Envestnet®| Yodlee®'s Online Shopping Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

A practical aggregation method for heterogeneous log-linear functions is presented. Inequality measures are employed in the construction of a simple but exact aggregate representation of an economy. Three macroeconomic applications are discussed: the aggregation of the Lucas supply function, the time-inconsistent behaviour of an egalitarian social planner facing heterogeneous discount rates, and the case of a simple heterogeneous growth model. In the latter application, aggregate CPS data is used to show that the slowdown that followed the first oil shock is worse than usually thought, and that the new economy growth resurgence is not as strong as it appears. The reaction of one man could be forecast by no known mathematics; the reaction of a billion is something else again.?Foundation and Empire, Isaac Asimov (1952)

Additional file 4. Example MM to check missing BP.

Data and Inferred Networks accompanying the manuscript entitled - “Aggregation of recount3 RNA-seq data improves the inference of consensus and context-specific gene co-expression networks”

Authors: Prashanthi Ravichandran, Princy Parsana, Rebecca Keener, Kaspar Hansen, Alexis Battle

Affiliations: Johns Hopkins University School of Medicine, Johns Hopkins University Department of Computer Science, Johns Hopkins University Bloomberg School of Public Health

Description:

This folder includes data produced in the analysis contained in the manuscript and inferred consensus and context-specific networks from graphical lasso and WGCNA with varying numbers of edges. Contents include:

• all_metadata.rds: File including meta-data columns of study accession ID, sample ID, assigned tissue category, cancer status and disease status obtained through manual curation for the 95,484 RNA-seq samples used in the study.

• all_counts.rds: log2 transformed RPKM normalized read counts for 5999 genes and 95,484 RNA-seq samples which was utilized for dimensionality reduction and data exploration

• precision_matrices.zip: Zipped folder including networks inferred by graphical lasso for different experiments presented in the paper using weighted covariance aggregation following PC correction.

• The networks can be found as follows. First, select the folder corresponding to the network of interest - for example, Blood, this will then include two or more folders which indicate the data aggregation utilized, select the folder corresponding appropriate level of data aggregation - either all samples/ GTEx for blood-specific networks, this includes precision matrices inferred across a range of penalization parameters. To view the precision matrix inferred for a particular value of the penalization parameter X, select the file labeled lambda_X.rds

• For select networks, we have included the computed centrality measures which can be accessed at centrality_X.rds for a particular value of the penalization parameter X.

• We have also included .rds files that list the hub genes from the consensus networks inferred from non-cancerous samples at “normal_hubs.rds”, and the consensus networks inferred from cancerous samples at “cancer_hubs.rds”

• The file “context_specific_selected_networks.csv” includes the networks that were selected for downstream biological interpretation based on the scale-free criterion which is also summarized in the Supplementary Tables.

• WGCNA.zip: A zipped folder containing gene modules inferred from WGCNA for sequentially aggregated GTEx, SRA, and blood studies. Select the data aggregated, and the number of studies based on folder names. For example, blood networks inferred from 20 studies can be accessed at blood/consensus/net_20. The individual networks correspond to distinct cut heights, and include information on the cut height used, the genes that the network was inferred over merged module labels, and merged module colors.

BIDS Phenotype Aggregation Example COPY OF "The NIMH Healthy Research Volunteer Dataset" (ds003982)

Modality-agnostic files were copied over and the CHANGES file was updated. Data was aggregated using:

python phenotype.py aggregate subject -i segregated_subject -o aggregated_subject

phenotype.py came from the GitHub repository: https://github.com/ericearl/bids-phenotype

THE ORIGINAL DATASET ds003982 README FOLLOWS

A comprehensive clinical, MRI, and MEG collection characterizing healthy research volunteers collected at the National Institute of Mental Health (NIMH) Intramural Research Program (IRP) in Bethesda, Maryland using medical and mental health assessments, diagnostic and dimensional measures of mental health, cognitive and neuropsychological functioning, structural and functional magnetic resonance imaging (MRI), along with diffusion tensor imaging (DTI), and a comprehensive magnetoencephalography battery (MEG).

In addition, blood samples are currently banked for future genetic analysis. All data collected in this protocol are broadly shared in the OpenNeuro repository, in the Brain Imaging Data Structure (BIDS) format. In addition, blood samples of healthy volunteers are banked for future analyses. All data collected in this protocol are broadly shared here, in the Brain Imaging Data Structure (BIDS) format. In addition, task paradigms and basic pre-processing scripts are shared on GitHub. This dataset is unique in its depth of characterization of a healthy population in terms of brain health and will contribute to a wide array of secondary investigations of non-clinical and clinical research questions.

This dataset is licensed under the Creative Commons Zero (CC0) v1.0 License.

Recruitment

Inclusion criteria for the study require that participants are adults at or over 18 years of age in good health with the ability to read, speak, understand, and provide consent in English. All participants provided electronic informed consent for online screening and written informed consent for all other procedures. Exclusion criteria include:

• A history of significant or unstable medical or mental health condition requiring treatment
• Current self-injury, suicidal thoughts or behavior
• Current illicit drug use by history or urine drug screen
• Abnormal physical exam or laboratory result at the time of in-person assessment
• Less than an 8th grade education or IQ below 70
• Current employees, or first-degree relatives of NIMH employees

Study participants are recruited through direct mailings, bulletin boards and listservs, outreach exhibits, print advertisements, and electronic media.

Clinical Measures

All potential volunteers first visit the study website (https://nimhresearchvolunteer.ctss.nih.gov), check a box indicating consent, and complete preliminary self-report screening questionnaires. The study website is HIPAA compliant and therefore does not collect PII ; instead, participants are instructed to contact the study team to provide their identity and contact information. The questionnaires include demographics, clinical history including medications, disability status (WHODAS 2.0), mental health symptoms (modified DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure), substance use survey (DSM-5 Level 2), alcohol use (AUDIT), handedness (Edinburgh Handedness Inventory), and perceived health ratings. At the conclusion of the questionnaires, participants are again prompted to send an email to the study team. Survey results, supplemented by NIH medical records review (if present), are reviewed by the study team, who determine if the participant is likely eligible for the protocol. These participants are then scheduled for an in-person assessment. Follow-up phone screenings were also used to determine if participants were eligible for in-person screening.

In-person Assessments

At this visit, participants undergo a comprehensive clinical evaluation to determine final eligibility to be included as a healthy research volunteer. The mental health evaluation consists of a psychiatric diagnostic interview (Structured Clinical Interview for DSM-5 Disorders (SCID-5), along with self-report surveys of mood (Beck Depression Inventory-II (BD-II) and anxiety (Beck Anxiety Inventory, BAI) symptoms. An intelligence quotient (IQ) estimation is determined with the Kaufman Brief Intelligence Test, Second Edition (KBIT-2). The KBIT-2 is a brief (20-30 minute) assessment of intellectual functioning administered by a trained examiner. There are three subtests, including verbal knowledge, riddles, and matrices.

Medical Evaluation

Medical evaluation includes medical history elicitation and systematic review of systems. Biological and physiological measures include vital signs (blood pressure, pulse), as well as weight, height, and BMI. Blood and urine samples are taken and a complete blood count, acute care panel, hepatic panel, thyroid stimulating hormone, viral markers (HCV, HBV, HIV), C-reactive protein, creatine kinase, urine drug screen and urine pregnancy tests are performed. In addition, blood samples that can be used for future genomic analysis, development of lymphoblastic cell lines or other biomarker measures are collected and banked with the NIMH Repository and Genomics Resource (Infinity BiologiX). The Family Interview for Genetic Studies (FIGS) was later added to the assessment in order to provide better pedigree information; the Adverse Childhood Events (ACEs) survey was also added to better characterize potential risk factors for psychopathology. The entirety of the in-person assessment not only collects information relevant for eligibility determination, but it also provides a comprehensive set of standardized clinical measures of volunteer health that can be used for secondary research.

MRI Scan

Participants are given the option to consent for a magnetic resonance imaging (MRI) scan, which can serve as a baseline clinical scan to determine normative brain structure, and also as a research scan with the addition of functional sequences (resting state and diffusion tensor imaging). The MR protocol used was initially based on the ADNI-3 basic protocol, but was later modified to include portions of the ABCD protocol in the following manner:

1. The T1 scan from ADNI3 was replaced by the T1 scan from the ABCD protocol.
2. The Axial T2 2D FLAIR acquisition from ADNI2 was added, and fat saturation turned on.
3. Fat saturation was turned on for the pCASL acquisition.
4. The high-resolution in-plane hippocampal 2D T2 scan was removed and replaced with the whole brain 3D T2 scan from the ABCD protocol (which is resolution and bandwidth matched to the T1 scan).
5. The slice-select gradient reversal method was turned on for DTI acquisition, and reconstruction interpolation turned off.
6. Scans for distortion correction were added (reversed-blip scans for DTI and resting state scans).
7. The 3D FLAIR sequence was made optional and replaced by one where the prescription and other acquisition parameters provide resolution and geometric correspondence between the T1 and T2 scans.

At the time of the MRI scan, volunteers are administered a subset of tasks from the NIH Toolbox Cognition Battery. The four tasks include:

1. Flanker inhibitory control and attention task assesses the constructs of attention and executive functioning.
2. Executive functioning is also assessed using a dimensional change card sort test.
3. Episodic memory is evaluated using a picture sequence memory test.
4. Working memory is evaluated using a list sorting test.

MEG

An optional MEG study was added to the protocol approximately one year after the study was initiated, thus there are relatively fewer MEG recordings in comparison to the MRI dataset. MEG studies are performed on a 275 channel CTF MEG system (CTF MEG, Coquiltam BC, Canada). The position of the head was localized at the beginning and end of each recording using three fiducial coils. These coils were placed 1.5 cm above the nasion, and at each ear, 1.5 cm from the tragus on a line between the tragus and the outer canthus of the eye. For 48 participants (as of 2/1/2022), photographs were taken of the three coils and used to mark the points on the T1 weighted structural MRI scan for co-registration. For the remainder of the participants (n=16 as of 2/1/2022), a Brainsight neuronavigation system (Rogue Research, Montréal, Québec, Canada) was used to coregister the MRI and fiducial localizer coils in realtime prior to MEG data acquisition.

Specific Measures within Dataset

Online and In-person behavioral and clinical measures, along with the corresponding phenotype file name, sorted first by measurement location and then by file name.

Sample output from the Aggregate_Builder_Constant_Population simulation. This data can be used to create the figures in the paper.

Additional file 2: Appendix B. Example AQL.

Monthly report including total dispatched trips, total dispatched shared trips, and unique dispatched vehicles aggregated by FHV (For-Hire Vehicle) base. These have been tabulated from raw trip record submissions made by bases to the NYC Taxi and Limousine Commission (TLC). This dataset is typically updated monthly on a two-month lag, as bases have until the conclusion of the following month to submit a month of trip records to the TLC. In example, a base has until Feb 28 to submit complete trip records for January. Therefore, the January base aggregates will appear in March at the earliest. The TLC may elect to defer updates to the FHV Base Aggregate Report if a large number of bases have failed to submit trip records by the due date. Note: The TLC publishes base trip record data as submitted by the bases, and we cannot guarantee or confirm their accuracy or completeness. Therefore, this may not represent the total amount of trips dispatched by all TLC-licensed bases. The TLC performs routine reviews of the records and takes enforcement actions when necessary to ensure, to the extent possible, complete and accurate information.

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."

This dataset contains de-identified data with an accompanying data dictionary and the R script for de-identification procedures., Objective(s): To demonstrate application of a risk based de-identification framework using the Smart Triage dataset as a clinical example. Data Description: This dataset contains the de-identified version of the Smart Triage Jinja dataset with the accompanying data dictionary and R script for de-identification procedures. Limitations: Utility of the de-identified dataset has only been evaluated with regard to use for the development of prediction models based on a need for hospital admission. Abbreviations: NA Ethics Declaration: The study was reviewed by the instituational review boards at the University of British Columbia in Canada (ID: H19-02398; H20-00484), The Makerere University School of Public Health in Uganda and the Uganda National Council for Science and Technology

The file set is a freely downloadable aggregation of information about Australian schools. The individual files represent a series of tables which, when considered together, form a relational database. The records cover the years 2008-2014 and include information on approximately 9500 primary and secondary school main-campuses and around 500 subcampuses. The records all relate to school-level data; no data about individuals is included. All the information has previously been published and is publicly available but it has not previously been released as a documented, useful aggregation. The information includes: (a) the names of schools (b) staffing levels, including full-time and part-time teaching and non-teaching staff (c) student enrolments, including the number of boys and girls (d) school financial information, including Commonwealth government, state government, and private funding (e) test data, potentially for school years 3, 5, 7 and 9, relating to an Australian national testing programme know by the trademark 'NAPLAN'

Documentation of this Edition 2016.1 is incomplete but the organization of the data should be readily understandable to most people. If you are a researcher, the simplest way to study the data is to make use of the SQLite3 database called 'school-data-2016-1.db'. If you are unsure how to use an SQLite database, ask a guru.

The database was constructed directly from the other included files by running the following command at a command-line prompt: sqlite3 school-data-2016-1.db < school-data-2016-1.sql Note that a few, non-consequential, errors will be reported if you run this command yourself. The reason for the errors is that the SQLite database is created by importing a series of '.csv' files. Each of the .csv files contains a header line with the names of the variable relevant to each column. The information is useful for many statistical packages but it is not what SQLite expects, so it complains about the header. Despite the complaint, the database will be created correctly.

Briefly, the data are organized as follows. (a) The .csv files ('comma separated values') do not actually use a comma as the field delimiter. Instead, the vertical bar character '|' (ASCII Octal 174 Decimal 124 Hex 7C) is used. If you read the .csv files using Microsoft Excel, Open Office, or Libre Office, you will need to set the field-separator to be '|'. Check your software documentation to understand how to do this. (b) Each school-related record is indexed by an identifer called 'ageid'. The ageid uniquely identifies each school and consequently serves as the appropriate variable for JOIN-ing records in different data files. For example, the first school-related record after the header line in file 'students-headed-bar.csv' shows the ageid of the school as 40000. The relevant school name can be found by looking in the file 'ageidtoname-headed-bar.csv' to discover that the the ageid of 40000 corresponds to a school called 'Corpus Christi Catholic School'. (3) In addition to the variable 'ageid' each record is also identified by one or two 'year' variables. The most important purpose of a year identifier will be to indicate the year that is relevant to the record. For example, if one turn again to file 'students-headed-bar.csv', one sees that the first seven school-related records after the header line all relate to the school Corpus Christi Catholic School with ageid of 40000. The variable that identifies the important differences between these seven records is the variable 'studentyear'. 'studentyear' shows the year to which the student data refer. One can see, for example, that in 2008, there were a total of 410 students enrolled, of whom 185 were girls and 225 were boys (look at the variable names in the header line). (4) The variables relating to years are given different names in each of the different files ('studentsyear' in the file 'students-headed-bar.csv', 'financesummaryyear' in the file 'financesummary-headed-bar.csv'). Despite the different names, the year variables provide the second-level means for joining information acrosss files. For example, if you wanted to relate the enrolments at a school in each year to its financial state, you might wish to JOIN records using 'ageid' in the two files and, secondarily, matching 'studentsyear' with 'financialsummaryyear'. (5) The manipulation of the data is most readily done using the SQL language with the SQLite database but it can also be done in a variety of statistical packages. (6) It is our intention for Edition 2016-2 to create large 'flat' files suitable for use by non-researchers who want to view the data with spreadsheet software. The disadvantage of such 'flat' files is that they contain vast amounts of redundant information and might not display the data in the form that the user most wants it. (7) Geocoding of the schools is not available in this edition. (8) Some files, such as 'sector-headed-bar.csv' are not used in the creation of the database but are provided as a convenience for researchers who might wish to recode some of the data to remove redundancy. (9) A detailed example of a suitable SQLite query can be found in the file 'school-data-sqlite-example.sql'. The same query, used in the context of analyses done with the excellent, freely available R statistical package (http://www.r-project.org) can be seen in the file 'school-data-with-sqlite.R'.

Additional file 1: Appendix A. Example MM.

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.

This file provides summary or aggregated measures for the 82 societies participating in the first four waves of the World Value Surveys. Thus, the society, rather than the individuals surveyed, are the unit of analysis.

"The World Values Survey is a worldwide investigation of sociocultural and political change. It is conducted by a network of social scientists at leading universities all around world.

Interviews have been carried out with nationally representative samples of the publics of more than 80 societies on all six inhabited continents. A total of four waves have been carried out since 1981 making it possible to carry out reliable global cross-cultural analyses and analysis of changes over time. The World Values Survey has produced evidence of gradual but pervasive changes in what people want out of life. Moreover, the survey shows that the basic direction of these changes is, to some extent, predictable.

This project is being carried out by an international network of social scientists, with local funding for each survey (though in some cases, it has been possible to raise supplementary funds from outside sources). In exchange for providing the data from interviews with a representative national sample of at least 1,000 people in their own society, each participating group gets immediate access to the data from all of the other participating societies. Thus, they are able to compare the basic values and beliefs of the people of their own society with those of more than 60 other societies. In addition, they are invited to international meetings at which they can compare findings and interpretations with other members of the WVS network."

Datasets and results associated with "Sample, estimate, aggregate: A recipe for causal discovery foundation models"

Additional information about the carboxylated SWCNT calibration curve, AFM images, EDS results, solar simulator light and UVB lamp spectra, TEM image of parent carboxylated SWCNTs, XPS spectra of the dark control P3 sample and the irradiated P3 sample, and a table summarizing the kinetic parameters (PDF). This dataset is associated with the following publication: Hou, W., C. He, Y. Wang, D. Wang, and R. Zepp. Phototransformation-Induced Aggregation of Functionalized Single-Walled Carbon Nanotubes: The Importance of Amorphous Carbon. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 50(7): 3494–3502, (2016).

1. Spatial capture-recapture (SCR) models are commonly used for analyzing data collected using non-invasive genetic sampling (NGS). Opportunistic NGS often leads to detections that do not occur at discrete detector locations. Therefore, spatial aggregation of individual detections into fixed detectors (e.g. center of grid cells) is an option to increase computing speed of SCR analyses. However, it may reduce precision and accuracy of parameter estimations.
2. Using simulations, we explored the impact that spatial aggregation of detections has on a trade-off between computing time and parameter precision and bias, under a range of biological conditions. We used three different observation models: the commonly used Poisson and Bernoulli models, as well as a novel way to partially aggregate detections (Partially Aggregated Binary model (PAB)) to reduce the loss of information after aggregating binary detections. The PAB model divides detectors into K subdetectors and models the frequency of subdetectors with more than one detection as a binomial response with a sample size of K. Finally, we demonstrate the consequences of aggregation and the use of the PAB model using NGS data from the monitoring of wolverine (Gulo gulo) in Norway.
3. Spatial aggregation of detections, while reducing computation time, does indeed incur costs in terms of reduced precision and accuracy, especially for the parameters of the detection function. SCR models estimated abundance with a low bias (< 10%) even at high degree of aggregation, but only for the Poisson and PAB models. Overall, the cost of aggregation is mitigated when using the Poisson and PAB models. At the same level of aggregation, the PAB observation models out-performs the Bernoulli model in terms of accuracy of estimates, while offering the benefits of a binary observation model (less assumptions about the underlying ecological process) over the count-based model.
4. We recommend that detector spacing after aggregation does not exceed 1.5 times the scale-parameter of the detection function in order to limit bias. We recommend the use of the PAB observation model when performing spatial aggregation of binary data as it can mitigate the cost of aggregation, compared to the Bernoulli model.

Analysis of ‘FHV Base Aggregate Report’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7992e33a-6319-413c-b196-dec3f18dafd0 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Monthly report including total dispatched trips, total dispatched shared trips, and unique dispatched vehicles aggregated by FHV (For-Hire Vehicle) base. These have been tabulated from raw trip record submissions made by bases to the NYC Taxi and Limousine Commission (TLC).

This dataset is typically updated monthly on a two-month lag, as bases have until the conclusion of the following month to submit a month of trip records to the TLC. In example, a base has until Feb 28 to submit complete trip records for January. Therefore, the January base aggregates will appear in March at the earliest. The TLC may elect to defer updates to the FHV Base Aggregate Report if a large number of bases have failed to submit trip records by the due date.

Note: The TLC publishes base trip record data as submitted by the bases, and we cannot guarantee or confirm their accuracy or completeness. Therefore, this may not represent the total amount of trips dispatched by all TLC-licensed bases. The TLC performs routine reviews of the records and takes enforcement actions when necessary to ensure, to the extent possible, complete and accurate information.

--- Original source retains full ownership of the source dataset ---

To realize the potential of citizens to contribute to conservation efforts through the acquisition of data for broad-scale species distribution models, scientists need to understand and minimize the influences of commonly observed sample selection bias on model performance. Yet evaluating these data with independent, planned surveys is rare, even though such evaluation is necessary for understanding and applying data to conservation decisions. We used the state-listed fox squirrel Sciurus niger in Florida, USA, to interpret the performance of models created with opportunistic observations from citizens and professionals by validating models with independent, planned surveys. Data from both citizens and professionals showed sample selection bias with more observations within 50 m of a road. While these groups showed similar sample selection bias in reference to roads, there were clear differences in the spatial coverage of the groups, with citizens observing fox squirrels more frequently in developed areas. Based on predictions at planned field surveys sites, models developed from citizens generally performed similarly to those developed with data collected by professionals. Accounting for potential sample selection bias in models, either through the use of covariates or via aggregating data into home range size grids, provided only slight increases in model performance. Synthesis and applications. Despite sample selection biases, over a broad spatial scale opportunistic citizen data provided reliable predictions and estimates of habitat relationships needed to advance conservation efforts. Our results suggest that the use of professionals may not be needed in volunteer programmes used to determine the distribution of species of conservation interest across broad spatial scales.

Comprehensive dataset of 41 Aggregate suppliers in Germany as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.