This dataset identifies all regions in which the full 95% confidence interval is wholly between 1.5 and 16 �C for all 12 months. The sea surface temperature data includes the mean sea surface temperature per month, the standard deviation and the number of observations used to calculate the mean. Based on these values, the 95% upper and lower confidence levels about the mean for each month have been generated.

Descriptive statistics of the dataset with mean, standard deviation (SD), median, and the lower (quantile 5%) and upper (quantile 95%) boundary of the 90% confidence interval.

This dataset identifies all regions in which the full 95% confidence interval is greater than 1 mg/m3 for all 12 months. The chlorophyll 2 data includes the mean chlorophyll 2 level per month, the standard deviation and the number of observations used to calculate the mean. Based on these values, the 95% upper and lower confidence levels about the mean for each month have been generated.

This dataset comes from the biennial City of Tempe Employee Survey question about feeling safe in the physical work environment (building). The Employee Survey question relating to this performance measure: “Please rate your level of agreement: My physical work environment (building) is safe, clean & maintained in good operating order.” Survey respondents are asked to rate their agreement level on a scale of 5 to 1, where 5 means “Strongly Agree” and 1 means “Strongly Disagree” (without “don’t know” responses included).The survey was voluntary and employees were allowed to complete the survey during work hours or at home. The survey allowed employees to respond anonymously and has a 95% confidence level.This page provides data about the Feeling Safe in City Facilities performance measure. The performance measure dashboard is available at 1.11 Feeling Safe in City FacilitiesAdditional InformationSource: Employee SurveyContact: Wydale HolmesContact E-Mail: Wydale_Holmes@tempe.govData Source Type: CSVPreparation Method: Data received from vendor and entered in CSVPublish Frequency: BiennialPublish Method: ManualData Dictionary (update pending)

In genomic study, log transformation is a common prepossessing step to adjust for skewness in data. This standard approach often assumes that log-transformed data is normally distributed, and two sample t-test (or its modifications) is used for detecting differences between two experimental conditions. However, recently it was shown that two sample t-test can lead to exaggerated false positives, and the Wilcoxon-Mann-Whitney (WMW) test was proposed as an alternative for studies with larger sample sizes. In addition, studies have demonstrated that the specific distribution used in modeling genomic data has profound impact on the interpretation and validity of results. The aim of this paper is three-fold: 1) to present the Exp-gamma distribution (exponential-gamma distribution stands for log-transformed gamma distribution) as a proper biological and statistical model for the analysis of log-transformed protein abundance data from single-cell experiments; 2) to demonstrate the inappropriateness of two sample t-test and the WMW test in analyzing log-transformed protein abundance data; 3) to propose and evaluate statistical inference methods for hypothesis testing and confidence interval estimation when comparing two independent samples under the Exp-gamma distributions. The proposed methods are applied to analyze protein abundance data from a single-cell dataset.

Vitamin D insufficiency appears to be prevalent in SLE patients. Multiple factors potentially contribute to lower vitamin D levels, including limited sun exposure, the use of sunscreen, darker skin complexion, aging, obesity, specific medical conditions, and certain medications. The study aims to assess the risk factors associated with low vitamin D levels in SLE patients in the southern part of Bangladesh, a region noted for a high prevalence of SLE. The research additionally investigates the possible correlation between vitamin D and the SLEDAI score, seeking to understand the potential benefits of vitamin D in enhancing disease outcomes for SLE patients. The study incorporates a dataset consisting of 50 patients from the southern part of Bangladesh and evaluates their clinical and demographic data. An initial exploratory data analysis is conducted to gain insights into the data, which includes calculating means and standard deviations, performing correlation analysis, and generating heat maps. Relevant inferential statistical tests, such as the Student’s t-test, are also employed. In the machine learning part of the analysis, this study utilizes supervised learning algorithms, specifically Linear Regression (LR) and Random Forest (RF). To optimize the hyperparameters of the RF model and mitigate the risk of overfitting given the small dataset, a 3-Fold cross-validation strategy is implemented. The study also calculates bootstrapped confidence intervals to provide robust uncertainty estimates and further validate the approach. A comprehensive feature importance analysis is carried out using RF feature importance, permutation-based feature importance, and SHAP values. The LR model yields an RMSE of 4.83 (CI: 2.70, 6.76) and MAE of 3.86 (CI: 2.06, 5.86), whereas the RF model achieves better results, with an RMSE of 2.98 (CI: 2.16, 3.76) and MAE of 2.68 (CI: 1.83,3.52). Both models identify Hb, CRP, ESR, and age as significant contributors to vitamin D level predictions. Despite the lack of a significant association between SLEDAI and vitamin D in the statistical analysis, the machine learning models suggest a potential nonlinear dependency of vitamin D on SLEDAI. These findings highlight the importance of these factors in managing vitamin D levels in SLE patients. The study concludes that there is a high prevalence of vitamin D insufficiency in SLE patients. Although a direct linear correlation between the SLEDAI score and vitamin D levels is not observed, machine learning models suggest the possibility of a nonlinear relationship. Furthermore, factors such as Hb, CRP, ESR, and age are identified as more significant in predicting vitamin D levels. Thus, the study suggests that monitoring these factors may be advantageous in managing vitamin D levels in SLE patients. Given the immunological nature of SLE, the potential role of vitamin D in SLE disease activity could be substantial. Therefore, it underscores the need for further large-scale studies to corroborate this hypothesis.

Assessment of the confidence limits of the data base by means of evaluation of the two involved numerical models: The wave model WAM (Parameter: Significant wave height Hs) and the Atmospheric model SKIRON (Parameter: Wind Speed 10m)

This dataset identifies all regions in which the full 95% confidence interval is between 4 and 16 �C for all 12 months. The sea surface temperature data includes the mean sea surface temperature per month, the standard deviation and the number of observations used to calculate the mean. Based on these values, the 95% upper and lower confidence levels about the mean for each month have been generated.

This dataset comes from the Annual Community Survey questions about satisfaction with Quality of City Services. The Community Survey question that relates to this dataset is: “Quality of services provided by City of Tempe.” Respondents are asked to rate their satisfaction level using a scale of 1 to 5, where 1 means "Very Dissatisfied" and 5 means "Very Satisfied".The survey is mailed to a random sample of households in the City of Tempe and has a 95% confidence level.This page provides data for the Quality of City Services performance measure.The performance measure dashboard is available at 3.36 Quality of City Services.Additional InformationSource: Community Attitude Survey (Vendor: ETC Institute) Contact: Wydale HolmesContact E-Mail: wydale_holmes@tempe.govData Source Type: Excel and PDF ReportPreparation Method: Extracted from Annual Community Survey resultsPublish Frequency: AnnualPublish Method: ManualData Dictionary

Czech translation of WordSim353. The Czech translation of English WordSim353 word pairs were obtained from four translators. All translation variants were scored according to the lexical similarity/relatedness annotation instructions for WordSim353 annotators, by 25 Czech annotators. The resulting data set consists of two annotation files: "WordSim353-cs.csv" and "WordSim-cs-Multi.csv". Both files are encoded in UTF-8, have a header, text is enclosed in double quotes, and columns are separated by commas. The rows are numbered. The WordSim-cs-Multi data set has rows numbered from 1 to 634, whereas the row indices in the WordSim353-cs data set reflect the corresponding row numbers in the WordSim-cs-Multi data set.

The WordSim353-cs file contains a one-to-one mapping selection of 353 Czech equivalent pairs whose judgments have proven to be most similar to the judgments of their corresponding English originals (compared by the absolute value of the difference between the means over all annotators in each language counterpart). In one case ("psychology-cognition"), two Czech equivalent pairs had identical means as well as confidence intervals, so we randomly selected one.

The "WordSim-cs-Multi.csv" file contains human judgments for all translation variants.

In both data sets, we preserved all 25 individual scores. In the WordSim353-cs data set, we added a column with their Czech means as well as a column containing the original English means and 95% confidence intervals in separate columns for each mean (computed by the CI function in the Rmisc R package). The WordSim-cs-Multi data set contains only the Czech means and confidence intervals. For the most convenient lexical search, we provided separate columns with the respective Czech and English single words, entire word pairs, and eventually an English-Czech quadruple in both data sets.

The data set also contains an xls table with the four translations and a preliminary selection of the best variants performed by an adjudicator.

This dataset comes from the Annual Community Survey question about satisfaction with the Value of Special Events. The Community Survey question relating to the Value of Special Events performance measure: "Please rate your level of satisfaction with each of the following: a) Value & benefits received by City from special events." Respondents are asked to rate their satisfaction level on a scale of 5 to 1, where 5 means "Very Satisfied" and 1 means "Very Dissatisfied" (without "don't know" as an option).The survey is mailed to a random sample of households in the City of Tempe and has a 95% confidence level.This page provides data for the Value of Special Events performance measure. The performance measure dashboard is available at 3.19 Value of Special Events.Additional InformationSource: Community Attitude Survey ( Vendor: ETC Institute)Contact: Wydale HolmesContact E-Mail: wydale_holmes@tempe.govData Source Type: Excel and PDFPreparation Method: Extracted from Annual Community Survey resultsPublish Frequency: AnnualPublish Method: Manual Data Dictionary

This dataset comes from the Annual Community Survey question related to satisfaction with the quality of the city’s online services. Respondents are asked to provide their level of satisfaction related to “Tempe's online services (registration, payment, etc.)” on a scale of 5 to 1, where 5 means "Very Satisfied" and 1 means "Very Dissatisfied" (without "don't know" as an option).The survey is mailed to a random sample of households in the City of Tempe and has a 95% confidence level.This page provides data for the Online Service Satisfaction performance measure. The performance measure dashboard is available at 2.05 Online Services Satisfaction Rate.Additional Information Source: Community Attitude Survey ( Vendor: ETC Institute)Contact: Wydale HolmesContact E-Mail: Wydale_Holmes@tempe.govData Source Type: Excel and PDFPreparation Method: Extracted from Annual Community Survey results  Publish Frequency: Annual  Publish Method: Manual  Data Dictionary

This data set contains estimated teen birth rates for age group 15–19 (expressed per 1,000 females aged 15–19) by county and year.

DEFINITIONS

Estimated teen birth rate: Model-based estimates of teen birth rates for age group 15–19 (expressed per 1,000 females aged 15–19) for a specific county and year. Estimated county teen birth rates were obtained using the methods described elsewhere (1,2,3,4). These annual county-level teen birth estimates “borrow strength” across counties and years to generate accurate estimates where data are sparse due to small population size (1,2,3,4). The inferential method uses information—including the estimated teen birth rates from neighboring counties across years and the associated explanatory variables—to provide a stable estimate of the county teen birth rate. Median teen birth rate: The middle value of the estimated teen birth rates for the age group 15–19 for counties in a state. Bayesian credible intervals: A range of values within which there is a 95% probability that the actual teen birth rate will fall, based on the observed teen births data and the model.

NOTES

Data on the number of live births for women aged 15–19 years were extracted from the National Center for Health Statistics’ (NCHS) National Vital Statistics System birth data files for 2003–2015 (5).

Population estimates were extracted from the files containing intercensal and postcensal bridged-race population estimates provided by NCHS. For each year, the July population estimates were used, with the exception of the year of the decennial census, 2010, for which the April estimates were used.

Hierarchical Bayesian space–time models were used to generate hierarchical Bayesian estimates of county teen birth rates for each year during 2003–2015 (1,2,3,4).

The Bayesian analogue of the frequentist confidence interval is defined as the Bayesian credible interval. A 100*(1-α)% Bayesian credible interval for an unknown parameter vector θ and observed data vector y is a subset C of parameter space Ф such that 1-α≤P({C│y})=∫p{θ │y}dθ, where integration is performed over the set and is replaced by summation for discrete components of θ. The probability that θ lies in C given the observed data y is at least (1- α) (6).

County borders in Alaska changed, and new counties were formed and others were merged, during 2003–2015. These changes were reflected in the population files but not in the natality files. For this reason, two counties in Alaska were collapsed so that the birth and population counts were comparable. Additionally, Kalawao County, a remote island county in Hawaii, recorded no births, and census estimates indicated a denominator of 0 (i.e., no females between the ages of 15 and 19 years residing in the county from 2003 through 2015). For this reason, Kalawao County was removed from the analysis. Also , Bedford City, Virginia, was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. For consistency, Bedford City was merged with Bedford County, Virginia, for the entire 2003–2015 period. Final analysis was conducted on 3,137 counties for each year from 2003 through 2015. County boundaries are consistent with the vintage 2005–2007 bridged-race population file geographies (7).

ABOUT THE COMMUNITY SURVEY REPORTFinal Reports for ETC Institute conducted annual community attitude surveys for the City of Tempe. These survey reports help determine priorities for the community as part of the City's on-going strategic planning process.In many of the survey questions, survey respondents are asked to rate their satisfaction level on a scale of 5 to 1, where 5 means "Very Satisfied" and 1 means "Very Dissatisfied" (while some questions follow another scale). The survey is mailed to a random sample of households in the City of Tempe and has a 95% confidence level.PERFORMANCE MEASURESData collected in these surveys applies directly to a number of performance measures for the City of Tempe including the following (as of 2022):1. Safe and Secure Communities1.04 Fire Services Satisfaction1.06 Crime Reporting1.07 Police Services Satisfaction1.09 Victim of Crime1.10 Worry About Being a Victim1.11 Feeling Safe in City Facilities1.23 Feeling of Safety in Parks2. Strong Community Connections2.02 Customer Service Satisfaction2.04 City Website Satisfaction2.05 Online Services Satisfaction Rate2.15 Feeling Invited to Participate in City Decisions2.21 Satisfaction with Availability of City Information3. Quality of Life3.16 City Recreation, Arts, and Cultural Centers3.17 Community Services Programs3.19 Value of Special Events3.23 Right of Way Landscape Maintenance3.36 Quality of City Services4. Sustainable Growth & DevelopmentNo Performance Measures in this category presently relate directly to the Community Survey5. Financial Stability & VitalityNo Performance Measures in this category presently relate directly to the Community SurveyMethodsThe survey is mailed to a random sample of households in the City of Tempe. Follow up emails and texts are also sent to encourage participation. A link to the survey is provided with each communication. To prevent people who do not live in Tempe or who were not selected as part of the random sample from completing the survey, everyone who completed the survey was required to provide their address. These addresses were then matched to those used for the random representative sample. If the respondent’s address did not match, the response was not used. To better understand how services are being delivered across the city, individual results were mapped to determine overall distribution across the city. Additionally, demographic data were used to monitor the distribution of responses to ensure the responding population of each survey is representative of city population. The 2022 Annual Community Survey data are available on data.tempe.gov. The individual survey questions as well as the definition of the response scale (for example, 1 means “very dissatisfied” and 5 means “very satisfied”) are provided in the data dictionary.More survey information may be found on the Strategic Management and Innovation Signature Surveys, Research and Data page at https://www.tempe.gov/government/strategic-management-and-innovation/signature-surveys-research-and-data.Additional InformationSource: Community Attitude SurveyContact (author): Adam SamuelsContact E-Mail (author): Adam_Samuels@tempe.govContact (maintainer): Contact E-Mail (maintainer): Data Source Type: Excel tablePreparation Method: Data received from vendor after report is completedPublish Frequency: AnnualPublish Method: ManualData Dictionary

This dataset comes from the Annual Community Survey question "Please rate your level of satisfaction with each of the following: a) Your ability to participate in City decision-making processes." Respondents are asked to rate their satisfaction level on a scale of 5 to 1, where 5 means "Very Satisfied" and 1 means "Very Dissatisfied" (without "don't know" as an option). This question relates to the Participating in City Decisions performance measure:The survey is mailed to a random sample of households in the City of Tempe and has a 95% confidence level.This page provides data for the Participating in City Decisions performance measure. The performance measure dashboard is available at 2.15 Participating in City Decisions.Additional InformationSource: Community Attitude Survey ( Vendor: ETC Institute)Contact: Wydale HolmesContact E-Mail: Wydale_Holmes@tempe.govData Source Type: Excel and PDFPreparation Method: Extracted from Annual Community Survey resultsPublish Frequency: AnnualPublish Method: ManualData Dictionary

Data for Figure 3.34 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6).

Figure 3.34 shows attribution of observed seasonal trends in the annular modes to forcings.

How to cite this dataset

When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005.

Figure subpanels

The figure has 3 panels, and all the data are provided in a single file named NAM_SAM_detection_attribution.nc.

List of data provided

This dataset contains

• Observed and simulated DJF NAM trends for 1958-2019
• Observed and simulated JJA NAM trends for 1958-2019
• Observed and simulated DJF SAM trends for 1979-2019
• Observed and simulated JJA SAM trends for 1979-2019
• Observed and simulated DJF SAM trends for 2000-2019
• Observed and simulated JJA SAM trends for 2000-2019 Simulations are from CMIP6 historical, hist-GHG, hist-aer, hist-nat, and hist-stratO3 simulations, and from equivalent time segments from CMIP6 piControl simulations (one segment from one model).

NAM: Northern Annular Mode ​​​ SAM: Southern Annular Mode GHG: greenhouse gas JJA: June, July, August DJF: December, January, February

Data provided in relation to figure

Panel a: - NAM_obs_DJF_1958_2019: grey horizontal lines in the left -->ERA5: obs_dataset = 0

 -->JRA-55: obs_dataset = 1

• NAM_piControl_DJF_62yrs: multimodel ensemble mean and percentiles for blue open box-whisker in the left
• NAM_hist_DJF_1958_2019: multimodel ensemble mean and percentiles for red open box-whisker in the left, and multimodel ensemble mean and confidence interval for red filled box, with ensemble means of individual models for black dots, in the left
• NAM_GHG_DJF_1958_2019: multimodel ensemble mean and confidence interval for brown filled box, with ensemble means of individual models for black dots, in the left
• NAM_aer_DJF_1958_2019: multimodel ensemble mean and confidence interval for light blue filled box, with ensemble means of individual models for black dots, in the left
• NAM_stratO3_DJF_1958_2019: multimodel ensemble mean and confidence interval for purple filled box, with ensemble means of individual models for black dots, in the left
• NAM_nat_DJF_1958_2019: multimodel ensemble mean and confidence interval for green filled box, with ensemble means of individual models for black dots, in the left

• NAM_obs_JJA_1958_2019: grey horizontal lines in the right -->ERA5: obs_dataset = 0

  -->JRA-55: obs_dataset = 1

• NAM_piControl_JJA_62yrs: multimodel ensemble mean and percentiles for blue open box-whisker in the right

• NAM_hist_JJA_1958_2019: multimodel ensemble mean and percentiles for red open box-whisker in the right, and multimodel ensemble mean and confidence interval for red filled box, with ensemble means of individual models for black dots, in the right

• NAM_GHG_JJA_1958_2019: multimodel ensemble mean and confidence interval for brown filled box, with ensemble means of individual models for black dots, in the right

• NAM_aer_JJA_1958_2019: multimodel ensemble mean and confidence interval for light blue filled box, with ensemble means of individual models for black dots, in the right

• NAM_stratO3_JJA_1958_2019: multimodel ensemble mean and confidence interval for purple filled box, with ensemble means of individual models for black dots, in the right

• NAM_nat_JJA_1958_2019: multimodel ensemble mean and confidence interval for green filled box, with ensemble means of individual models for black dots, in the right ... For full abstract see: https://catalogue.ceda.ac.uk/uuid/678ee967fe114a34a6d1f7d50e4aa7ee.

Conformal prediction is introduced as an alternative approach to domain applicability estimation. The advantages of using conformal prediction are as follows: First, the approach is based on a consistent and well-defined mathematical framework. Second, the understanding of the confidence level concept in conformal predictions is straightforward, e.g. a confidence level of 0.8 means that the conformal predictor will commit, at most, 20% errors (i.e., true values outside the assigned prediction range). Third, the confidence level can be varied depending on the situation where the model is to be applied and the consequences of such changes are readily understandable, i.e. prediction ranges are increased or decreased, and the changes can immediately be inspected. We demonstrate the usefulness of conformal prediction by applying it to 10 publicly available data sets.

Analysis of ‘2.15 Feeling Invited to Participate (summary)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/8f236200-9d1e-4f14-afa6-4b44614e472e on 11 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset comes from the Annual Community Survey question "Please rate your level of satisfaction with each of the following: a) Your ability to participate in City decision-making processes." Respondents are asked to rate their satisfaction level on a scale of 5 to 1, where 5 means "Very Satisfied" and 1 means "Very Dissatisfied" (without "don't know" as an option). This question relates to the Feeling Invited to Participate in City Decisions performance measure:

The survey is mailed to a random sample of households in the City of Tempe and has a 95% confidence level.

This page provides data for the Feeling Invited to Participate in City Decisions performance measure.

The performance measure dashboard is available at 2.15 Feeling Invited to Participate.

Additional Information

Source:Community Attitude Survey ( Vendor: ETC Institute)

Contact: Wydale Holmes

Contact E-Mail: Wydale_Holmes@tempe.gov

Data Source Type: Excel and PDF

Preparation Method: Extracted from Annual Community Survey results

Publish Frequency: Annual

Publish Method: Manual

Data Dictionary

--- Original source retains full ownership of the source dataset ---

Analysis of ‘3.17 Community Services Programs (summary)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7d0b904f-e944-469c-b647-2d2ba1ade6cb on 11 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset comes from the Annual Community Survey questions about satisfaction with Community Service Programs. The Community Survey question relating to the Community Services Programs performance measure: "Please rate your level of satisfaction with each of the following: a) Quality of Before & After School (Kid Zone) programs; b) Quality of City library programs & services; c) Quality of City recreation programs & services; d) Quality of Tempe Center for the Arts programs." Respondents are asked to rate their satisfaction level on a scale of 5 to 1, where 5 means "Very Satisfied" and 1 means "Very Dissatisfied" (responses of "don't know" are excluded).

The survey is mailed to a random sample of households in the City of Tempe and has a 95% confidence level.

This page provides data for the Community Services Programs performance measure.

The performance measure dashboard is available at 3.17 Community Services Programs.

Additional Information

Source: Community Attitude Survey (Vendor: ETC Institute)

Contact: Wydale Holmes

Contact E-Mail: wydale_holmes@tempe.gov

Data Source Type: Excel and PDF Report

Preparation Method: Extracted from Annual Community Survey results

Publish Frequency: Annual

Publish Method: Manual

Data Dictionary

--- Original source retains full ownership of the source dataset ---

This dataset identifies all regions in which the full 95% confidence interval is between 4 and 18 �C for all 12 months. The sea surface temperature data includes the mean sea surface temperature per month, the standard deviation and the number of observations used to calculate the mean. Based on these values, the 95% upper and lower confidence levels about the mean for each month have been generated.