In the United States in 2022, the majority of diagnostic vendors only shared data to health information exchanges (HIE) on a regional or state level. While around ** percent said they contributed data to a private HIE.

Principle data supporting "Scaling theory for the statistics of slip at frictional interfaces"

T. W. J. de Geus and M. Wyart (2022), Phys. Rev. E, 106(6):065001.

See code at doi: 10.5281/zenodo.10723197 (and its documentation) for workflow, detailed information of the data, and further dependencies.

The files N=*_Run*.zip contain fully restorable events for event-driven athermal quasi-static shear. Sequentually numbered files contain different parts of a single dataset.

The file summary.zip contains an extract of the key variables of these runs, and of triggers at different stresses. Finally, it contains "flow" data acquired by driving at finite rate.

The files N=3^6x4_Trigger_EnsemblePack.zip contain fully restorable triggers at different stresses in the largest system. The sequentially numbered files correspond to one dataset split in different .h5 files.

Highly specific (and poorly documentated) plotting functions are available upon request.

BackgroundNational Health Systems managers have been subject in recent years to considerable pressure to increase concentration and allow mergers. This pressure has been justified by a belief that larger hospitals lead to lower average costs and better clinical outcomes through the exploitation of economies of scale. In this context, the opportunity to measure scale efficiency is crucial to address the question of optimal productive size and to manage a fair allocation of resources.Methods and findingsThis paper analyses the stance of existing research on scale efficiency and optimal size of the hospital sector. We performed a systematic search of 45 past years (1969–2014) of research published in peer-reviewed scientific journals recorded by the Social Sciences Citation Index concerning this topic. We classified articles by the journal’s category, research topic, hospital setting, method and primary data analysis technique. Results showed that most of the studies were focussed on the analysis of technical and scale efficiency or on input / output ratio using Data Envelopment Analysis. We also find increasing interest concerning the effect of possible changes in hospital size on quality of care.ConclusionsStudies analysed in this review showed that economies of scale are present for merging hospitals. Results supported the current policy of expanding larger hospitals and restructuring/closing smaller hospitals. In terms of beds, studies reported consistent evidence of economies of scale for hospitals with 200–300 beds. Diseconomies of scale can be expected to occur below 200 beds and above 600 beds.

Follow the instructions provided in the companion repo to automatically download and decompress the archive. The following files are included:

Dataset (Model input, snow distribution and validation) for the precipitation scaling paper, which should be cited along with the data set citation. This data is useful for distributed hydrological modelling or other tasks that involve the study of snow distribution and precipitation in the high Alpine. The format of the data is for Alpine3D (models.slf.ch) model runs but other models could be used, too. Please cite: Vögeli, C., Lehning, M., Wever, N., Bavay M., 2016: Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution., Front. Earth Sci. 4: 108. doi: 10.3389/feart.2016.00108. Dataset is provided as a single zip file. The archive contains two directories, the valuable distributed snow depth maps for the landscape Davos and the simulation input. The archive also contains the file: "ReadMeMetadataDataSetPrecipitationScaling" which explains the data structure.

The study analyzes quantitative micro-level data aggregated to the city-level in urban systems in Europe and the United States. The study demonstrates how urban scaling laws arise from within-city inequality. We show that indicators of interconnectivity, productivity, and innovation have heavy tailed distributions in cities, and that city tails, and their growth with city size, play an important role in the emergence of urban scaling. With agent-based simulation and an analysis of longitudinal micro-level data, we identify a city-size dependent cumulative advantage mechanism behind differences in the tailedness of urban indicators by city size.

The data and code that support the findings of this study are available for download here. We collected the online networking data for Russia and Ukraine through the VKontakte API (https://vk.com/dev/openapi), the data on US patents are from the US Patent and Trademark Office (https://www.patentsview.org) and on research grants from Dimensions (https://www.dimensions.ai). The code for these data collections is available upon request. The Swedish micro-level data come from administrative and tax records and can therefore not be shared; access may be requested from Statistics Sweden (https://scb.se/en/services/guidance-for-researchers-and-universities). Additional information and data may be requested from the authors.

Repository of data, code, and analysis for manuscript titled "Scaling COVID-19 rates with population size in the United States".

This dataset provides performance data rate measurements a 3GPP-compliant service-based architecture platform that demonstrates the concept of cloud-native service orchestration and routing for a media vertical sector application. Cloud-native service orchestration and routing is a complete end-to-end approach that enables virtualisation and management of multiple layers in the OSI model, which provides considerable flexibility and control to achieve delivery of QoS to users in the face of varying demand, at reasonable cost.

Many morphological, physiological and ecological traits of trees scale with diameter, shaping the structure and function of forest ecosystems. Understanding the mechanistic basis for such scaling relationships is key to understanding forests globally and their role in Earth's changing climate system. Here, we evaluate theoretical predictions for the scaling of nine variables in a mixed-age temperate deciduous forest (CTFS-ForestGEO forest dynamics plot at the Smithsonian Conservation Biology Institute, Virginia, USA) and compare observed scaling parameters to those from other forests world-wide. We examine fifteen species and various environmental conditions. Structural, physiological and ecological traits of trees scaled with stem diameter in a manner that was sometimes consistent with existing theoretical predictions – more commonly with those predicting a range of scaling values than a single universal scaling value. Scaling relationships were variable among species, reflecting substantive ecological differences. Scaling relationships varied considerably with environmental conditions. For instance, the scaling of sap flux density varied with atmospheric moisture demand, and herbivore browsing dramatically influenced stem abundance scaling. Thus, stand-level, time-averaged scaling relationships (e.g., the scaling of diameter growth) are underlain by a diversity of species-level scaling relationships that can vary substantially with fluctuating environmental conditions. In order to use scaling theory to accurately characterize forest ecosystems and predict their responses to global change, it will be critical to develop a more nuanced understanding of both the forces that constrain stand-level scaling and the complexity of scaling variation across species and environmental conditions.

Invited talk given by Tim Evans (Imperial College London) at the EPSRC Workshop on "Scaling in Social Systems” held at the Saïd Business School, Oxford on 1st December 2011. Abstract:

The pattern of innovation seen through citations of academic papers has long fascinated academics. It has been known for at least fifty years that the data shows various long tailed distributions. In this talk I will look at some of the features of the data and show how to extract some simple universal patterns. I will discuss some of the implications of the results and some of the further questions it raises. •What is a citation? •What does an individual citation mean? •Is the data perfect? •Why citation count? •If not citation count, what else? •What does this data say about me? •Why h-index? •What is a self-citation? •How else can I use this data? •How will things change?

Tim S. Evans – Mini Biography Tim studied the mixture of quantum field theory and statistical physics in his PhD at Imperial College London. He was supervised by Prof. Ray Rivers who also supervised another speaker, Prof. Luis Bettencourt. Tim then spent time as a researcher at the University of Alberta in Edmonton Canada, before returning to research positions back here at Imperial, latterly as a Royal Society University Research Fellow. He was appointed to a lectureship at Imperial in 1997. Around 2003 he expanded his work on statistical physics to cover at problems in complexity, with a particular interest in network methods. This has included participation in an EU collaboration with social scientists on innovation, ―ISCOM, run in part by Prof. Geoff West (another speaker today). This fuelled his interest in social science applications and started an on going collaboration with an archaeologist.

Scaling patterns in plants have long interested biologists, particularly whether different species share similar patterns of growth, and whether differences in growth trajectories depend on plant size.  Using 8,794,737 measurements for 285 species from the U.S. Forest Inventory and Analysis database, we test several predictions emerging from a recently published “flow similarity†model for plant growth and allometry. We show that the model’s predicted curvature for intraspecific relationships between height, DBH and biomass is found in 88.1% of examined cases, and empirical slopes fall as predicted between the elastic similarity and flow similarity predictions in 71.1% of cases. We also find a strong size dependence in observed intraspecific allometric exponents, with large species, particularly gymnosperms, converging near the expectation for elastic similarity, and the central tendency among small species approaching the expectations for flow similarity in most cases. Our results supp..., The data from which these regression statistics were generated is publically available as cited in the manuscript., , There are two supplementary tables in support of our manuscript: SMA Regression Statistics Table and FIA BigTree Comparison Table

The data in the **SMA Regression Statistics **table are allometric regression statistics for 285 North American tree species contained within the USFS Forest Inventory and Analysis (FIA) database, which are all publicly available as cited in the manuscript. FIA records tree DBH, Height and estimates Biomass for all tree species greater than 5 in DBH in their plots. For details on FIA methods see references cited in the manuscript accompanying this dataset. See manuscript for full description.

For the SMA Regression Statistics Table, column headers are as follows:

Common Name - the common name for that species recorded in FIA database

Genus - The genus

Species - The species

AngiospermGymnosperm - 1 if gymnosperm, 0 if angiosperm

SpeciesCode - the FIA species code

HvsDslope - SMA regression slope for the height vs dia...

Abstract

This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. The metadata was not provided by the data supplier and has been compiled by the programme based on known details.

The scaling factors used as input to SEACI (South-eastern Australia climate initiative) future hydroclimate projections.

Dataset History

See report: Post, David; Chiew, Francis; Hendon, Harry; Timbal, Bertrand (2012): South Eastern Australian Climate Initiative future hydroclimate projections. v2. CSIRO. Data Collection. http://doi.org/10.4225/08/5702FC8181B52

Dataset Citation

CSIRO (2016) SEACI GCM scaling. Bioregional Assessment Source Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/bde1e7b7-5bb3-483c-8616-c4176fde8818.

Data publication with data to reproduce all figures in ''Scaling functions of the three-dimensional Z(2), O(2) and O(4) models and their finite size dependence in an external field", arXiv:2304.01710.

Dataset for Rasch analyses of the brief Critical Thinking Scale (CTh) and assessment of change Data from the study reported in ” Exploring first semester changes in domain-specific critical thinking”. Data are from Danish Psychology students, and consists of three data sets containing the variables described below. Baseline data set (n = 336) Gender: 1 = female, 2 = male Agegroup (median split): 1 = 21 years and younger, 2 = 22 years and older Math (perception of own mathematical knowledge as adequate): 1 = inadequate, 2 = adequate Statfuture (expectation to need statistics in future employment): 1 = yes, 2 = maybe, 3 = no CTh1, CTh2, CTh5 are items of the CTh scale with response scale (item statements included in the article): 1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = always RescaledWML (rescaled person parameter estimates at baseline) Matched longitunial data set dataset (n = 165) Gender, Agegroup, Math and Statfuture were collected at baseline Gender: 1 = female, 2 = male Agegroup (median split): 1 = 21 years and younger, 2 = 22 years and older Math (perception of own mathematical knowledge as adequate): 1 = inadequate, 2 = adequate Statfuture (expectation to need statistics in future employment): 1 = yes, 2 = maybe, 3 = no CTh1b, CTh2b, CTh5b are items of the CTh scale at baseline with response scale (item statements included in the article): 1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = always CTh1f, CTh2f, CTh5f are items of the CTh scale at follow-up with response scale (item statements included in the article): 1 = never, 2 = rarely, 3 = sometimes, 4 = often, 5 = always RescaledWMLf (rescaled person parameter estimates at follow-up)

Supplementary raw data, R scripts, and results underpinning analyses of geometric morphometric and linear data derived from scale insects; includes source code for all analyses, raw data for ostiole, leg, and body shape analyses, and alpha tables for body size analysis and ostioles analyses. Abbreviations are defined in the R script file. See README for list of resources.

The MNIST Large Scale dataset is based on the classic MNIST dataset, but contains large scale variations up to a factor of 16. The motivation behind creating this dataset was to enable testing the ability of different algorithms to learn in the presence of large scale variability and specifically the ability to generalise to new scales not present in the training set over wide scale ranges.

The dataset contains training data for each one of the relative size factors 1, 2 and 4 relative to the original MNIST dataset and testing data for relative scaling factors between 1/2 and 8, with a ratio of $\sqrt[4]{2}$ between adjacent scales.

Scales are collected annually from smolt trapping operations in Maine as wellas other sampling opportunities (e.g. marine surveys, fishery sampling etc.). Scale samples are imaged and age, origin, and measurement data are collected as needed for specific growth-related research.

The natural amenities scale is a measure of the physical characteristics of a county area that enhance the location as a place to live. The scale was constructed by combining six measures of climate, topography, and water area that reflect environmental qualities most people prefer. These measures are warm winter, winter sun, temperate summer, low summer humidity, topographic variation, and water area. The data are available for counties in the lower 48 States. The file contains the original measures and standardized scores for each county as well as the amenities scale.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Data file For complete information, please visit https://data.gov.

Scaling properties of language are a useful tool for understanding generative processes in texts. We investigate the scaling relations in citywise Twitter corpora coming from the Metropolitan and Micropolitan Statstical Areas of the United States. We observe a slightly superlinear urban scaling with the city population for the total volume of the tweets and words created in a city. We then find that a certain core vocabulary follows the scaling relationship of that of the bulk text, but most words are sensitive to city size, exhibiting a super- or a sublinear urban scaling. For both regimes we can offer a plausible explanation based on the meaning of the words. We also show that the parameters for Zipf's law and Heaps law differ on Twitter from that of other texts, and that the exponent of Zipf's law changes with city size.

Source data assessment of statistical capacity (scale 0 - 100) in Argentina was reported at 90 in 2020, according to the World Bank collection of development indicators, compiled from officially recognized sources. Argentina - Source data assessment of statistical capacity (scale 0 - 100) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.