The Healthcare Data Aggregation Services market has emerged as a crucial component in the evolving landscape of healthcare management, driven by the increasing volume of health-related data generated daily. Healthcare data aggregation involves the collection, integration, and analysis of disparate data sources to pr

Health data and environmental data are commonly collected at different levels of aggregation. A persistent challenge of using a spatial regression model to link these data is that their associations can vary as a function of aggregation. This results into ecological fallacy if association at one aggregation level is used for inferencing at another level. We address this challenge by presenting a hierarchically adaptable spatial regression model. In essence, the model extends the spatially varying coefficient model to allow the response to be count data at larger aggregation levels than that of the covariates. A Bayesian hierarchical approach is used for inferencing the model parameters. Robust inference and optimal prediction over geographical space and at different spatial aggregation levels are studied by simulated data sets. The spatial associations at different spatial supports are largely different, but can be efficiently inferred when prior knowledge of the associations is available. The model is applied to study hand, foot and mouth disease (HFMD) in Da Nang city, Viet Nam. Decrease in vegetated areas corresponds with elevated HFMD risks. A study to the identifiability of the parameters shows a strong need for a highly informative prior distribution. We conclude that the model is robust to the underlying aggregation levels of the calibrating data for association inference and it is ready for application in health geography.

Health data and environmental data are commonly collected at different levels of aggregation. A persistent challenge of using a spatial regression model to link these data is that their associations can vary as a function of aggregation. This results into ecological fallacy if association at one aggregation level is used for inferencing at another level. We address this challenge by presenting a hierarchically adaptable spatial regression model. In essence, the model extends the spatially varying coefficient model to allow the response to be count data at larger aggregation levels than that of the covariates. A Bayesian hierarchical approach is used for inferencing the model parameters. Robust inference and optimal prediction over geographical space and at different spatial aggregation levels are studied by simulated data sets. The spatial associations at different spatial supports are largely different, but can be efficiently inferred when prior knowledge of the associations is available. The model is applied to study hand, foot and mouth disease (HFMD) in Da Nang city, Viet Nam. Decrease in vegetated areas corresponds with elevated HFMD risks. A study to the identifiability of the parameters shows a strong need for a highly informative prior distribution. We conclude that the model is robust to the underlying aggregation levels of the calibrating data for association inference and it is ready for application in health geography.

Pearson correlation analysis across different aggregators and their Facebook counts.

Objective(s): The 2024 Pediatric Sepsis Data Challenge provides an opportunity to address the lack of appropriate mortality prediction models for LMICs. For this challenge, we are asking participants to develop a working, open-source algorithm to predict in-hospital mortality and length of stay using only the provided synthetic dataset. The original data used to generate the real-world data (RWD) informed synthetic training set available to participants was obtained from a prospective, multisite, observational cohort study of children with suspected sepsis aged 6 months to 60 months at the time of admission to hospitals in Uganda. For this challenge, we have created a RWD-informed synthetically generated training data set to reduce the risk of re-identification in this highly vulnerable population. The synthetic training set was generated from a random subset of the original data (full dataset A) of 2686 records (70% of the total dataset - training dataset B). All challenge solutions will be evaluated against the remaining 1235 records (30% of the total dataset - test dataset C). Data Description: Report describing the comparison of univariate and bivariate distributions between the Synthetic Dataset and Test Dataset C. Additionally, a report showing the maximum mean discrepancy (MMD) and Kullback–Leibler (KL) divergence statistics. Data dictionary for the synthetic training dataset containing 148 variables. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

Analysis of (dis)agreement among aggregators in Twitter counts (re)tweets, and distinct tweeters.

This deposit contains all raw data and analysis from the paper "In-cell titration of small solutes controls protein stability and aggregation". Data is collected into several types: 1) analysis*.tar.gz are the analysis scripts and the resulting data for each cell. The numbers correspond to the numbers shown in Fig.S1. (in publication) 2) scripts.tar.gz contains helper scripts to create the dataset in bash format. 3) input.tar.gz contains headers and other information that is fed into bash scripts to create the dataset. 4) All rawData*.tar.gz are tarballs of the data of cells in different solutes in .mat files readable by matlab, as follows: - Each experiment included in the publication is represented by two matlab files: (1) a calibration jump under amber illumination (_calib.mat suffix) (2) a full jump under blue illumination (FRET data) - Each file contains the following fields: coordleft - coordinates of cropped and aligned acceptor channel on the original image coordright - coordinates of cropped and aligned donor channel on the original image] dataleft - a 3d 12-bit integer matrix containing acceptor channel flourescence for each pixel and time step. Not available in _calib files dataright - a 3d 12-bit integer matrix containing donor channel flourescence for each pixel and time step. This will be mCherry in _calib files and AcGFP in data files. frame1 - original image size imgstd - cropped dimensions numFrames - number of frames in dataleft and dataright videos - a structure file containing camera data. Specifically, videos.TimeStamp includes the time from each frame.

Objective(s): Momentum for open access to research is growing. Funding agencies and publishers are increasingly requiring researchers make their data and research outputs open and publicly available. However, clinical researchers struggle to find real-world examples of Open Data sharing. The aim of this 1 hr virtual workshop is to provide real-world examples of Open Data sharing for both qualitative and quantitative data. Specifically, participants will learn: 1. Primary challenges and successes when sharing quantitative and qualitative clinical research data. 2. Platforms available for open data sharing. 3. Ways to troubleshoot data sharing and publish from open data. Workshop Agenda: 1. “Data sharing during the COVID-19 pandemic” - Speaker: Srinivas Murthy, Clinical Associate Professor, Department of Pediatrics, Faculty of Medicine, University of British Columbia. Investigator, BC Children's Hospital 2. “Our experience with Open Data for the 'Integrating a neonatal healthcare package for Malawi' project.” - Speaker: Maggie Woo Kinshella, Global Health Research Coordinator, Department of Obstetrics and Gynaecology, BC Children’s and Women’s Hospital and University of British Columbia This workshop draws on work supported by the Digital Research Alliance of Canada. Data Description: Presentation slides, Workshop Video, and Workshop Communication Srinivas Murthy: Data sharing during the COVID-19 pandemic presentation and accompanying PowerPoint slides. Maggie Woo Kinshella: Our experience with Open Data for the 'Integrating a neonatal healthcare package for Malawi' project presentation and accompanying Powerpoint slides. This workshop was developed as part of Dr. Ansermino's Data Champions Pilot Project supported by the Digital Research Alliance of Canada., NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on this page under "collaborate with the pediatric sepsis colab."

Symbionts tend to be aggregated on their hosts, such that few hosts harbor the majority of symbionts. This ubiquitous pattern can result from stochastic processes, but aggregation patterns may also depend on the type of host-symbiont interaction, plus traits that affect host exposure and susceptibility to symbionts. Untangling how aggregation patterns both within and among populations depend on stochastic processes, interaction type and host traits remains an outstanding challenge. Here, we address this challenge by using null models to compare aggregation patterns in a neutral system of Balanomorpha barnacles attached to patellid limpets and a host-parasite system of Trinidadian guppies (Poecilia reticulata) and their Gyrodactylus spp. monogeneans. We first used a model to predict patterns of symbiont-host aggregation due to random partitioning of symbionts to hosts. This null model accurately predicted the aggregation of barnacles on limpets, but the degree of aggregation varied across 303 quadrats. Quadrats with larger limpets had less aggregated barnacles, whereas aggregation increased with variation in limpet size. Across 84 guppy populations, Gyrodactylus spp. parasites were significantly less aggregated than predicted by the null model. As in the neutral limpet-barnacle system, aggregation decreased with mean host size. Parasites were also significantly less aggregated on males than females because male guppies tended to have higher prevalence and lower parasite burdens than predicted by the null model. Together, these results suggest stochastic processes can explain aggregation patterns in neutral but not parasitic systems, though in both systems host traits affect aggregation patterns. Because the distribution of symbionts on hosts can affect symbiont evolution via intraspecific interactions, and reciprocally host behavior and evolution via host-symbiont interactions, identifying the drivers of aggregation enriches our understanding of host-symbiont interactions.

Phylogenetic regression is frequently utilized in macroevolutionary studies, and its statistical properties have been thoroughly investigated. By contrast, phylogenetic ANOVA has received relatively less attention, and the conditions leading to incorrect statistical and biological inferences when comparing multivariate phenotypes among groups remains under-explored. Here we propose a refined method of randomizing residuals in a permutation procedure (RRPP) for evaluating phenotypic differences among groups while conditioning the data on the phylogeny. We show that RRPP displays appropriate statistical properties for both phylogenetic ANOVA and regression models, and for univariate and multivariate datasets. For ANOVA, we find that RRPP exhibits higher statistical power than methods utilizing phylogenetic simulation. Additionally, we investigate how group dispersion across the phylogeny affects inferences, and reveal that highly aggregated groups generate strong and significant correlations with the phylogeny, which reduce statistical power and subsequently affect biological interpretations. We discuss the broader implications of this phylogenetic group aggregation, and its relation to challenges encountered with other comparative methods where one or a few transitions in discrete traits are observed on the phylogeny. Finally, we recommend that phylogenetic comparative studies of continuous trait data utilize RRPP for assessing the significance of indicator variables as sources of trait variation.

KPLEX is funded under the European Commission’s Horizon 2020 research programme to undertake a 15-month investigation of the ways in which a focus on ‘big data’ in ICT research elides important issues about the information environment that we live in. While the phrase may sound inclusive and integrative, in fact, ‘big data’ approaches are highly selective, excluding any input that cannot be effectively structured, represented, or, indeed, digitised.Data of this messy, dirty sort is precisely the kind that humanities and cultural researchers deal with best. It will therefore be the contribution of the KPLEX project to investigate these elements of humanities and cultural data, and the strategies researchers have developed to deal with them. In doing so it will remain at the margins of ICT so as to better shed light on the gap between analogue or augmented digital practices and fully computational ones. As such, it will expand our awareness of the risks inherent in big data and to suggest ways in which phenomena that resist datafication can still be represented (if only by their absence) in knowledge creation approaches reliant upon the interrogation of large data corpora.KPLEX approaches this challenge in a comparative, multidisciplinary and multisectoral fashion, focusing on 3 key challenges to the knowledge creation capacity of big data approaches: the manner in which data that are not digitised or shared become ‘hidden’ from aggregation systems; the fact that data is human created, and lacks the objectivity often ascribed to the term; the subtle ways in which data that are complex almost always become simplified before they can be aggregated. It will approach these questions via a humanities research perspective, but using social science research tools to look at both the humanistic and computer science approaches to the term ‘data’ and its many possible meanings and implications.As such, KPLEX project defines and describes key aspects of data that are at risk of being left out of our knowledge creation processes in a system where large scale data aggregation is becoming ever more the gold standard.

Analysis of (dis)agreement among aggregators in Wikipedia counts.

Background: The reduction in the deaths of millions of children who die from infectious diseases requires early initiation of treatment and improved access to care available in health facilities. A major challenge is the lack of objective evidence to guide front line health workers in the community to recognize critical illness in children earlier in their course. Methods: We undertook a prospective observational study of children less than 5 years of age presenting at the outpatient or emergency department of a rural tertiary care hospital between October 2012 and April 2013. Study physicians collected clinical signs and symptoms from the facility records, and with a mobile application performed recordings of oxygen saturation, heart rate and respiratory rate. Facility physicians decided the need for hospital admission without knowledge of the oxygen saturation. Multiple logistic predictive models were tested. Findings: Twenty-five percent of the 3374 assessed children, with a median (interquartile range) age of 1.02 (0.42–2.24), were admitted to hospital. We were unable to contact 20% of subjects after their visit. A logistic regression model using continuous oxygen saturation, respiratory rate, temperature and age combined with dichotomous signs of chest indrawing, lethargy, irritability and symptoms of cough, diarrhea and fast or difficult breathing predicted admission to hospital with an area under the receiver operating characteristic curve of 0.89 (95% confidence interval -CI: 0.87 to 0.90). At a risk threshold of 25% for admission, the sensitivity was 77% (95% CI: 74% to 80%), specificity was 87% (95% CI: 86% to 88%), positive predictive value was 70% (95% CI: 67% to 73%) and negative predictive value was 91% (95% CI: 90% to 92%). Conclusion: A model using oxygen saturation, respiratory rate and temperature in combination with readily obtained clinical signs and symptoms predicted the need for hospitalization of critically ill children. External validation of this model in a community setting will be required before adoption into clinical practice. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

Objective(s): Momentum for open access to research is growing. Funding agencies and publishers are increasingly requiring researchers make their data and research outputs open and publicly available. However, this introduces many challenges, especially when managing confidential clinical data. The aim of this 1 hr virtual workshop is to provide participants with knowledge about what synthetic data is, methods to create synthetic data, and the 2023 Pediatric Sepsis Data Challenge. Workshop Agenda: 1. Introduction - Speaker: Mark Ansermino, Director, Centre for International Child Health 2. "Leveraging Synthetic Data for an International Data Challenge" - Speaker: Charly Huxford, Research Assistant, Centre for International Child Health 3. "Methods in Synthetic Data Generation." - Speaker: Vuong Nguyen, Biostatistician, Centre for International Child Health and The HIPpy Lab This workshop draws on work supported by the Digital Research Alliance of Canada. Data Description: Presentation slides, Workshop Video, and Workshop Communication Charly Huxford: Leveraging Synthetic Data for an International Data Challenge presentation and accompanying PowerPoint slides. Vuong Nguyen: Methods in Synthetic Data Generation presentation and accompanying Powerpoint slides. This workshop was developed as part of Dr. Ansermino's Data Champions Pilot Project supported by the Digital Research Alliance of Canada., NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on this page under "collaborate with the pediatric sepsis colab."

The global news aggregator market is experiencing robust growth, driven by increasing demand for personalized and efficient news consumption. The market size in 2025 is estimated at $15 billion, with a projected Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors. The proliferation of smartphones and mobile internet access has made it easier than ever for users to access news from various sources on the go. Furthermore, the increasing reliance on algorithms and artificial intelligence to curate and personalize news feeds contributes to enhanced user experience and engagement. The market is segmented by news type (local, regional, international) and application (enterprise, personal), with the personal segment dominating due to the widespread adoption of news aggregator apps by individual users. Competition is intense, with a wide range of established players and emerging startups vying for market share. Challenges include maintaining data accuracy, combating the spread of misinformation, and ensuring data privacy. The market is also subject to fluctuations based on geopolitical events and shifts in consumer behavior. The regional landscape shows significant variations in market penetration. North America currently holds a leading position due to high internet penetration and a technologically advanced population. However, rapid growth is anticipated in Asia-Pacific regions like India and China, driven by rising smartphone adoption and increasing internet access. Europe also presents a substantial market, with significant variations in growth across individual countries. The continued development of sophisticated algorithms, personalized news feeds, and enhanced user interfaces will be critical for sustained growth. The market will continue to evolve, adapting to changing user preferences and technological advancements, presenting both opportunities and challenges for market participants. The future likely involves more integration with social media, the emergence of niche news aggregation platforms, and a growing emphasis on ethical and responsible news curation.

Soluble oligomers of Aβ-1-42 are widely recognized as crucial targets for the design of inhibitors for potential therapeutic intervention against Alzheimer´s disease. However, the intrinsically disordered character of this polypeptide poses serious difficulties for the experimental determination of conserved structural motifs. Indeed, initial aggregation steps are extremely challenging for state-of-the-art experimental techniques. Although molecular dynamics simulations harbor the potential to capture such initial association events, unbiased exploration of the conformational landscape available to unstructured dimers implies a significant computational cost. Here, we provide a dataset of configurations of Aβ1-42 dimers obtained by coarse-grained molecular dynamics (MD) simulations using the SIRAH force field. Trajectories are provided in standard gromacs format and can be easily converted to fully atomistic representations for visualization and analysis using molecular visualization/analysis software. The dataset contains MD trajectories of Aβ1-42 that undergo spontaneous and unbiased dimerization. We provide the time series of 25 replicates simulated for 10 microseconds under room conditions and physiological salt concentration. These multiple aggregation events provide valuable information not only on new binding pockets formed by the dimeric interface but also monomeric hot spots that can be targeted by small molecules on high-throughput docking campaigns. Alternatively, Aβ1-42 dimers could be used as aggregation seeds in studies of Aβ secondary nucleation

The Open Banking Platform market is experiencing robust growth, driven by increasing consumer demand for greater control over their financial data, the rise of fintech innovation, and regulatory mandates promoting open banking initiatives globally. The market, estimated at $10 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $45 billion by 2033. This significant expansion is fueled by several key factors. Firstly, the proliferation of APIs and enhanced data security measures are enabling seamless integration between financial institutions and third-party providers, fostering a more competitive and customer-centric financial ecosystem. Secondly, the emergence of innovative financial products and services, such as personalized financial advice, streamlined loan applications, and enhanced fraud detection, are directly attributable to the capabilities unlocked by open banking. Finally, the ongoing expansion of open banking regulations in key markets worldwide is further accelerating market adoption and propelling growth. However, despite the considerable growth potential, the market faces certain challenges. Data privacy and security concerns remain paramount, requiring robust security protocols and transparent data handling practices to maintain consumer trust. Furthermore, the integration complexities involved in connecting diverse financial systems across different institutions can pose a significant hurdle for smaller players and startups. Nevertheless, ongoing technological advancements in areas such as AI and machine learning are continuously improving data processing, security, and interoperability, mitigating some of these challenges. The market is segmented by various factors including deployment type (cloud, on-premise), application type (personal finance management, payment initiation, account aggregation), and geography. Key players such as Plaid, Tink, Yodlee, and others are actively shaping the market landscape through strategic partnerships, technological innovations, and market expansion efforts.

Field studies of social behaviour are challenging due to the need to record or infer interactions between multiple individuals, often under suboptimal environmental conditions or with potential disturbance by observers. Due to the limited field techniques available, we present a novel method to quantify social behaviours in the field by comparing the counts of individuals caught in traps across multiple locations sampled simultaneously. The distribution of individuals between traps gives the extent of aggregation, and phenotypic data allow for inference of non-random assortment. As a case study, we applied this method to populations of three-spined sticklebacks (Gasterosteus aculeatus) in freshwater ponds, using minnow traps. As expected, we observed a strong trend for aggregation. We were able to describe the ecological drivers of aggregation, comparing environmental and phenotypic conditions across sites. Aggregation was not related to environmental parameters, but was negatively asso..., , , # Quantifying animal social behaviour with ecological field methods

https://doi.org/10.5061/dryad.g79cnp5zw

Description of the data and file structure

Overview

This project introduces a novel method to quantify animal social behaviour by comparing counts of individuals caught in traps across multiple locations sampled simultaneously. The method was applied to three-spined sticklebacks (Gasterosteus aculeatus) in freshwater ponds using minnow traps.

Dataset

The dataset includes the number of fish caught per trap location including the number of males in breeding condition (red males), aggregation scores calculated using the index of dispersion, measurements of fish body length, and various environmental parameters. Data was collected from 4 pond sites over 13 weeks from May to November 2021. The associated script performs statistical analyses to explore the relationships between these variables.

Key Analyses

1. **Aggrega...

Overview of the main methods of collecting, tracking, and updating metrics across different altmetric data aggregators—As reported by the data aggregators.

The Broadband Access Aggregation Service market has emerged as a critical component in the telecommunications landscape, revolutionizing how internet connectivity and data services are delivered to households and businesses. This service acts as a bridge, aggregating various broadband access technologies-such as DSL