Data Description. 3.5.1.1 Task / group responsible for generating data
Data Description. The following CMS data file(s) is/are covered under this Agreement.
Data Description. The following CMS data file(s) is/are covered under this Agreement. (note for form creator - change the header column for System of Record to “Charge Per Year*” and add a “Total” on bottom line) (the file will be prefilled in with “MEDPAR File Extract – Provider # ”) “Medicare Provider Analysis and Review (MEDPAR), HHS/CMS/OIS, 09-70-0514” Privacy Act System of Records, published at 71 Fed. Reg. 17470 (April 06, 2006) *For pre-FFY 1996, the charge is $1,200 per year (FFY or provider cost year), per provider; For FFY 1996 thru December 7, 2004, the charge is $900 per year (FFY or provider cost year), per provider; Where the cost year includes part of both FFY 1995 and FFY 1996, the charge is $900.
Data Description. 3.1.1.1 Task / group responsible for generating data T1.1.2, Xxxxx XXXXXXX, UOXF
Data Description. 3.9.1.1 Task(s)/group(s) responsible for generating data T1.2.6 (UPM)
Data Description. Analyses involved two samples (A498, a tumor line, and hREF, a pool of healthy tissues), each with three technical replicates for all platforms considered (Affymetrix GeneChip⃝c xxXXX Array, Agilent Human xxXXX Microarray (V1) and Illumina humanMI_V2). XxXXX selection described in the Experimental Section, resulted in a total of 813 human miRNAs considered for analysis, which account for 95.99% of human miRNAs on Affymetrix platforms, 95.53% on Agilent and 94.76% on Illumina (see Figure 1). Pairwise intersections of human xxXXX lists revealed that the larger overlap occurred when Affymetrix and Agilent were considered (830 miRNAs, 97.99% of Affymetrix hsaand 97.53% of Agilent hsa), whereas Illumina showed a slightly poorer degree of overlap with both Affymetrix (817 miRNAs, 96.46% of Affymetrix and 95.22% of Illumina) and Agilent (815 miRNAs, 95.77% of Agilent and 94.99% of Illumina).
Data Description cplan database (matrix) and program • entire DMR and BRS minerals directory, containing many grids, shapefiles and databases. Not sorted, simply presented 'as is'. • Aeromagnetics • Centres of endemism - fauna • Centres of endemism - flora • Centres of endemism - invertebrates • Fauna assemblage models • Flora point localities with buffers • Forest ecosystems • High Quality Habitat Old Growth • Old growth forest by forest ecosystem • Old growth forest and other successional stages • Site productivity • UNE NSW Forest Structure at 1:25,000 • Fauna models - arboreal mammals • Fauna models - amphibia • Fauna models - bats (flying foxes) • Fauna models - diurnal birds • Fauna models - ground mammals • Fauna models - reptiles • Fauna models - nocturnal birds • Fauna models - turtles • Fauna shape files - arboreal mammals • Fauna shape files - aphibia • Fauna shape files - bats (flying foxes) • Fauna shape files - diurnal birds • Fauna shape files - ground mammals • Fauna shape files - reptiles • Fauna shape files - nocturnal birds • Fauna shape files - turtles • Flora models - various species • Disjunct fauna populations • Disjunct flora populations • Important habitat • Limits of range - fauna • Limits of range - flora • Migratory species • Natural landscapes • Primitive and Relictual Sp. • Rare fauna • Rare flora • Rare vegetation communities • Refugia • Remnant vegetation • Fauna richness • Flora richness • Habitat richness • Undisturbed catchments • Vegetation comm. richness • Wilderness capability • Identified Wilderness Composite • NWI Wilderness Areas • NWI database • Declared Wilderness composite • Frames program and data • Cultural Values - general contextual layer • Cultural Heritage - high conservation value layer • Mapping of forest fire history • Attribute data linked to planning units to feed directly into C-Plan:- year of logging event, number of events, total volume removed, frequently disturbed areas • Roads, rail, pipelines, power lines, etc • Grazing • Grazing potential • Mapping of Forest Logging History, ( Events not mapped at subcompartment level) • Mapping of Forest Logging History ( Events mapped at sub-compartment level ) • Roads - within SFNSW • Mapping of Forest silviculture history • Drainage lines • Apiary sites • CRA boundaries • Areas acquired but not gazetted as National Park • SFNSW easements • Planning units (incorporating tenure information) • Planning units (summarised with fewer items) • Forest Types • Gravel pits • SFNSW le...
Data Description. (Xxxxxxx XXXX) After understanding the data properly, next we used Kolmogorov-Smirnov test to determine the distribution of the variables. From the 1-KS test it showed that our variables are normally distributed or Test distribution is normal. Also, our variables are interval scaled. After determining the distribution of the dependent and independent variables we selected Xxxxxxx’x correlation to test our hypotheses or the relation between the independent and dependent variables. Understandability Based on the calculation, the Xxxxxxx’x correlation value showed that there’s high significance and correlation between user TimeSpent and PreferredUserCognizance, r = 0.94, p = 0.00. There was also high significance and correlation between OverallUserUnderstanding and PreferredUserCognizance, r = 0.93, p = 0.00. From the results, we could verify our hypothesis that the more time a user spends on reading the XXXX the more she prefers to know the Terms and Conditions of the software. We could also verify that the more the user is interested in the overall understanding of the User Agreement the more she prefers to know the Terms and Conditions of the software. The result of the calculation validated our third hypothesis. Digest From the results, we saw that there was good significance and correlation between SummarizingOverallEULA and ShorterEULA, r = 0.40, p = 0.03. Based on the result we could verify our hypothesis that a shorter length XXXX is perceived as a summarized XXXX. This means that the best way to represent a xxxxxxx XXXX is to summarize the contents of the XXXX instead of showing the user a huge textual representation of the XXXX. There was less significance and correlation between SummarizingOverallEULA and PreferredUserCognizance, r = 0.22, p = 0.25, which was against our hypothesis expectation that if the Terms and Conditions is summarized then we expect to have more user preferring to read the Terms and Conditions of the software. We suspected that we got poor correlation and significance value due to low data points. We would have required more data to strongly validate our hypothesis. We got poor significance and correlation between InfographicEULA and PreferredUserCognizance, r = -0.09, p = 0.64, which was also against our hypothesis. We expected good correlation and significance between these since if the Terms and Conditions page is in Infographic form then it should have more user preferring to read the Terms and Conditions. This in turn wou...
Data Description. 3.10.1.1 Task(s)/group(s) responsible for generating data T1.2.1, UZH
Data Description. 3.12.1.1 Task / group responsible for generating data T1.1.6/SNS, EBRI, CNR
