In over 100 years of blood donation practice, INTERVAL is the first randomised controlled trial to assess the impact of varying the frequency of blood donation on donor health and the blood supply. It provided policy-makers with evidence that collecting blood more frequently than current intervals can be implemented over two years without impacting on donor health, allowing better management of the supply to the NHS of units of blood with in-demand blood groups. INTERVAL was designed to deliver a multi-purpose strategy: an initial purpose related to blood donation research aiming to improve NHS Blood and Transplant’s core services and a longer-term purpose related to the creation of a comprehensive resource that will enable detailed studies of health-related questions.

Approximately 50,000 generally healthy blood donors were recruited between June 2012 and June 2014 from 25 NHS Blood Donation centres across England. Approximately equal numbers of men and women; aged from 18-80; ~93% white ancestry. All participants completed brief online questionnaires at baseline and gave blood samples for research purposes. Participants were randomised to giving blood every 8/10/12 weeks (for men) and 12/14/16 weeks (for women) over a 2-year period. ~30,000 participants returned after 2 years and completed a brief online questionnaire and gave further blood samples for research purposes.

The baseline questionnaire includes brief lifestyle information (smoking, alcohol consumption, etc), iron-related questions (e.g., red meat consumption), self-reported height and weight, etc. The SF-36 questionnaire was completed online at baseline and 2-years, with a 6-monthly SF-12 questionnaire between baseline and 2-years.

All participants have had the Affymetrix Axiom UK Biobank genotyping array assayed and then imputed to 1000G+UK10K combined reference panel (80M variants in total). 4,000 participants have 50X whole-exome sequencing and 12,000 participants have 15X whole-genome sequencing. Whole-blood RNA sequencing has commenced in ~5,000 participants.

The dataset also contains data on clinical chemistry biomarkers, blood cell traits, >200 lipoproteins, metabolomics (Metabolon HD4), lipidomics, and proteomics (SomaLogic, Olink), either cohort-wide or is large sub-sets of the cohort.

Introductory statistical inference texts and courses treat the point estimation, hypothesis testing, and interval estimation problems separately, with primary emphasis on large-sample approximations. Here, I present an alternative approach to teaching this course, built around p-values, emphasizing provably valid inference for all sample sizes. Details about computation and marginalization are also provided, with several illustrative examples, along with a course outline. Supplementary materials for this article are available online.

This table shows overall ATCEMS response interval performance for entire fiscal years. Data in the table is broken out by incident response priority and service area (City of Austin or Travis County).

This dataset comprises comprehensive information from ranked matches played in the game League of Legends, spanning the time frame between January 12, 2023, and May 18, 2023. The matches cover a wide range of skill levels, specifically from the Iron tier to the Diamond tier.

The dataset is structured based on time intervals, presenting game data at various percentages of elapsed game time, including 20%, 40%, 60%, 80%, and 100%. For each interval, detailed match statistics, player performance metrics, objective control, gold distribution, and other vital in-game information are provided.

This collection of data not only offers insights into how matches evolve and strategies change over different phases of the game but also enables the exploration of player behavior and decision-making as matches progress. Researchers and analysts in the field of esports and game analytics will find this dataset valuable for studying trends, developing predictive models, and gaining a deeper understanding of the dynamics within ranked League of Legends matches across different skill tiers.

These data document understory vegetation cover, richness and regeneration tree counts for a prescribed burning study with unburned controls on the Malheur National Forest in the southern Blue Mountains of Oregon. The original prescribed fires were conducted in the fall of 1997 and spring of 1998 and were repeated at two intervals, five and fifteen years. Five year interval reburns have been repeated three times (four burns total) and the fifteen year interval a single time (two burns total). Data include vegetation conditions prior to and following the last reburns and include understory vegetation cover; graminoid (grass and sedge) cover as well as leafing and flowering culm height, and flowering culm count data; shrub cover; conifer regeneration count data; presence/absence of all vascular species; and plant functional group information, descriptions and associated species.

We present a Wilson interval for binomial proportions for use with multiple imputation for missing data. Using simulation studies, we show that it can have better repeated sampling properties than the usual confidence interval for binomial proportions based on Rubin’s combining rules. Further, in contrast to the usual multiple imputation confidence interval for proportions, the multiple imputation Wilson interval is always bounded by zero and one. Supplementary material is available online.

A number of methods of evaluating the validity of interval forecasts of financial data are analysed, and illustrated using intraday FTSE100 index futures returns. Some existing interval forecast evaluation techniques, such as the Markov chain approach of Christoffersen (1998), are shown to be inappropriate in the presence of periodic heteroscedasticity. Instead, we consider a regression-based test, and a modified version of Christoffersen's Markov chain test for independence, and analyse their properties when the financial time series exhibit periodic volatility. These approaches lead to different conclusions when interval forecasts of FTSE100 index futures returns generated by various GARCH(1,1) and periodic GARCH(1,1) models are evaluated.

This is the data set behind the Wind Generation Interactive Query Tool created by the CEC. The visualization tool interactively displays wind generation over different time intervals in three-dimensional space. The viewer can look across the state to understand generation patterns of regions with concentrations of wind power plants. The tool aids in understanding high and low periods of generation. Operation of the electric grid requires that generation and demand are balanced in each period.

This table contains data describing ATCEMS performance in delivering patients with time-sensitive conditions (aka “Alert Patients”) to receiving facilities in a timely manner. The call-to-door interval begins when the first 911 call for an incident is answered in the Communications Center, and ends when the patient is recorded in CAD as arriving at a receiving facility.

Abridged life tables showing life expectancy at birth and at age 65, low 95% confidence interval, high 95% confidence interval, and coefficients of variation for life expectancy, by sex, 1990 to 2006.

There are surveys that gather precise information on an outcome of interest, but measure continuous covariates by a discrete number of intervals, in which case the covariates are interval censored. For applications with a second independent dataset precisely measuring the covariates, but not the outcome, this paper introduces a semiparametrically efficient estimator for the coefficients in a linear regression model. The second sample serves to establish point identification. An empirical application investigating the relationship between income and body mass index illustrates the use of the estimator.

This paper considers identification and estimation of a fixed-effects model with an interval-censored dependent variable. In each time period, the researcher observes the interval (with known endpoints) in which the dependent variable lies but not the value of the dependent variable itself. Two versions of the model are considered: a parametric model with logistic errors and a semiparametric model with errors having an unspecified distribution. In both cases, the error disturbances can be heteroskedastic over cross-sectional units as long as they are stationary within a cross-sectional unit; the semiparametric model also allows for serial correlation of the error disturbances. A conditional-logit-type composite likelihood estimator is proposed for the logistic fixed-effects model, and a composite maximum-score-type estimator is proposed for the semiparametric model. In general, the scale of the coefficient parameters is identified by these estimators, meaning that the causal effects of interest are estimated directly in cases where the latent dependent variable is of primary interest (e.g., pure data-coding situations). Monte Carlo simulations and an empirical application to birthweight outcomes illustrate the performance of the parametric estimator.

The New Zealand Fundamental Soil Layer originates from a relational join of features from two databases: the New Zealand Land Resource Inventory (NZLRI), and the National Soils Database (NSD). The NZLRI is a national polygon database of physical land resource information, including a soil unit. Soil is one in an inventory of five physical factors (including rock, slope, erosion, and vegetation) delineated by physiographic polygons at approximately 1:50,000 scale. The NSD is a point database of soil physical, chemical, and mineralogical characteristics for over 1500 soil profiles nationally. A relational join between the NZLRI dominant soil and derivative tables from the NSD was the means by which 14 important soil attributes were attached to the NZLRI polygons. Some if these attributes originate from exact matches with NSD records, while others derive from matches to similar soils or professional estimates. This layers contains flood return interval attributes. The classes originate from and are described more fully in Webb and Wilson (1995).

These data document surface fuels data for a prescribed burning study with unburned controls on the Malheur National Forest in the southern Blue Mountains of Oregon. The original prescribed fires were conducted in the fall of 1997 and spring of 1998 and were repeated at two intervals, five and fifteen years. Five year interval reburns have been repeated three times (four burns total) and the fifteen year interval a single time (two burns total). These data document fuels prior to (2012) and following the last reburns including 1-hour (0 to 0.64 centimeter [cm] diameter), 10-hour (0.64 to 2.54 cm diameter), 100-hour (2.54 to 7.62 cm diameter) and 1000-hour fuels (> 7.62 cm diameter); average combined litter and duff depth; and surface fuel height.

This dataset contains the data that was a basis for the results discussed in the paper “Persistent homology as a new method of the assessment of heart rate variability” by Grzegorz Graff, Beata Graff, Paweł Pilarczyk, Grzegorz Jabłoński, Dariusz Gąsecki, Krzysztof Narkiewicz, Plos One (2021), DOI: 10.1371/journal.pone.0253851.

Dataset contains direct, diffuse, global and downward longwave irradiances at 60 seconds time resolution. Dataset also contains air temperature, relative humidity and air pressure at instrument height. Supplemental information

The Global River Points dataset is a high-resolution vector file geodatabase of 73 rivers world-wide. Each river is represented by a series of points spaced 150 meters apart and each point has attached environmental attributes extracted from multiple data sets. The attributes include physical information (slope, elevation, temperature, precipitation, river width and discharge) and landscape variables (human influence, fishing pressure, and organic load). The dataset also incorporates the river classification data from the Global River Reach Classifications GloRiC Version 1.0 dataset.

The p-value is a likelihood ratio p-value and thus identical for both comparison measures. The numbers needed to treat (NNT) were based on the estimated risk difference.

This dataset is constructed using measurements of cloudheight-sensors and a algorithm for coverage. The dataset is neither validated nor are missing values completed.

These data document environmental variables including overstory canopy cover, O horizon depth, ground cover and soils for a prescribed burning study with unburned controls on the Malheur National Forest in the southern Blue Mountains of Oregon. The original prescribed fires were conducted in the fall of 1997 and spring of 1998 and were repeated at two intervals, five and fifteen years. Five year interval reburns have been repeated three times (four burns total) and the fifteen year interval a single time (two burns total). Data include environmental conditions prior to and following the last reburns except for soils data which were collected prior to the last reburns only. Specifically, this data publication includes overstory tree canopy cover data from the 10-meter radius plots, ground cover data (litter, rock, bare soil and coarse woody debris) from the 1 x 1 meter quadrats, O horizon depth data from the 10-meter radius plots, and soils data (e.g. carbon, nitrogen and phosphorous concentrations, pH and bulk density) from 2012.