Dataset provided by = Björn Holzhauer

Dataset Description==Meta-analyses of clinical trials often treat the number of patients experiencing a medical event as binomially distributed when individual patient data for fitting standard time-to-event models are unavailable. Assuming identical drop-out time distributions across arms, random censorship and low proportions of patients with an event, a binomial approach results in a valid test of the null hypothesis of no treatment effect with minimal loss in efficiency compared to time-to-event methods. To deal with differences in follow-up - at the cost of assuming specific distributions for event and drop-out times - we propose a hierarchical multivariate meta-analysis model using the aggregate data likelihood based on the number of cases, fatal cases and discontinuations in each group, as well as the planned trial duration and groups sizes. Such a model also enables exchangeability assumptions about parameters of survival distributions, for which they are more appropriate than for the expected proportion of patients with an event across trials of substantially different length. Borrowing information from other trials within a meta-analysis or from historical data is particularly useful for rare events data. Prior information or exchangeability assumptions also avoid the parameter identifiability problems that arise when using more flexible event and drop-out time distributions than the exponential one. We discuss the derivation of robust historical priors and illustrate the discussed methods using an example. We also compare the proposed approach against other aggregate data meta-analysis methods in a simulation study.

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This product contains plot location data in a .shp format as well as annual land cover, land use, and change process variables for each reference data plot in a separate .csv table. The same information available in the.csv file is also provided in a .xlsx format. The LCMAP Reference Data Product was utilized for evaluation and validation of the Land Change Monitoring, Assessment, and Projection (LCMAP) land cover and land cover change products. The LCMAP Reference Data Product includes the collection of an independent dataset of 25,000 randomly-distributed 30-meter by 30-meter plots across the conterminous United States (CONUS). This dataset was collected via manual image interpretation to aid in validation of the land cover and land cover change products as well as area estimates. The LCMAP Reference Data Product collected variables related to primary and secondary land use, primary and secondary land cover(s), change processes, and other ancillary variables annually across CONUS from 1984-2018. First posted - May 1, 2020 (available from author) Revised - September 21, 2021 (version 1.1) Revised - November 17, 2021 (version 1.2)

Integrated Urgent Care (IUC) describes a range of services including NHS 111 and Out of Hours services, which aim to ensure a seamless patient experience with minimum handoffs and access to a clinician where required.

Blockchain data query: Aggregate Data - Lending Markets Stablecoins

The Shared Savings Program County-level Aggregate Expenditure and Risk Score Data on Assignable Beneficiaries Public Use File (PUF) for the Medicare Shared Savings Program (Shared Savings Program) provides aggregate data consisting of per capita Parts A and B FFS expenditures, average CMS-HCC prospective risk scores and total person-years for Shared Savings Program assignable beneficiaries by Medicare enrollment type (End Stage Renal Disease (ESRD), disabled, aged/dual eligible, aged/non-dual eligible).

Aggregation data released on Freedom of Information Act (FOIA) Request 15-00242-F appeal. Fields with value of b6 indicate data withheld in accordance with VHA Expert Determination methodology and/or VA Office of General Counsel direction.

This paper compares two methods of analyzing aggregate data that is classified by period and age. Because there is a linear relationship among age, period, and cohort, it is not possible to distinguish the separate effects without employing an identifying assumption. The first method, which is applied in the economics literature, assumes that period effects are orthogonal to a linear time trend. The second method, which is applied in the statistics literature, assumes that the effect parameters change gradually. Simulation results suggest that the performances of both methods are comparable. The results of applying the second method to household saving rates suggest that period effects had a negligible influence in the United States but considerable influence in Japan.

The csv file contains aggregated data on the results of the experiment. The task is to analyze the results of the experiment and write your recommendations.

The Measurable AI Amazon Consumer Transaction Dataset is a leading source of email receipts and consumer transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Japan) - EMEA (Spain, United Arab Emirates)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018. All the transactional e-receipts are sent from app to users’ registered accounts.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

The Measurable AI Temu & Fast Fashion E-Receipt Dataset is a leading source of email receipts and transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Japan, Thailand, Malaysia, Vietnam, Indonesia, Singapore, Hong Kong, Phillippines) - EMEA (Spain, United Arab Emirates, Saudi, Qatar) - Latin America (Brazil, Mexico, Columbia, Argentina)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more - Email ID (can work out user overlap with peers and loyalty)

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

Aggregated data attached to Diversity in the High Tech industry report

This data release contains aggregated data records documenting the implementation of conservation practices supported by the U.S. Department of Agriculture (USDA) on farms within the Chesapeake Bay watershed. The data are supplied as annual totals aggregated by county, and by eight digit hydrologic unit code (HUC-8) watershed. This initial data release covers from 2007 through 2017. Updates are planned for December of each year.

This presenation provides an overview of the software Beyond 20/20, which allows users to manipulate, display, extract, and save aggregate data.

Aggregated data from the SOM project. Here I have aggregated several of the indicators to country-years, and added contextual data from many other sources. For the original SOM data, refer to Joost Berkhout; Sudulich, Laura; Ruedin, Didier; Peintinger, Teresa; Meyer, Sarah; Vangoidsenhoven, Guido; Cunningham, Kevin; Ros, Virgina; Wunderlich, Daniel, 2013, "Political Claims Analysis: Support and Opposition to Migration", https://hdl.handle.net/1902.1/17967, Harvard Dataverse, V1, UNF:5:8Gnxt4ColWPEe52HFrHoeg== For an enhanced version: https://doi.org/10.7910/DVN/4FGJTH

Blockchain data query: Aggregated Data 0xVM

The UK censuses took place on 27 March 2011. They were run by the Northern Ireland Statistics & Research Agency (NISRA), National Records of Scotland (NRS), and the Office for National Statistics (ONS) for both England and Wales. The UK comprises the countries of England, Wales, Scotland and Northern Ireland.

Statistics from the UK censuses help paint a picture of the nation and how we live. They provide a detailed snapshot of the population and its characteristics and underpin funding allocation to provide public services. This is the home for all UK census data.

The aggregate data produced as outputs from censuses in the United Kingdom provide information on a wide range of demographic and socio-economic characteristics. They are predominantly a collection of aggregated, or summary counts of the numbers of people, families or households resident in specific geographical areas possessing particular characteristics drawn from the themes of population, people and places, families, ethnicity and religion, health, work, and housing.

Aggregate data for Census 2011 cover the full range of geographies employed within the census, from the smallest (output areas with an average of 150 persons in England and Wales) to the nation as a whole.

• Access data through InFuse
• Census aggregate data guide

Citation: Office for National Statistics. (2019). 2011 Census: Aggregate Data. [data collection]. UK Data Service. SN: 7427, http://doi.org/10.5257/census/aggregate-2011-2

View Aggregate materials import data USA including customs records, shipments, HS codes, suppliers, buyer details & company profile at Seair Exim.

Introduction

Concrete is one if the most used building materials worldwide. With up to 80% of volume, a large constituent of concrete consists of fine and coarse aggregate particles (normally, sizes of 0.1mm to 32 mm) which are dispersed in a cement paste matrix. The size distribution of the aggregates (i.e. the grading curve) substantially affects the properties and quality characteristics of concrete, such as e.g. its workability at the fresh state and the mechanical properties at the hardened state. In practice, usually the size distribution of small samples of the aggregate is determined by manual mechanical sieving and is considered as representative for a large amount of aggregate. However, the size distribution of the actual aggregate used for individual production batches of concrete varies, especially when e.g. recycled material is used as aggregate. As a consequence, the unknown variations of the particle size distribution have a negative effect on the robustness and the quality of the final concrete produced from the raw material.

Towards the goal of deriving precise knowledge about the actual particle size distribution of the aggregate, thus eliminating the unknown variations in the material’s properties, we propose a data set for the image based prediction of the size distribution of concrete aggregates. Incorporating such an approach into the production chain of concrete enables to react on detected variations in the size distribution of the aggregate in real-time by adapting the composition, i.e. the mixture design of the concrete accordingly, so that the desired concrete properties are reached.

https://data.uni-hannover.de/dataset/f00bdcc4-8b27-4dc4-b48d-a84d75694e18/resource/042abf8d-e87a-4940-8195-2459627f57b6/download/overview.png" alt="Classicial vs. image based granulometry" title=" ">

Classification data

In the classification data, nine different grading curves are distinguished. In this context, the normative regulations of DIN 1045 are considered. The nine grading curves differ in their maximum particle size (8, 16, or 32 mm) and in the distribution of the particle size fractions allowing a categorisation of the curves to coarse-grained (A), medium-grained (B) and fine-grained (C) curves, respectively. A quantitative description of the grain size distribution of the nine curves distinguished is shown in the following figure, where the left side shows a histogram of the particle size fractions 0-2, 2-8, 8-16, and 16-32 mm and the right side shows the cumulative histograms of the grading curves (the vertical axes represent the mass-percentages of the material).

For each of the grading curves, two samples (S1 and S2) of aggregate particles were created. Each sample consists of a total mass of 5 kg of aggregate material and is carefully designed according to the grain size distribution shwon in the figure by sieving the raw material in order to separate the different grain size fractions first, and subsequently, by composing the samples according to the dedicated mass-percentages of the size distributions.

https://data.uni-hannover.de/dataset/f00bdcc4-8b27-4dc4-b48d-a84d75694e18/resource/17eb2a46-eb23-4ec2-9311-0f339e0330b4/download/statistics_classification-data.png" alt="Particle size distribution of the classification data">

For data acquisition, a static setup was used for which the samples are placed in a measurement vessel equipped with a set of calibrated reference markers whose object coordinates are known and which are assembled in a way that they form a common plane with the surface of the aggregate sample. We acquired the data by taking images of the aggregate samples (and the reference markers) which are filled in the the measurement vessel and whose constellation within the vessel is perturbed between the acquisition of each image in order to obtain variations in the sample’s visual appearance. This acquisition strategy allows to record multiple different images for the individual grading curves by reusing the same sample, consequently reducing the labour-intensive part of material sieving and sample generation. In this way, we acquired a data set of 900 images in total, consisting of 50 images of each of the two samples (S1 and S2) which were created for each of the nine grading curve definitions, respectively (50 x 2 x 9 = 900). For each image, we automatically detect the reference markers, thus receiving the image coordinates of each marker in addition to its known object coordinates. We make use of these correspondences for the computation of the homography which describes the perspective transformation of the reference marker’s plane in object space (which corresponds to the surface plane of the aggregate sample) to the image plane. Using the computed homography, we transform the image in order to obtain an perspectively rectified representation of the aggregate sample with a known, and especially a for the entire image consistent, ground sampling distance (GSD) of 8 px/mm. In the following figure, example images of our data set showing aggregate samples of each of the distinguished grading curve classes are depicted.

https://data.uni-hannover.de/dataset/f00bdcc4-8b27-4dc4-b48d-a84d75694e18/resource/59925f1d-3eef-4b50-986a-e8d2b0e14beb/download/examples_classification_data.png" alt="Example images of the classification data">

Related publications:

If you make use of the proposed data, please cite the publication listed below.

• Coenen, M., Beyer, D., Heipke, C. and Haist, M., 2022: Learning to Sieve: Prediction of Grading Curves from Images of Concrete Aggregate. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2022, pp. 227-235, Link.

Comprehensive dataset containing 10 verified Aggregate supplier businesses in Venezuela with complete contact information, ratings, reviews, and location data.