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License information was derived automatically
The objective of this study is to evaluate users’ perceptions and preferences on the design features of the COVID-19 prevention promotion icon from the perspective of users’ aesthetic and perceptual needs. In this study, 120 officially published icons from 24 countries and regions were collected from online platforms for ranking tests, and then the top-ranked icons were subjectively rated by the semantic differential method. By evaluating the quality of users’ perceptions of multiple semantic dimensions of icons, we extracted the perceptual semantic words that users valued as the main icon design features. Spearmen correlations were applied to derive possible correlations between user rankings and semantic scales, and a Friedman test was also conducted to determine the true differences in user perceptions and preferences for different styles of icons. Factor analysis was conducted to extract six perceptual words that influence the design features of the COVID-19 prevention promotion icon. The methodology adopted in this study facilitated the screening of design features related to icon effectiveness, and the findings show that “Interesting,” “Simple,” “Familiar, “Recognizable,” “Concrete,” and “Close(semantic distance)” are the key features that influence users’ perception and preference of COVID-19 icon design. The results of this study can be used as the basis for designing and improving publicity icons for preventive measures in COVID-19, and the methods adopted in this study can be applied to evaluate other types of icon design.
description: A Visitor Study and Report on the Connections between People, Place and Bison Conservation at the Rocky Mountain Arsenal National Wildlife Refuge. This study makes an important contribution to visitor management and human dimensions of wildlife research at the Rocky Mountain Arsenal National Wildlife Refuge (RMA or Refuge) in metropolitan Denver. Denver Zoo researchers conducted visitor-intercept interviews to collect data about how the Refuge s conservation bison herd, reintroduced in 2009, is shaping the visitor experience. This report summarizes the 2015 findings of this work including the socio-demographic characteristics of Refuge visitors, their self-reported site use patterns and experiences, and their sense of connection to this grassland protected area and its conservation bison herd. Research findings illuminate greater opportunities for RMA, as an urban refuge, to attract and engage Denver publics and other visitors through its bison conservation herd, cementing the value of RMA herd for social and ecological benefit. Denver Zoo s conservation social science team conducted 100 visitor-intercept interviews with Refuge visitors from early July to late August 2015. These interviews followed the pilot testing of a structured interview guide in mid-June 2015. A multi-stage random sampling design for the visitor intercepts ensured a highly representative sample. Interviews were conducted across a range of weeks, days (e.g. weekdays and weekends), and times (morning and afternoon) to capture a variety of visitors. In conclusion, the Refuge s bison herd motivates visitation for almost 20% of Refuge visitors. The herd is seen by visitors as an asset and natural amenity that adds value to their experience and sense of connection to the Refuge. Looking forward, the Refuge s conservation bison herd is an opportunity for attracting broader audiences and supporters, across metropolitan Denver and more globally, to the Refuge and connecting them to the grassland ecosystem it protects. Moreover, the Refuge bison herd is a highly recognizable ambassador herd (and part of the U.S. Fish and Wildlife Service s bison meta-population critical for conservation) that can be interpreted to more effectively demonstrate to urban audiences the importance and value of grassland restoration and contemporary bison recovery across the American West.; abstract: A Visitor Study and Report on the Connections between People, Place and Bison Conservation at the Rocky Mountain Arsenal National Wildlife Refuge. This study makes an important contribution to visitor management and human dimensions of wildlife research at the Rocky Mountain Arsenal National Wildlife Refuge (RMA or Refuge) in metropolitan Denver. Denver Zoo researchers conducted visitor-intercept interviews to collect data about how the Refuge s conservation bison herd, reintroduced in 2009, is shaping the visitor experience. This report summarizes the 2015 findings of this work including the socio-demographic characteristics of Refuge visitors, their self-reported site use patterns and experiences, and their sense of connection to this grassland protected area and its conservation bison herd. Research findings illuminate greater opportunities for RMA, as an urban refuge, to attract and engage Denver publics and other visitors through its bison conservation herd, cementing the value of RMA herd for social and ecological benefit. Denver Zoo s conservation social science team conducted 100 visitor-intercept interviews with Refuge visitors from early July to late August 2015. These interviews followed the pilot testing of a structured interview guide in mid-June 2015. A multi-stage random sampling design for the visitor intercepts ensured a highly representative sample. Interviews were conducted across a range of weeks, days (e.g. weekdays and weekends), and times (morning and afternoon) to capture a variety of visitors. In conclusion, the Refuge s bison herd motivates visitation for almost 20% of Refuge visitors. The herd is seen by visitors as an asset and natural amenity that adds value to their experience and sense of connection to the Refuge. Looking forward, the Refuge s conservation bison herd is an opportunity for attracting broader audiences and supporters, across metropolitan Denver and more globally, to the Refuge and connecting them to the grassland ecosystem it protects. Moreover, the Refuge bison herd is a highly recognizable ambassador herd (and part of the U.S. Fish and Wildlife Service s bison meta-population critical for conservation) that can be interpreted to more effectively demonstrate to urban audiences the importance and value of grassland restoration and contemporary bison recovery across the American West.
http://reference.data.gov.uk/id/open-government-licencehttp://reference.data.gov.uk/id/open-government-licence
This MSOA atlas provides a summary of demographic and related data for each Middle Super Output Area in Greater London. The average population of an MSOA in London in 2010 was 8,346, compared with 1,722 for an LSOA and 13,078 for a ward.
The profiles are designed to provide an overview of the population in these small areas by combining a range of data on the population, births, deaths, health, housing, crime, commercial property/floorspace, income, poverty, benefits, land use, environment, deprivation, schools, and employment.
If you need to find an MSOA and you know the postcode of the area, the ONS NESS search page has a tool for this.
The MSOA Atlas is available as an XLS as well as being presented using InstantAtlas mapping software. This is a useful tool for displaying a large amount of data for numerous geographies, in one place (requires HTML 5).
CURRENT MSOA BOUNDARIES (2011)
PREVIOUS MSOA BOUNDARIES (2001)
NB. It is currently not possible to export the map as a picture due to a software issue with the Google Maps background. We advise you to print screen to copy an image to the clipboard.
Tips:
- Select a new indicator from the Data box on the left. Select the theme, then indicator and then year to show the data.
- To view data just for one borough*, use the filter tool.
- The legend settings can be altered by clicking on the pencil icon next to the MSOA tick box within the map legend.
- The areas can be ranked in order by clicking at the top of the indicator column of the data table.
Themes included here are Census 2011 Population, Mid-year Estimates, Population by Broad Age, Households, Household composition, Ethnic Group, Country of Birth, Language, Religion, Tenure, Dwelling type, Land Area, Population Density, Births, General Fertility Rate, Deaths, Standardised Mortality Ratio (SMR), Population Turnover Rates (per 1000), Crime (numbers), Crime (rates), House Prices, Commercial property (number), Rateable Value (£ per m2), Floorspace; ('000s m2), Household Income, Household Poverty, County Court Judgements (2005), Qualifications, Economic Activity, Employees, Employment, Claimant Count, Pupil Absence, Early Years Foundation Stage, Key Stage 1, GCSE and Equivalent, Health, Air Emissions, Car or Van availability, Income Deprivation, Central Heating, Incidence of Cancer, Life Expectancy, and Road Casualties.
These profiles were created using the most up to date information available at the time of collection (Spring 2014).
You may also be interested in LSOA Atlas and Ward Atlas.
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
This MSOA atlas provides a summary of demographic and related data for each Middle Super Output Area in Greater London. The average population of an MSOA in London in 2010 was 8,346, compared with 1,722 for an LSOA and 13,078 for a ward. The profiles are designed to provide an overview of the population in these small areas by combining a range of data on the population, births, deaths, health, housing, crime, commercial property/floorspace, income, poverty, benefits, land use, environment, deprivation, schools, and employment. If you need to find an MSOA and you know the postcode of the area, the ONS NESS search page has a tool for this. The MSOA Atlas is available as an XLS as well as being presented using InstantAtlas mapping software. This is a useful tool for displaying a large amount of data for numerous geographies, in one place (requires HTML 5). CURRENT MSOA BOUNDARIES (2011) PREVIOUS MSOA BOUNDARIES (2001) NB. It is currently not possible to export the map as a picture due to a software issue with the Google Maps background. We advise you to print screen to copy an image to the clipboard. Tips: - Select a new indicator from the Data box on the left. Select the theme, then indicator and then year to show the data. - To view data just for one borough*, use the filter tool. - The legend settings can be altered by clicking on the pencil icon next to the MSOA tick box within the map legend. - The areas can be ranked in order by clicking at the top of the indicator column of the data table. Themes included here are Census 2011 Population, Mid-year Estimates, Population by Broad Age, Households, Household composition, Ethnic Group, Country of Birth, Language, Religion, Tenure, Dwelling type, Land Area, Population Density, Births, General Fertility Rate, Deaths, Standardised Mortality Ratio (SMR), Population Turnover Rates (per 1000), Crime (numbers), Crime (rates), House Prices, Commercial property (number), Rateable Value (£ per m2), Floorspace; ('000s m2), Household Income, Household Poverty, County Court Judgements (2005), Qualifications, Economic Activity, Employees, Employment, Claimant Count, Pupil Absence, Early Years Foundation Stage, Key Stage 1, GCSE and Equivalent, Health, Air Emissions, Car or Van availability, Income Deprivation, Central Heating, Incidence of Cancer, Life Expectancy, and Road Casualties. The London boroughs are: City of London, Barking and Dagenham, Barnet, Bexley, Brent, Bromley, Camden, Croydon, Ealing, Enfield, Greenwich, Hackney, Hammersmith and Fulham, Haringey, Harrow, Havering, Hillingdon, Hounslow, Islington, Kensington and Chelsea, Kingston upon Thames, Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond upon Thames, Southwark, Sutton, Tower Hamlets, Waltham Forest, Wandsworth, Westminster. These profiles were created using the most up to date information available at the time of collection (Spring 2014). You may also be interested in LSOA Atlas and Ward Atlas.
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
The LSOA atlas provides a summary of demographic and related data for each Lower Super Output Area in Greater London. The average population of an LSOA in London in 2010 was 1,722 compared with 8,346 for an MSOA and 13,078 for a ward. The profiles are designed to provide an overview of the population in these small areas by combining a range of data on the population, diversity, households, health, housing, crime, benefits, land use, deprivation, schools, and employment. Due to significant population change in some areas, not all 2011 LSOA boundaries are the same as previous LSOA boundaries that had been used from 2001. A lot of data is still only available using the 2001 boundaries therefore two Atlases have been created - one using the current LSOA boundaries (2011) and one using the previous boundaries (2001). If you need to find an LSOA and you know the postcode of the area, the ONS NESS search page has a tool for this. The LSOA Atlas is available as an XLS as well as being presented using InstantAtlas mapping software. This is a useful tool for displaying a large amount of data for numerous geographies, in one place (requires HTML 5). CURRENT LSOA BOUNDARIES (2011) NOTE: There is comparatively less data for the new boundaries compared with the old boundaries PREVIOUS LSOA BOUNDARIES (2001) For 2011 Census data used in the 2001 Boundaries Atlas: For simplicity, where two or more areas have been merged, the figures for these areas have been divided by the number of LSOAs that used to make that area up. Therefore, these data are not official ONS statisitcs, but presented here as indicative to display trends. NB. It is currently not possible to export the map as a picture due to a software issue with the Google Maps background. We advise you to print screen to copy an image to the clipboard. IMPORTANT: Due to the large amount of data and areas, the LSOA Atlas may take up to a minute to fully load. Once loaded, the report will work more efficiently by using the filter tool and selecting one borough at a time. Displaying every LSOA in London will slow down the data reload. Tips: - Select a new indicator from the Data box on the left. Select the theme, then indicator and then year to show the data. - To view data just for one borough, use the filter tool. - The legend settings can be altered by clicking on the pencil icon next to the LSOA tick box within the map legend. - The areas can be ranked in order by clicking at the top of the indicator column of the data table. Beware of large file size for 2001 Boundary Atlas (58MB) alternatively download Zip file (21MB). Themes included in the atlases are Census 2011 population, Mid-year Estimates by age, Population Density, Households, Household Composition, Ethnic Group, Language, Religion, Country of Birth, Tenure, Number of dwellings, Vacant Dwellings, Dwellings by Council Tax Band, Crime (numbers), Crime (rates), Economic Activity, Qualifications, House Prices, Workplace employment numbers, Claimant Count, Employment and Support Allowance, Benefits claimants, State Pension, Pension Credit, Incapacity Benefit/ SDA, Disability Living Allowance, Income Support, Financial vulnerability, Health and Disability, Land use, Air Emissions, Energy consumption, Car or Van access, Accessibility by Public Transport/walk, Road Casualties, Child Benefit, Child Poverty, Lone Parent Families, Out-of-Work families, Fuel Poverty, Free School Meals, Pupil Absence, Early Years Foundation Stage, Key Stage 1, Key Stage 2, GCSE, Level 3 (e.g A/AS level), The Indices of Deprivation 2010, Economic Deprivation Index, and The IMD 2010 Underlying Indicators. The London boroughs are: City of London, Barking and Dagenham, Barnet, Bexley, Brent, Bromley, Camden, Croydon, Ealing, Enfield, Greenwich, Hackney, Hammersmith and Fulham, Haringey, Harrow, Havering, Hillingdon, Hounslow, Islington, Kensington and Chelsea, Kingston upon Thames, Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond upon Thames, Southwark, Sutton, Tower Hamlets, Waltham Forest, Wandsworth, Westminster. These profiles were created using the most up to date information available at the time of collection (Spring 2014). You may also be interested in MSOA Atlas and Ward Atlas.
*** TYPE OF SURVEY AND METHODS *** The data set includes responses to a survey conducted by professionally trained interviewers of a social and market research company in the form of computer-aided telephone interviews (CATI) from 2017-02 to 2017-04. The target population was inhabitants of Germany aged 18 years and more, who were randomly selected by using the sampling approaches ADM eASYSAMPLe (based on the Gabler-Häder method) for landline connections and eASYMOBILe for mobile connections. The 1,331 completed questionnaires comprise 44.2 percent mobile and 55.8 percent landline phone respondents. Most questions had options to answer with a 5-point rating scale (Likert-like) anchored with ‘Fully agree’ to ‘Do not agree at all’, or ‘Very uncomfortable’ to ‘Very comfortable’, for instance. Responses by the interviewees were weighted to obtain a representation of the entire German population (variable ‘gewicht’ in the data sets). To this end, standard weighting procedures were applied to reduce differences between the sample and the entire population with regard to known rates of response and non-response depending on household size, age, gender, educational level, and place of residence. *** RELATED PUBLICATION AND FURTHER DETAILS *** The questionnaire, analysis and results will be published in the corresponding report (main text in English language, questionnaire in Appendix B in German language of the interviews and English translation). The report will be available as open access publication at KIT Scientific Publishing (https://www.ksp.kit.edu/). Reference: Orwat, Carsten; Schankin, Andrea (2018): Attitudes towards big data practices and the institutional framework of privacy and data protection - A population survey, KIT Scientific Report 7753, Karlsruhe: KIT Scientific Publishing. *** FILE FORMATS *** The data set of responses is saved for the repository KITopen at 2018-11 in the following file formats: comma-separated values (.csv), tapulator-separated values (.dat), Excel (.xlx), Excel 2007 or newer (.xlxs), and SPSS Statistics (.sav). The questionnaire is saved in the following file formats: comma-separated values (.csv), Excel (.xlx), Excel 2007 or newer (.xlxs), and Portable Document Format (.pdf). *** PROJECT AND FUNDING *** The survey is part of the project Assessing Big Data (ABIDA) (from 2015-03 to 2019-02), which receives funding from the Federal Ministry of Education and Research (BMBF), Germany (grant no. 01IS15016A-F). http://www.abida.de *** CONTACT *** Carsten Orwat, Karlsruhe Institute of Technology, Institute for Technology Assessment and Systems Analysis orwat@kit.edu Andrea Schankin, Karlsruhe Institute of Technology, Institute of Telematics andrea.schankin@kit.edu
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Demographic Characteristics (N = 1,741).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Socio-demographic characteristics of participants (n = 113).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Demographic description of the two subject groups and outcomes in cognitive architecture.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Details of Ae. koreicus collected from Italy, Slovenia, and the Republic of Korea (ROK) that were used in the population genetic study. For each population, details about the first historical record, year of collection, symbol for the identification, and number of samples are indicated (N). For the historical reports, the references are indicated. Geographical references of the collection site and details of single specimens can be found in S1 Table.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Summary of demographic data of the subjects considered in this report (obtained from ANDES cohort, n = 101).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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One-way analysis of variance (ANOVA) for continuous variables among the three groups. Chi-square test for categorical variables.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Demographic and pulmonary function test variables.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Demographic data of experimental subjects.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Participant Demographics from the SHARP Study only include those participants with a schizophrenia diagnosis.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Phase 2 demographics and neuropsychological test performance.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The objective of this study is to evaluate users’ perceptions and preferences on the design features of the COVID-19 prevention promotion icon from the perspective of users’ aesthetic and perceptual needs. In this study, 120 officially published icons from 24 countries and regions were collected from online platforms for ranking tests, and then the top-ranked icons were subjectively rated by the semantic differential method. By evaluating the quality of users’ perceptions of multiple semantic dimensions of icons, we extracted the perceptual semantic words that users valued as the main icon design features. Spearmen correlations were applied to derive possible correlations between user rankings and semantic scales, and a Friedman test was also conducted to determine the true differences in user perceptions and preferences for different styles of icons. Factor analysis was conducted to extract six perceptual words that influence the design features of the COVID-19 prevention promotion icon. The methodology adopted in this study facilitated the screening of design features related to icon effectiveness, and the findings show that “Interesting,” “Simple,” “Familiar, “Recognizable,” “Concrete,” and “Close(semantic distance)” are the key features that influence users’ perception and preference of COVID-19 icon design. The results of this study can be used as the basis for designing and improving publicity icons for preventive measures in COVID-19, and the methods adopted in this study can be applied to evaluate other types of icon design.