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.

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.

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.

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.

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.

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.

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.

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

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

This RR interval dataset is derived from 10,000 cases of 24-hour Holter monitoring data sampled at 128 Hz. Among the cases, 9,500 are labeled as non-atrial fibrillation (NAF), and 500 as paroxysmal atrial fibrillation (PAF). These data have been used in the article "Clinician-AI Collaboration: A Win-Win solution for Efficiency and Reliability in Atrial Fibrillation Diagnosis".The dataset formated as CSV file consists of two columns:rr_interval: Represents the interval between consecutive R-peaks, measured in milliseconds.label: Categorical labels for the beats, where:1 indicates AF0 indicates NAF-1 indicates noise or artifactsEach case is named based on its category. NAF cases are labeled as NAF0001.csv through NAF9500.csv, while PAF cases are labeled as PAF0001.csv through PAF0500.csv.For any questions, please contact the email: hustzp@hust.edu.cn

Predicted water level heights at Bowen at regular time intervals.

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.

Predicted water level heights at Port Alma at regular time intervals.

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.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

• Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported.
• Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

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.

One problem with discriminant analysis of microarray data is representation of each sample by a large number of genes that are possibly irrelevant, insignificant, or redundant. Methods of variable selection are, therefore, of great significance in microarray data analysis. A new method for key gene selection has been proposed on the basis of interval segmentation purity that is defined as the purity of samples belonging to a certain class in intervals segmented by a mode search algorithm. This method identifies key variables most discriminative for each class, which offers possibility of unraveling the biological implication of selected genes. A salient advantage of the new strategy over existing methods is the capability of selecting genes that, though possibly exhibit a multimodal distribution, are the most discriminative for the classes of interest, considering that the expression levels of some genes may reflect systematic difference in within-class samples derived from different pathogenic mechanisms. On the basis of the key genes selected for individual classes, a support vector machine with block-wise kernel transform is developed for the classification of different classes. The combination of the proposed gene mining approach with support vector machine is demonstrated in cancer classification using two public data sets. The results reveal that significant genes have been identified for each class, and the classification model shows satisfactory performance in training and prediction for both data sets.

Supplementary MaterialData filesFigures A1 - A8: Simulations BoxplotsFigures B1: B16: Application Dendrograms Software: R and Matlab