Global Data Pooling Services Market is segmented by Application (Healthcare_Finance_Retail_Telecom_Research), Type (Shared Data Platforms_Data Exchange Networks_Data Lakes_Data Clean Rooms_Federated Analytics), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

Objective To consider the problem of the calculation of number needed to treat (NNT) derived from risk difference, odds ratio, and raw pooled events shown to give different results using data from a review of nursing interventions for smoking cessation. Discussion A review of nursing interventions for smoking cessation from the Cochrane Library provided different values for NNT depending on how NNTs were calculated. The Cochrane review was evaluated for clinical heterogeneity using L'Abbé plot and subsequent analysis by secondary and primary care settings. Three studies in primary care had low (4%) baseline quit rates, and nursing interventions were without effect. Seven trials in hospital settings with patients after cardiac surgery, or heart attack, or even with cancer, had high baseline quit rates (25%). Nursing intervention to stop smoking in the hospital setting was effective, with an NNT of 14 (95% confidence interval 9 to 26). The assumptions involved in using risk difference and odds ratio scales for calculating NNTs are discussed. Summary Clinical common sense and concentration on raw data helps to detect clinical heterogeneity. Once robust statistical tests have told us that an intervention works, we then need to know how well it works. The number needed to treat or harm is just one way of showing that, and when used sensibly can be a useful tool.

Background The "integrated safety report" of the drug registration files submitted to health authorities usually summarizes the rates of adverse events observed for a new drug, placebo or active control drugs by pooling the safety data across the trials. Pooling consists of adding the numbers of events observed in a given treatment group across the trials and dividing the results by the total number of patients included in this group. Because it considers treatment groups rather than studies, pooling ignores validity of the comparisons and is subject to a particular kind of bias, termed "Simpson's paradox." In contrast, meta-analysis and other stratified analyses are less susceptible to bias. Methods We use a hypothetical, but not atypical, application to demonstrate that the results of a meta-analysis can differ greatly from those obtained by pooling the same data. In our hypothetical model, a new drug is compared to 1) a placebo in 4 relatively small trials in patients at high risk for a certain adverse event and 2) an active reference drug in 2 larger trials of patients at low risk for this event. Results Using meta-analysis, the relative risk of experiencing the adverse event with the new drug was 1.78 (95% confidence interval [1.02; 3.12]) compared to placebo and 2.20 [0.76; 6.32] compared to active control. By pooling the data, the results were, respectively, 1.00 [0.59; 1.70] and 5.20 [2.07; 13.08]. Conclusions Because these findings could mislead health authorities and doctors, regulatory agencies should require meta-analyses or stratified analyses of safety data in drug registration files.

Note:1no suramin pre-treatment;2body mass index,3cerebrospinal fluid,4white blood cell.

Distractors and responses are integrated in an event file when they occur together. Further, when all or some features repeat, the whole event file is retrieved, affecting later action as observed in so-called binding effects. Previous research used varying distractor pool sizes (ranging from just two to well over 30) to choose distractors from, but it is unclear whether distractor pool size has an effect on the size of distractor-based binding effects. The present study investigates, if and how distractor pool size modulates binding effects. Using an adapted prime-probe design, participants were assigned to large (384 distractors) or small (2 distractors) distractor pool sizes, and distractor-response binding effects were measured. Binding effects were stronger for the large distractor pool condition compared to the small pool condition. We discuss these findings against the background of the negative priming literature and research on novelty.

datajuicer/data-juicer-t2v-evolution-data-pool dataset hosted on Hugging Face and contributed by the HF Datasets community

Animals were incorporated into pools in different proportions to estimate error and evaluate factors influencing error. Animals were incorporated into 2 types of pools, sub-pools and super pools. Within phenotype, liver abscess or normal, 16 animals were combined into 4 sub-pools, 4 animals per sub-pool in parts of 1:2:3:4. Sub-pools were constructed based on crushed frozen liver tissue mass. Within phenotype, 4 sub-pools were incorporated into 2 super pools in parts of 1:2:3:4 for super pool 1 and 3:4:1:2 for super pool 2. Super pools were made based on DNA quantity. Errors in DNA quantification would create error in forming super pools from sub-pools and variation in cell content or DNA content of liver tissue would result in error in combining sub-pools from animals. Animal contributions to sub-pools for livers with abscess sub-pool 1A was 1:2:3:4 parts of 15A, 36A, 35A, and 23A. sub-pool 2A was 1:2:3:4 parts of 42A, 37A, 12A, and 22A. sub-pool 3A was 1:2:3:4 parts of 17A, 1A, 49A, and 48A . sub-pool 4A was 1:2:3:4 parts of 3A, 20A, 16A, and 13A. Each part was 0.1 g of pulverized frozen liver tissue. Animal contributions to livers without abscess sub-pool 1N was 1:2:3:4 parts of 46N, 23N, 17N, and 12N. sub-pool 2N was 1:2:3:4 parts of 1N, 31N, 6N, and 48N. sub-pool 3N was 1:2:3:4 parts of 36N, 43N, 32N, and 13N. sub-pool 4N was 1:2:3:4 parts of 34N, 19N, 41N, and 50N. Sub-pool contributions to super pools for livers with abscess super pool 1A was:1:2:3:4 parts sub-pool 1A, sub-pool 2A, sub-pool 3A, and sub-pool 4A. super pool 2A was 3:4:1:2 parts sub-pool 1A, sub-pool 2A, sub-pool 3A, and sub-pool 4A. Sub-pool contributions to super pools for livers with without abscess super pool 1N was:1:2:3:4 parts sub-pool 1N, sub-pool 2N, sub-pool 3N, and sub-pool 4N. super pool 2N was 3:4:1:2 parts sub-pool 1N, sub-pool 2N, sub-pool 3N, and sub-pool 4N. Funded by the USDA Agricultural Research Service, Developing a Systems Biology Approach to Enhance Efficiency and Sustainability of Beef and Lamb Production/ 3040-31000-100-000-D Resources in this dataset:Resource Title: xy data for individual animals. File Name: xyIndividuals.csv.gzResource Description: X (red) and Y (green) intensity data for 32 animals. There are 64 columns, an X and Y column for each animalResource Title: Genotypes, Number of copies of B allele for BovineHD 770K. File Name: g.csv.gzResource Description: Values are 0, 1 and 2 for 32 animals and 777,962 SNP, DNA was extracted from pulverized frozen liver tissueResource Title: x and y data for pools. File Name: xyPools.csv.gzResource Description: X (red) and Y (green) intensity for 12 pools. There are 2 columns per pool, first is X followed by Y. First 8 columns are super pools and second 16 are sub-pools. Examples superPool.1A.X is superPool 1 for abscess livers and X intensity sub-pool.1A.Y is sub-pools 1 for abscess livers and Y intensity

Using data from an experiment conducted in 70 Colombian communities, we investigate who pools risk with whom when trust is crucial for enforcing risk pooling arrangements. We explore the roles played by risk attitudes and social networks. Both empirically and theoretically, we find that close friends and relatives group assortatively on risk attitudes and are more likely to join the same risk pooling group, while unfamiliar participants group less and rarely assort. These findings indicate that where there are advantages to grouping assortatively on risk attitudes those advantages may be inaccessible when trust is absent or low. (JEL C93, O12, O18, Z13)

The dataset used in this paper is not explicitly described, but it is mentioned that the authors examined the trend away from max pooling in newer architectures.

Standardized do files to facilitate within- and across-country data pooling and analysis

Data-Juicer Sandbox: A Comprehensive Suite for Multimodal Data-Model Co-development

  Project description

The emergence of large-scale multi-modal generative models has drastically advanced artificial intelligence, introducing unprecedented levels of performance and functionality. However, optimizing these models remains challenging due to historically isolated paths of model-centric and data-centric developments, leading to suboptimal outcomes and inefficient resource… See the full description on the dataset page: https://huggingface.co/datasets/datajuicer/data-juicer-t2v-optimal-data-pool.

Risk management is a problem humans have faced throughout history and across societies. One way to manage risk is to transfer it to other parties through formal and informal insurance systems. One informal method of self-insurance is limited risk pooling, where individuals can ask for help only when in need. Models suggest that need-based transfer systems may require coordination and common knowledge to be effective. To explore the impact of common knowledge on social coordination and risk pooling in volatile environments, we designed and ran a Risk Pooling Game. We compared participants who played the game with no advance priming or framing to participants who read one of two texts describing real-world systems of risk pooling. Players in the primed games engaged in more repetitive asking and repetitive giving than those in the control games. Players in the primed games also gave more in response to requests and were more likely to respond positively to requests than players in the control games. In addition, players in the primed games were more tolerant of wide differences between what the two players gave and received. These results suggest that the priming texts led players to pay less attention to debt and repayment and more attention to the survival of the other player, and thus to more risk pooling. These results are consistent with findings from fieldwork in small-scale societies that suggest that humans use need-based transfer systems to pool risk when environmental volatility leads to needs with unpredictable timing. Models suggest that the need-based transfer strategy observed in this experiment can outperform debt-based strategies. The results of the present study suggest that the suite of behaviors associated with need-based transfers is an easily triggered part of the human behavioral repertoire.

This dataset include the raw data of our experience after building a bespoke laboratory to control critical areas by pooling samples of saliva. From August 2020 to February 2022; 928,528 individual self-samples of saliva were processed in 52,580 pools, 4,935 of which were positive and helped us detect 5,806 nonsymptomatic individuals.

The dataset was collected directly from the laboratory information system and was processed to delete the columns with sensitive information.

The Dataset was charged as XLSX file and only need a compatible software.

To facilitate the understanding of the data set, which uses the Galician language and an internal nomenclature of our laboratory, we include a sheet named "description" and a README file in which it defines the terms used and their equivalence with those used in the article.

This repository contains modified TNC trip data obtained from the Chicago data portal for the year 2019. The raw trip data is first cleaned by removing trivial and erroneous records. This includes short trips with travel times of less than 2 minutes or distances shorter than 0.1 miles. We also exclude entries with missing pickup or dropoff census tract i.e trips originating or ending outside Chicago. Lastly, we remove trips marked as not authorized as shared trips but coded as shared trips. The filtered data is then aggregated by pickup_hour, 'pickup_date, pickup_day, and pickup_month. We also aggregate by census tracts in addition to the earlier ones for detour plots.

Get the latest USA Pool Coping import data with importer names, shipment details, buyers list, product description, price, quantity, and major US ports.

Video recognition aims to automatically analyze the contents of videos (e.g., events and actions), and has a wide range of applications, including intelligent surveillance, multimedia retrieval and recommendation.

Attendance records for NYC Parks outdoor swimming pools

As of 2022, outdoor pool attendance can be found here: https://data.cityofnewyork.us/Recreation/Outdoor-Pools-Session-Information/82jf-bykm/about_data

Supplementary Material 3.

2D swimming pool outlines derived from Perth metropolitan imagery using a deep learning algorithm. This includes a classification for the type of pool as Below Ground, Above Ground or Not Pool (indicates a potential false positive). The algorithm was created in-house within Landgate to demonstrate to approved State and Local Government (Government) entities the capabilities of Deep Learning [DL] and Artificial Intelligence [AI] algorithms. Show full description

Feature layer containing Pool information in the City of Sioux Falls, South Dakota.