ‘Active’ or ‘Pending’. Active leks comprised leks with breeding males observed within the last 5 years (through the 2014 breeding season). Pending leks comprised leks without consistent breeding activity during the prior 3 - 5 surveys or had not been surveyed during the past 5 years; these leks typically trended towards ‘inactive’, or newly discovered leks with at least 2 males. A sage-grouse management area (SGMA) was calculated by buffering Population Management Units developed by NDOW by 10km. This included leks from the Buffalo-Skedaddle PMU that straddles the northeastern California – Nevada border, but excluded leks for the Bi-State Distinct Population Segment. The 5-year average (2011 - 2015) for the number of male grouse (or NDOW classified 'pseudo-males' if males were not clearly identified but likely) attending each lek was calculated. Compared to the 2014 input lek dataset, 36 leks switched from pending to inactive, and 74 new leks were added for 2015 (which included pending ‘new’ leks with one year of counts. A total of 917 leks were used for space use index calculation in 2015 compared to 878 leks in 2014. Utilization distributions describing the probability of lek occurrence were calculated using fixed kernel density estimators (Silverman 1986) with bandwidths estimated from likelihood based cross-validation (CVh) (Horne and Garton 2006). UDs were weighted by the 5-year average (2011 - 2015) for the number of males grouse (or unknown gender if males were not identified) attending leks. UDs and bandwidths were calculated using Geospatial Modelling Environment (Beyer 2012) and the ‘ks’ package (Duong 2012) in Program R. Grid cell size was 30m. The resulting raster was re-scaled between zero and one by dividing by the maximum pixel value. The non-linear effect of distance to lek on the probability of grouse spatial use was estimated using the inverse of the utilization distribution curves described by Coates et al. (2013), where essentially the highest probability of grouse spatial use occurs near leks and then declines precipitously as a non-linear function. Euclidean distance was first calculated in ArcGIS, reclassified into 30-m distance bins (ranging from 0 - 30,000m), and bins reclassified according to the non-linear curve in Coates et al. (2013). The resulting raster was re-scaled between zero and one by dividing by the maximum cell value. A Spatial Use Index (SUI) was calculated by taking the average of the lek utilization distribution and non-linear distance-to-lek rasters in ArcGIS, and re-scaled between zero and one by dividing by the maximum cell value. The volume of the SUI at cumulative at specific isopleths was extracted in Geospatial Modelling Environment (Beyer 2012) with the command ‘isopleth’. Interior polygons (i.e., donuts’ > 1.2 km2) representing no probability of use within a larger polygon of use were erased from each isopleth. The 85% isopleth, which provided greater spatial connectivity and consistency with previously used agency standards (e.g., Doherty et al. 2010), was ultimately recommended by the Sagebrush Ecosystem Technical Team. The 85% SUI isopleth was clipped by the Nevada state boundary. MANAGEMENT CATEGORIES: The process for category determination was directed by the Nevada Sagebrush Ecosystem Technical team. Sage-grouse habitat was categorized into 4 classes: High, Moderate, Low, and Non-Habitat as described above, and intersected with the space use index to form the following management categories . 1) Core habitat: Defined as the intersection between all suitable habitat (High, Moderate, and Low) and the 85% Space Use Index (SUI). 2) Priority habitat: Defined as all high quality habitat

Daniel Sage is an author. They were born in 1980. They have 2 books in our database.

THIS RESOURCE IS NO LONGER IN SERVICE, documented September 6, 2016. The Unified Library Database, or UniLib, takes a library-level view of the EST and SAGE libraries present in NCBI's dbEST, UniGene and SAGEmap resources. This database was initially developed by NCBI in order to track and annotate libraries being generated by NCI's CGAP project. The query bar of the UniLib Library browser provides the most friendly way to navigate through these libraries. When matches to the Library browser query are returned as summaries, full library records can be retrieved through the linked Record retriever.

SPACE USE INDEX CALCULATIONLek coordinates and associated trend count data were obtained from the 2013 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/10/2013). We queried the database for leks with a ‘LEKSTATUS’ field classified as ‘Active’ or ‘Pending’. Active leks comprised leks with breeding males observed within the last 5 years. Pending leks comprised leks without consistent breeding activity during the prior 3 – 5 surveys or had not been surveyed during the past 5 years; these leks typically trended towards ‘inactive’. A sage-grouse management area (SGMA) was calculated by buffering Population Management Units developed by NDOW by 10km. This included leks from the Buffalo-Skedaddle PMU that straddles the northeastern California – Nevada border, but excluded leks for the Bi-State Distinct Population Segment. The 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending each lek was calculated. The final dataset comprised 907 leks. Utilization distributions describing the probability of lek occurrence were calculated using fixed kernel density estimators (Silverman 1986) with bandwidths estimated from likelihood based cross-validation (CVh) (Horne and Garton 2006). UDs were weighted by the 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending leks. UDs and bandwidths were calculated using Geospatial Modelling Environment (Beyer 2012) and the ‘ks’ package (Duong 2012) in Program R. Grid cell size was 30m. The resulting raster was clipped by the SGMA polygon, and values were re-scaled between zero and one by dividing by the maximum pixel value.The non-linear effect of distance to lek on the probability of grouse spatial use was estimated using the inverse of the utilization distribution curves described by Coates et al. (2013), where essentially the highest probability of grouse spatial use occurs near leks and then declines precipitously as a non-linear function. Euclidean distance was first calculated in ArcGIS, reclassified into 30-m distance bins (ranging from 0 – 30,000m), and bins reclassified according to the non-linear curve in Coates et al. (2013). The resulting raster was clipped by the SGMA polygon, and re-scaled between zero and one by dividing by the maximum pixel value.A Spatial Use Index (SUI) was calculated taking the average of the lek utilization distribution and non-linear distance to lek rasters in ArcGIS, and re-scaled between zero and 1 by dividing by the maximum pixel value.The volume of the SUI at cumulative 5% increments (isopleths) was extracted in Geospatial Modelling Environment (Beyer 2012) with the command ‘isopleth’. Interior polygons (i.e., donuts’ > 1.2 km2) representing no probability of use within a larger polygon of use were erased from each isopleth. The relationship between percent land area within each isopleth and isopleth volume (VanderWal and Rodgers 2012) indicated statistically concentrated use at the 70% isopleth. The 85% isopleth, which provided greater spatial connectivity and consistency with previously used agency standards (e.g., Doherty et al. 2010), was ultimately recommended by the Sagebrush Ecosystem Technical Team. The 85% SUI isopleth was clipped by the SGMA clipped by the Nevada state boundary, which only included habitat within the state of Nevada.Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online)REFERENCES Beyer HL. 2012. Geospatial Modelling Environment (Version 0.7.2.0). http://www.spatialecology.com/gmeCoates PS, Casazza ML, Blomberg EJ, Gardner SC, Espinosa SP, Yee JL, Wiechman L, Halstead BJ. 2013. “Evaluating greater sage-grouse seasonal space use relative to leks: Implications for surface use designations in sagebrush ecosystems.” The Journal of Wildlife Management 77: 1598-1609.Doherty KE, Tack JD, Evans JS, Naugle DE. 2010. Mapping breeding densities of greater sage-grouse: A tool for range-wide conservation planning. Bureau of Land Management. Report Number: L10PG00911. Accessed at: http://www.conservationgateway.org/ConservationByGeography/NorthAmerica/Pages/sagegrouse.aspx# Duong T. 2012. ks: Kernel smoothing. R package version 1.8.10. http://CRAN.R-project.org/package=ksHorne JS, Garton EO. 2006. “Likelihood cross-validation versus least squares cross-validation for choosing the smoothing parameter in kernel home-range analysis.” Journal of Wildlife Management 70: 641-648.Silverman BW. 1986. Density estimation for statistics and data analysis. Chapman & Hall, London, United Kingdom.Vander Wal E, Rodgers AR. 2012. “An individual-based quantitative approach for delineating core areas of animal space use.” Ecological Modelling 224: 48-53.NOTE: This file does not include habitat areas for the Bi-State management area.

This statistic presents the result of a keyword search on five electronic databases to identify the amount of scientific research into the implementation of mHealth apps for dementia healthcare in 2018. In total, 6,195 research articles were found using select search terms in the following databases: MEDLINE, PubMed, SAGE, IEEE Xplore, and Science Direct.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 9, 2022. The SAGE Genie website provides highly intuitive, visual displays of human and mouse gene expression, based on a unique analytical process (Boon et al.) that reliably matches SAGE tags, 10 or 17 nucleotides in length, to known genes. Recently, with the construction and incorporation of a reference database of SNP-associated alternative tags into SAGE Genie (Silva et al.), the interpretation of tag to human gene mapping has been enhanced. Human SAGE Genie Tools The SAGE Anatomic Viewer visually displays the relative expression of a given gene in normal and malignant tissues of the human body. In addition, there is access to: o The Digital Northern, which shows the relative expression of the gene in each library. o The Ludwig Transcript (LT) Viewer, which visually represents a particular transcript with up to four possible virtual SAGE tag locations (starting from the 3' end) and locations of internally primed or alternatively polyadenylated transcripts. o Expanded access to brain, hES cells, and breast cell subtypes. * The SAGE Digital Gene Expression Displayer distinguishes significant differences in gene expression profiles between two pools of SAGE libraries. * The SAGE Experimental Viewer provides DGED results for pre-set pairs of libraries, one under control and the other under experimental conditions. * The SAGE Absolute Level Lister lists all SAGE libraries and links to the distribution of transcript expression levels in any given library. * The SAGE Library Finder tool searches for one or more tissue-specific libraries from the SAGE collection. * SAGE Genie Downloads provide files of genes, tags, datasets, mappings, and more. * SAGE Genomics Finder tool searches for genomics info for tags. * DKView tool for viewing Digital Karyotyping data. * DK Microbe tool searches for exact matches of 17 base-pair digital karyotyping tags in bacterial and viral genomes. * SAGE Tag Extraction tool to extract tags from sequence files. Mouse SAGE Genie Tools * The mSAGE Expression Matrix visually displays the relative expression of a given gene through stages of mouse development. In addition, there is access to: o The Digital Northern, which shows the relative expression of the gene in each library. o The Ludwig Transcript (LT) Viewer, which visually represents a particular transcript with up to four possible virtual SAGE tag locations (starting from the 3' end) and locations of internally primed or alternatively polyadenylated transcripts. * The mSAGE Absolute Level Lister lists all mouse SAGE libraries, organized by either tissue (normal or malignant) or developmental stage, and links to the distribution of transcript expression levels in any given library. * The mSAGE Digital Gene Expression Displayer distinguishes significant differences in gene expression profiles between two pools of mouse SAGE libraries. * The mSAGE Experimental Viewer automatically sets up the DGED with mouse libraries, prepared as stand alone experiments, for gene expression comparison. * The mSAGE Library Finder tool searches for one or more tissue-specific mouse libraries from the SAGE collection. * mSAGE Genie Downloads provide files of mouse genes, tags, datasets, mappings, and more. * SAGE Genomics Finder tool searches for genomics info for tags. * SAGE Tag Extraction tool to extract tags from sequence files. Notes: The majority of human libraries are short whereas the majority of mouse libraries are long. Several mouse developmental libraries are prepared by the SAGELite method. SAGELite is an extension of the long Sage protocol that includes a PCR-based amplification stage to allow the use of 10ng to 100ng of total RNA to produce a Sage library. However, it has been observed that a significant bias incurred by this amplification process. SAGE Genie was produced as part of the NCI CGAP SAGE project with collaborators from Johns Hopkins University, the Ludwig Institute for Cancer Research, Sao Paulo Branch, the University of Texas MD Anderson Cancer Center, and the Genome Sciences Center of the BCCRC.

447 Global import shipment records of Sage Oil with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

THIS RESOURCE IS NO LONGER IN SERVICE, Documented on March 24, 2014. A resource for gene expression studies, storing highly curated MIAME-compliant studies (i.e. experiments) employing a variety of technologies such as filter arrays, 2-channel microarrays, Affymetrix chips, SAGE, MPSS and RT-PCR. Data were available for querying and downloading based on the MGED ontology, publications or genes. Both public and private studies (the latter viewable only by users having appropriate logins and permissions) were available from this website. Specific details on protocols, biomaterials, study designs, etc., are collected through a user-friendly suite of web annotation forms. Software has been developed to generate MAGE-ML documents to enable easy export of studies stored in RAD to any other database accepting data in this format. RAD is part of a more general Genomics Unified Schema (http://gusdb.org), which includes a richly annotated gene index (http://allgenes.org), thus providing a platform that integrates genomic and transcriptomic data from multiple organisms. NOTE: Due to changes in technology and funding, the RAD website is no longer available. RAD as a schema is still very much active and incorporated in the GUS (Genomics Unified Schema) database system used by CBIL (EuPathDB, Beta Cell Genomics) and others. The schema for RAD can be viewed along with the other GUS namespaces through our Schema Browser.

Emily Sage is an author. They have 2 books in our database.

This feature class includes monitoring data collected nationally to understand the status, condition, and trend of resources on BLM lands. Data are collected in accordance with the BLM Assessment, Inventory, and Monitoring (AIM) Strategy. The AIM Strategy specifies a probabilistic sampling design, standard core indicators and methods, electronic data capture and management, and integration with remote sensing. Attributes include the BLM terrestrial core indicators: bare ground, vegetation composition, plant species of management concern, non-native invasive species, and percent canopy gaps (see Entity/Attribute Section for exact details on attributes). Data were collected and managed by BLM Field Offices, BLM Districts, and/or affiliated field crews with support from the BLM National Operations Center. Data are stored in a centralized database (TerrADat) at the BLM National Operations Center. The Terrestrial AIM data (TerrADat) dataset was collected by the BLM using the Monitoring Manual for Grassland, Shrubland, and Savannah Ecosystems (2nd edition; https://www.landscapetoolbox.org/manuals/monitoring-manual/). Also see Interpreting Indicators of Rangeland Health (version 4; https://www.landscapetoolbox.org/wp-content/uploads/2015/01/IIRHv4.pdf). The vast majority of monitoring locations were selected using spatially balanced, random sampling approaches and thus provide an unbiased representation of land conditions. However, these data should not be used for statistical or spatial inferences without knowledge of how the sample design was drawn or without calculating spatial weights for the points based on the sample design. General Definitions Noxious: Noxious status and growth form (forb, shrub, etc.) are designated for each BLM Administrative State using the state noxious list and local botany expertise often after consulting the USDA plants database. Non-Noxious: Non-Noxious status and growth form (forb, shrub, etc.) are designated for each BLM Administrative State using the state noxious list and local botany expertise often after consulting the USDA plants database. Sagebrush: Sagebrush species are designated for each BLM Administrative State using local botany expertise. Non-Sagebrush Shrub: Non Sagebrush Shrub species are designated for each BLM Administrative State as the plants determined to be shrubs that are not also Sagebrush. Tall Stature Perennial Grass: Tall Stature Perennial Grasses status was determined by Sage Grouse biologist and modified slightly in each state. Short Stature Perennial Grass: Short Stature Perennial Grasses status was determined by Sage Grouse biologist and modified slightly in each state. Preferred Forb: Preferred forb for Sage Grouse status was determined for each state by Sage Grouse biologist and other local experts. Live: The Core Methods measure Live vs Standing Dead plant cover – i.e. if a pin drop hits a plant part and that plant part is dead (even if it’s on a living plant) that hit is considered a dead hit. Any occurrence of Live Sagebrush calculations indicates that the measurement is only hits that were live plant parts. If a pin hits both a live and a standing dead plant part in the same pin drop – that hit is considered live.

To accelerate the process of tumor antigen discovery, we generated a publicly available Human Potential Tumor Associated Antigen database (HPtaa) with pTAAs identified by insilico computing. 3518 potential targets have been included in the database, which is freely available to academic users. It successfully screened out 41 of 82 known Cancer-Testis antigens, 6 of 18 differentiation antigen, 2 of 2 oncofetal antigen, and 7 of 12 FDA approved cancer markers that have Gene ID, therefore will provide a good platform for identification of cancer target genes. This database utilizes expression data from various expression platforms, including carefully chosen publicly available microarray expression data, GEO SAGE data, Unigene expression data. In addition, other relevant databases required for TAA discovery such as CGAP, CCDS, gene ontology database etc, were also incorporated. In order to integrate different expression platforms together, various strategies and algorithms have been developed. Known tumor antigens are gathered from literature and serve as training sets. A total tumor specificity penalty was computed from positive clue penalty for differential expression in human cancers, the corresponding differential ratio, and normal tissue restriction penalty for each gene. We hope this database will help with the process of cancer immunome identification, thus help with improving the diagnosis and treatment of human carcinomas.

Customer database including contact details of general public requesting fuel consumption booklets etc.

This dataset consists of point intercept data, sampled with a point frame, from three 1 ha sites along an elevation and precipitation gradient within Reynolds Creek Experimental Watershed collected between late May and mid July, 2019. The lowest elevation site ('wbs1', 1,425 m) was vegetated by shrub steppe dominated Wyoming big sage (Artemisia tridentata ssp. wyomingensis). Vegetation at the middle elevation site ('los1', 1,680 m) was shrub steppe dominated by low sage (Artemisia arbuscula). Shrub steppe at the highest elevation site ('mbs1', 2,110 m) was dominated by mountain big sage (Artemisia tridentata ssp. vaseyana) and Utah snowberry (Symphoricarpos oreophilus utahensis). At each site 30 randomly located square 1 m^2 plots were sampled. The plots were oriented with one axis randomly chosen from 45, 90, 135, 180, 225, 270, 315 and 360 degrees north azimuth. A point frame of 20 pins was orientated perpendicular to the azimuth and each pin was lowered through the canopy and each contact was recorded to species or other plant material category. Whether the contacted material was photosynthetic (coded as a '+') or non-photosynthetic (coded as '-') was also recorded. Last seasons senesced plant material that is alive but not photosynthetic is coded as '.'. There may be 0, 1, 2 or more canopy hits for each pin (numbered 1 through n with 1 being the top-most canopy hit). A final basal hit is recorded for each pin and coded as hit 0. The point frame was moved so that a total of 5 rows were recorded for a total of 100 pins for each plot. The plant species codes used follow the USDA Plants Database. Resources in this dataset:Resource Title: Data from: UAS imagery protocols to map vegetation are transferable between dryland sites across an elevational gradient . File Name: point_frame_2019_reynoldscreek.xlsxResource Description: This dataset consists of point frame data from three 1 ha sites along an elevation and precipitation gradient within Reynolds Creek Experimental Watershed collected between late May and mid July, 2019. The lowest site's ('wbs1', 1,425 m) characteristic dominant shrub is Wyoming big sage (Artemisia tridentata ssp. wyomingensis). The middle elevation site's ('los1', 1,680 m) dominant shrub is low sage (Artemisia arbuscula). The highest elevation site's ('mbs1', 2,110 m) dominant shrubs are mountain big sage (Artemisia tridentata ssp. vaseyana) and Utah snowberry (Symphoricarpos oreophilus utahensis). At each site 30 randomly located square 1 m^2 plots were sampled. The plots were oriented with one axis randomly chosen from 45, 90, 135, 180, 225, 270, 315 and 360 degrees north azimuth. A point frame of 20 pins was orientated perpendicular to the azimuth and each pin was lowered through the canopy and each contact was recorded to species or other plant material category. Whether the contacted material was photosynthetic (coded as a '+') or non-photosynthetic (coded as '-') was also recorded. Last seasons senesced plant material that is alive but not photosynthetic is coded as '.'. There may be 0, 1, 2 or more canopy hits for each pin (numbered 1 through n with 1 being the top-most canopy hit). A final basal hit is recorded for each pin and coded as hit 0. The point frame was moved so that a total of rows rows were recorded for a total of 100 pins for each plot. The plant species codes used follow the USDA Plants Database.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: GeoJSON. File Name: ReynoldsCrkExpWtrshdGeoJSON.json

This dataset contains two predictive lek (breeding site) persistence raster layers covering the U.S. greater sage-grouse distribution. In the United States, locations where males display and breed with females (i.e., leks) are often monitored annually by state wildlife agencies, providing valuable information on the persistence of birds in the surrounding areas. A U.S. range-wide lek database was recently compiled for greater sage-Grouse (O’Donnell et al. 2021), providing a standardized source of information to build statistical models to evaluate environmental characteristics associated with lek persistence. The compiled lek database classified a subset of leks as being either active (leks currently used for breeding activities) or inactive (leks no longer used for breeding activities) based on count data collected over a 20-year monitoring period. We fit the outcome of a lek being active or inactive as a function of environmental predictors characterizing surrounding conditions in a logistic regression model. Covariates included sagebrush cover, pinyon-juniper cover, topography, precipitation, point and line disturbance densities, and landscape configuration metrics. We included the Bureau of Land Management habitat assessment areas (termed mid-scales) as regional random effects in the form of random intercepts and random slopes (for a subset of covariates). The final model included 13 covariates. We predicted conditional probabilities of lek persistence across the U.S. occupied range using the covariate layers and regional mid-scales, which we make available here as a 30-meter resolution continuous raster dataset. The predictions were conditional because they were specific to each mid-scale factor level (i.e., pixel predictions were influenced by the regional mid-scale polygon they fell within via the associated mid-scale intercept and random slope deviations). We applied sensitivity thresholds (capturing percentage of leks correctly classified as active) to the continuous probability layer to bin persistence probabilities into high, medium, low, and marginal areas of persistence, which we make available here as a 30-m categorical raster dataset.

The following files are designed to be run using the Path Landscape Model software, version 3.0.4. Later versions of the software cannot run these files. To get a copy of this software, please contact Apex RMS at path@apexrms.com. 1) "Path Landscape Mode" folder contains files to be run in the PLM softwarel, version 3.0.4 or later. Path models MUST be run with the provided MCM mulitplier files to apply the required transition probability adjustments for procesess such as insect outbreaks, wildfire, and climate change trends. Each Path database is set up with three folders: - The 'Common' folder contains a single Path scenario (also named 'Common'). The Transitions tab within the Common scenario contains the climate-smart STM. - The 'Multipliers' folder contains multipliers specific to each ownership-allocation to activate or deactivate transitions (both climate change and management). Actual treatments are input in the Treatments tab for each stratum in the 'Runs' folder. - The 'Runs' folder contains one Path scenario per modeling stratum, with initial conditions specific to each stratum (combination of watershed and ownership-allocation). The models are stored as a dependency from the 'Common' folder and the multipliers as a dependency from the 'Multipliers' scenario. For the scenarios that have management activities (current and restoration management), a specified number of acres for each treatment is shown in the Treatments tab for treated stata. There are 12 databases, one for each combintation of management and climate scenario run in southeast Oregon. Climate scenarios include: - No climate change - continuing current climate (NoCC) - HadGEM global circulation model, representative concentration pathway 8.5 (HadGEM) - NorESM global circulation model, representative concentration pathway 8.5 (NorESM) - MRI global circulation model, representative concentration pathway 8.5 (MRI) Management scenarios include: - No management - no restoration treatments (NoMgt) - Current management - current treatment rates compiled from managers in the region (CurMgt) - Restoration management - a restoration scenario to restore sage-grouse habitat (RestMgt) 2) The "lookupTables" folder contains files necessary for providing definitions and context for the information located in other folders. 3) The 'Spatial' folder contains the SEO region stratum map, called SEO_Modeling_Strata.tif. This map can be joined to output from the climate-informed state-and-transition models to map projected future condition or sage-grouse habitat on the "Strata" field. Modeling strata consist of the intersection of watershed (Hydrologic unit code [HUC]) and ownership-allocation map. Watersheds are three digit codes starting at 101. Ownership-allocation categories are a two-character label, and are described in the attribute table through the fields "Ownership", and "Allocation". The field ScenarioID indicates the internal ID number used by the Path software to link results for modeling strata to its Scenario names. 4) This folder contains summarized modeling results that can be viewed as nonspatial trends across the whole landscape or displayed across mapped modeling strata. The .tif file in the 3Spatial folder shows the spatial distribution of modeling strata. There are 3 subfolders: ClassesSummary, TransitionSummary, and HabitatSummary. The csv files within ClassesSummary contain summaries of state class area (in Acres) for each timestep, over monte carlo repetitions, within each modeling stratum. Those within TransitionSummary are summarized in the same manner, but contain summaries of area affected by each Transition Type. The csv files within HabitatSummary are designed to be joined ot the grid mentioned above, to allow for a spatial depiction of habitat projections. There is one column per modeled year, containing a summary that indicates the proportion of each modeling stratum that is comprised of potential greater sage grouse habitat (the general summary, not the high-quality summary, averaged over monte carlo repetitions). Sample queries outlining how to build new summaries of output data for mapping from the ClassesSummary files and lookup tables are included in the database: SEO_Summaries.accdb. Due to a minor error in starting conditions, summaries of sage-grouse habitat may be underestimated in the early years, especially within the northwestern quadrant of the map. Because of this, we have removed the summaries of the first four years within the Habitat summary files, and constrain the sample queries to only show projections for after 2011. If the early years of this summary data are needed for viewing other parts of the map, they can be constructed by removing a constraint within query1 of the sample queries shown above.

The data include used and random available steps at 11-hour resolution generated for 26 female greater sage-grouse in the 60 days post-translocation to North Dakota, with associated environmental predictors and individual information. The code fits individual habitat selection models in an Integrated Step Selection Analysis framework.

Data used to fit the models described in:

Picardi, S., Ranc, N., Smith, B.J., Coates, P.S., Mathews, S.R., Dahlgren, D.K. Individual variation in temporal dynamics of post-release habitat selection. Frontiers in Conservation Science (in review)

Code used to implement the analysis is available on GitHub: https://github.com/picardis/picardi-et-al_2021_sage-grouse_frontiers-in-conservation

Rat Strain

This dataset was created to monitor the status, condition and trend of national BLM resources in accordance with BLM policies. It focuses on the BLM terrestrial core indicators, which include measures of vegetation and soil condition such as plant species cover and composition, plant height, and soil stability. The BLM terrestrial core indicators and methods were identified through a multi-disciplinary process and are described in BLM Technical Note 440 (https://ia800701.us.archive.org/6/items/blmcoreterrestri00mack/BlmCoreTerrestrialIndicatorsAndMethods_88072539.pdf). The Landscape Monitoring Framework (LMF) dataset was collect using the Natural Resource Conservation Services (NRCS) National Resource Inventory (NRI) methodology which mirrors the data collected by the BLM using the Monitoring Manual for Grassland, Shrubland, and Savannah Ecosystems (2nd edition; https://www.landscapetoolbox.org/methods-manuals/monitoring-manual-2nd-edition/). Specific instructions for data collectors each year the data were collected can be found at https://grazingland.cssm.iastate.edu/. Also see Interpreting Indicators of Rangeland Health (version 5; https://www.landscapetoolbox.org/manuals/iirhv5/).The monitoring locations were selected using spatially balanced, random sampling approaches and thus provide an unbiased representation of land conditions. However, these data should not be used for statistical or spatial inferences without knowledge of how the sample design was drawn or without calculating spatial weights for the points based on the sample design.General DefinitionsNoxious: Noxious status and growth form (forb, shrub, etc.) are designated for each BLM Administrative State using the state noxious list and local botany expertise often after consulting the USDA plants database. Each state’s noxious list can be found in tblStateSpecies Table, where the Noxious field is ‘YES’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).Non-Noxious: Non-Noxious status and growth form (forb, shrub, etc.) are designated for each BLM Administrative State using the state noxious list and local botany expertise often after consulting the USDA plants database. Non-Noxious status can be found in tblStateSpecies Table, where the Noxious field is ‘NO’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).Sagebrush: Sagebrush species are designated for each BLM Administrative State using local botany expertise. This list can be found for each state in in the tblStateSpecies Table, where SG_Group field is ‘Sagebrush’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).Non-Sagebrush Shrub: Non Sagebrush Shrub species are designated for each BLM Administrative State as the plants determined to be shrubs that are not also Sagebrush. This list can be found for each state in in the tblStateSpecies Table, where SG_Group field is ‘NonSagebrushShrub’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).Tall Stature Perennial Grass: Tall Stature Perennial Grasses status was determined by Sage Grouse biologist and modified slightly in each state and this list can be found in tblStateSpecies in the SG_Group field where SG_Group field is ‘TallStaturePerennialGrass’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).Short Stature Perennial Grass: Short Stature Perennial Grasses status was determined by Sage Grouse biologist and modified slightly in each state and this list can be found in tblStateSpecies in the SG_Group field where SG_Group field is ‘ShortStaturePerennialGrass’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).Preferred Forb: Preferred forb for Sage Grouse status was determined for each state by Sage Grouse biologist and other local experts and this list can be found in tblStateSpecies in the SG_Group field where SG_Group field is ‘PreferredForb’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).Live: The NRI Methods measure Live vs Dead plant cover – i.e. if a pin drop hits a plant part and that plant part is dead (even if it’s on a living plant) that hit is considered a dead hit. Any occurrence of Live Sagebrush calculations indicates that the measurement is only hits that were live plant parts. If a pin hits both a live and a dead plant part in the same pin drop – that hit is considered live.Growth Habit: The form of a plant, in this dataset the options are Forb, Graminoid, Sedge, Succulent, Shrub, SubShrub, Tree, NonVascular. The most common growth habit for each state was determined by local botany expertise often after consulting the USDA plants database. The growth habit for each species is a state can be found in tblStateSpecies in the GrowthHabitSub field. The values are used to place each plant in a Growth Habit/Duration bin such as Perennial Grass, or Annual Forb, etc.Duration: The life length of a plant. In this dataset we consider plants to be either Perennial or Annual – Biennial plants are classified as Annuals. The most common duration for each state was determined by local botany expertise often after consulting the USDA plants database. The duration for each species is a state can be found in tblStateSpecies in the Duration field. The values are used to place each plant in a Growth Habit/Duration bin such as Perennial Grass, or Annual Forb, etc.tblStateSpecies: This table in the database contains the Species Lists for each state. In the instance where a species code does not have a Growth Habit, Growth Habit Sub, or Duration – any occurrence of that code will not be included in calculations that require that information – for example a code that has NonWoody Forb but no information about annual or perennial will not be included in any of the calculations that are perennial or annual forb calculations. Most codes with no information will have the following in the notes – indicating that the only calculation it will be included in is Total Foliar which doesn’t require any growth habit and duration information – “Not used for calculations except Total Foliar.”

% mislocalization refers to animals with mislocalization out of total animals observed. (n) = the number of animals examined. 0 = 0% mislocalization, + = 1–25% mislocalization, ++ = 26–50% mislocalization, +++ = 51–75% mislocalization.*Name of the gene was queried individually against the SAGE database.