The dataset trust-deduplicated.csv shows the trusts-value given by anonymous users of the censorship resistant Freenet network to other users. IDs are anonymized by mapping each ID to an integer-index in a non-shared list of known IDs. The format is trust-giver;trust-receiver;trust-value.The dataset trust-sone.csv only includes trust relationships which fit the trust-allocation style in the Sone social networking plugin (either 75 for non-spammers or -25 for disruptive behaviour).The dataset trust-anonymized-2020-11-01-under-attack.csv shows a newer snapshot after the web of trust has been under attack for half a year. The dataset attacking-nodes-2020-11-01.csv includes the IDs of the likely attackers.The *.scm files were used to crawl the data from a locally running Freenet node (see https://freenetproject.org ).trust-deduplicated-force-atlas-hub-centrality.png and trust-sone-reingold.png provide first looks into the data visualized with the free tool Gephi.The 2020-11-01*scm files were used to create the 2020-11-01 datasets.Additional details are available at http://www.draketo.de/english/freenet/social-graph-snapshot

Learning powerful discriminative features is the key for machine fault diagnosis. Most existing methods based on convolutional neural network (CNN) have achieved promising results. However, they primarily focus on global features derived from sample signals and fail to explicitly mine relationships between signals. In contrast, graph convolutional network (GCN) is able to efficiently mine data relationships by taking graph data with topological structure as input, making them highly effective for feature representation in non-Euclidean space. In this article, to make good use of the advantages of CNN and GCN, we propose a graph attentional convolutional neural network (GACNN) for effective intelligent fault diagnosis, which includes two subnetworks of fully CNN and GCN to extract the multilevel features information, and uses Efficient Channel Attention (ECA) attention mechanism to reduce information loss. Extensive experiments on three datasets show that our framework improves the representation ability of features and fault diagnosis performance, and achieves competitive accuracy against other approaches. And the results show that GACNN can achieve superior performance even under a strong background noise environment.

About this dataset

Context

The dataset tabulates the Wilna town population by age. The dataset can be utilized to understand the age distribution and demographics of Wilna town.

Content

The dataset constitues the following three datasets

• Wilna, New York Age Group Population Dataset: A complete breakdown of Wilna town age demographics from 0 to 85 years, distributed across 18 age groups
• Wilna, New York Age Cohorts Dataset: Children, Working Adults, and Seniors in Wilna town - Population and Percentage Analysis
• Wilna, New York Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

The dataset tabulates the Wixon Valley population by age. The dataset can be utilized to understand the age distribution and demographics of Wixon Valley.

Content

The dataset constitues the following three datasets

• Wixon Valley, TX Age Group Population Dataset: A complete breakdown of Wixon Valley age demographics from 0 to 85 years, distributed across 18 age groups
• Wixon Valley, TX Age Cohorts Dataset: Children, Working Adults, and Seniors in Wixon Valley - Population and Percentage Analysis
• Wixon Valley, TX Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Business process event data modeled as labeled property graphs

Data Format
-----------

The dataset comprises one labeled property graph in two different file formats.

#1) Neo4j .dump format

A neo4j (https://neo4j.com) database dump that contains the entire graph and can be imported into a fresh neo4j database instance using the following command, see also the neo4j documentation: https://neo4j.com/docs/

/bin/neo4j-admin.(bat|sh) load --database=graph.db --from=

The .dump was created with Neo4j v3.5.

#2) .graphml format

A .zip file containing a .graphml file of the entire graph


Data Schema
-----------

The graph is a labeled property graph over business process event data. Each graph uses the following concepts

:Event nodes - each event node describes a discrete event, i.e., an atomic observation described by attribute "Activity" that occurred at the given "timestamp"

:Entity nodes - each entity node describes an entity (e.g., an object or a user), it has an EntityType and an identifier (attribute "ID")

:Log nodes - describes a collection of events that were recorded together, most graphs only contain one log node

:Class nodes - each class node describes a type of observation that has been recorded, e.g., the different types of activities that can be observed, :Class nodes group events into sets of identical observations

:CORR relationships - from :Event to :Entity nodes, describes whether an event is correlated to a specific entity; an event can be correlated to multiple entities

:DF relationships - "directly-followed by" between two :Event nodes describes which event is directly-followed by which other event; both events in a :DF relationship must be correlated to the same entity node. All :DF relationships form a directed acyclic graph.

:HAS relationship - from a :Log to an :Event node, describes which events had been recorded in which event log

:OBSERVES relationship - from an :Event to a :Class node, describes to which event class an event belongs, i.e., which activity was observed in the graph

:REL relationship - placeholder for any structural relationship between two :Entity nodes

The concepts a further defined in Stefan Esser, Dirk Fahland: Multi-Dimensional Event Data in Graph Databases. CoRR abs/2005.14552 (2020) https://arxiv.org/abs/2005.14552


Data Contents
-------------

neo4j-bpic15-2021-02-17 (.dump|.graphml.zip)

An integrated graph describing the raw event data of the entire BPI Challenge 2015 dataset.
van Dongen, B.F. (Boudewijn) (2015): BPI Challenge 2015. 4TU.ResearchData. Collection. https://doi.org/10.4121/uuid:31a308ef-c844-48da-948c-305d167a0ec1

This data is provided by five Dutch municipalities. The data contains all building permit applications over a period of approximately four years. There are many different activities present, denoted by both codes (attribute concept:name) and labels, both in Dutch (attribute taskNameNL) and in English (attribute taskNameEN). The cases in the log contain information on the main application as well as objection procedures in various stages. Furthermore, information is available about the resource that carried out the task and on the cost of the application (attribute SUMleges). The processes in the five municipalities should be identical, but may differ slightly. Especially when changes are made to procedures, rules or regulations the time at which these changes are pushed into the five municipalities may differ. Of course, over the four year period, the underlying processes have changed. The municipalities have a number of questions, namely: What are the roles of the people involved in the various stages of the process and how do these roles differ across municipalities? What are the possible points for improvement on the organizational structure for each of the municipalities? The employees of two of the five municipalities have physically moved into the same location recently. Did this lead to a change in the processes and if so, what is different? Some of the procedures will be outsourced from 2018, i.e. they will be removed from the process and the applicant needs to have these activities performed by an external party before submitting the application. What will be the effect of this on the organizational structures in the five municipalities? Where are differences in throughput times between the municipalities and how can these be explained? What are the differences in control flow between the municipalities? There are five different log files available in this collection. Events are labeled with both a code and a Dutch and English label. Each activity code consists of three parts: two digits, a variable number of characters, and then three digits. The first two digits as well as the characters indicate the subprocess the activity belongs to. For instance ‘01_HOOFD_xxx’ indicates the main process and ‘01_BB_xxx’ indicates the ‘objections and complaints’ (‘Beroep en Bezwaar’ in Dutch) subprocess. The last three digits hint on the order in which activities are executed, where the first digit often indicates a phase within a process. Each trace and each event, contain several data attributes that can be used for various checks and predictions. Furthermore, some employees may have performed tasks for different municipalities, i.e. if the employee number is the same, it is safe to assume the same person is being identified.

The data contains the following entities and their events

- Application - a building permit application handled in one of five Dutch municipalities
- Case_R - a user or worker involved in handling the application
- Responsible_actor - a user or worker designated as responsible actor for an activity
- monitoringResource - a user or worker designated as monitoring resource for an activity

The data contains 5 event log nodes as the data was integrated from 5 different event logs from 5 different systems.


Data Size
---------

BPIC15, nodes: 268851, relationships: 2620418

This data release includes water-quality data collected at 38 sites in central and eastern Massachusetts from April 2018 through May 2019 by the U.S. Geological Survey to support the implementation of site-dependent aluminum criteria for Massachusetts waters. Samples of effluent and receiving surface waters were collected monthly at four wastewater-treatment facilities (WWTFs) and seven water-treatment facilities (WTFs) (see SWQ_data_and_instantaneous_CMC_CCC_values.txt). The measured properties and constituents include pH, hardness, and filtered (dissolved) organic carbon, which are required inputs to the U.S. Environmental Protection Agency's Aluminum Criteria Calculator version 2.0. Outputs from the Aluminum Criteria Calculator are also provided in that file; these outputs consist of acute (Criterion Maximum Concentration, CMC) and chronic (Criterion Continuous Concentration, CCC) instantaneous water-quality values for total recoverable aluminum, calculated for monthly samples at selected ambient sites near each of the 11 facilities. Quality-control data from blank, replicate, and spike samples are provided (see SWQ_QC_data.txt). In addition to data tables, the data release includes time-series graphs of the discrete water-quality data (see SWQ_plot_discrete_all.zip). For pH, time-series graphs also are provided showing pH from the discrete monthly water-quality samples as well as near-continuous pH measured at one surface-water site at each facility (see SWQ_plot_contin_discrete_pH.zip). The near-continuous pH data, along with all of the discrete water-quality data except the quality-control data, are also available online from the U.S. Geological Survey's National Water Information System (NWIS) database (https://nwis.waterdata.usgs.gov/nwis).

This dataset includes information for projects that appear on the City of Austin’s Capital Improvement Visualization Information and Communication (CIVIC) Map Viewer, www.austintexas.gov/GIS/civic. These projects, also known as Capital Improvements Program (CIP) projects, implement the construction, replacement, or renovation of city assets that are useful to the community. Data is currently available for most CIP projects funded in full or in part by voter-approved bond programs from 2010 and 2012. The dataset below is subject to change at any time, and does not represent a comprehensive list of capital improvement projects. For more information about the City of Austin’s Capital Improvement Program, please visit www.austintexas.gov/department/civic. The City of Austin has produced CIVIC, a web application to search Capital Improvement Projects, for informational purposes only. The data and information available at this web site is provided "As is", and "As Available" and without any warranties of any kind either express or implied. The City makes no warranty regarding the accuracy or completeness of this site and the information provided. By accessing or using CIVIC, you agree to these terms of use. The City of Austin may change the terms of use at any time at its sole discretion and without notice.”

Abstract

The dataset was derived by the Bioregional Assessment Programme from stream gauge data from the Victorian Department of Environment and Primary Industries. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

The dataset includes graphs of mean monthly and annual flow distribution for selected sites within the Gippsland Basin bioregion Assessment area.

Flow distribution has been graphed for the following stream gauge sites:

• Meeniyan on the Tarwin River in South Gippsland River Region (GS:227202)

• Glenaladale on the Mitchell River in South Gippsland River Region (GS:224203)

• Sardine Creek on the Brodribb River in Snowy River Region (GS:222202)

• Princes Highway on the Bemm River in East Gippsland River Region (GS:221212)

Dataset History

Graphs of mean monthly and annual flow distribution for selected sites within the Gippsland Basin bioregion assessment area were developed using the analysis and data shown in the dataset Gippsland 1.1 Flow Distribution Calculation Spreadsheet (GUID: b0dc7bde-5b59-464b-a7a1-c53b45c0f425).

Dataset Citation

Bioregional Assessment Programme (2015) Gippsland Basin Bioregional Assessment product 1.1 - Flow Distribution Graphs. Bioregional Assessment Derived Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/d35670f9-a5f2-4117-8261-1c9d2eada9d5.

Dataset Ancestors

• Derived From Gippsland 1.1 flow distribution calculation spreadsheet

• Derived From Stream gauge data for GIP 1.1 flow distribution graphs

Context

This Dataset is an updated version of the Amazon review dataset released in 2014. As in the previous version, this dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). In addition, this version provides the following features:

• More reviews:

  • The total number of reviews is 233.1 million (142.8 million in 2014).

• New reviews:

  • Current data includes reviews in the range May 1996 - Oct 2018.

• Metadata: - We have added transaction metadata for each review shown on the review page.

  • Added more detailed metadata of the product landing page.

Acknowledgements

If you publish articles based on this dataset, please cite the following paper:

• Jianmo Ni, Jiacheng Li, Julian McAuley. Justifying recommendations using distantly-labeled reviews and fined-grained aspects. EMNLP, 2019.

Historical chart and dataset showing Syrian Arab Republic electricity access by year from 2000 to 2023.

About this dataset

Context

The dataset tabulates the University Heights population by age. The dataset can be utilized to understand the age distribution and demographics of University Heights.

Content

The dataset constitues the following three datasets

• University Heights, OH Age Group Population Dataset: A complete breakdown of University Heights age demographics from 0 to 85 years, distributed across 18 age groups
• University Heights, OH Age Cohorts Dataset: Children, Working Adults, and Seniors in University Heights - Population and Percentage Analysis
• University Heights, OH Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

ZINC is a free database of commercially-available compounds for virtual screening. ZINC contains over 230 million purchasable compounds in ready-to-dock, 3D formats. ZINC also contains over 750 million purchasable compounds that can be searched for analogs.

About this dataset

Context

The dataset tabulates the Woods Cross population by age. The dataset can be utilized to understand the age distribution and demographics of Woods Cross.

Content

The dataset constitues the following three datasets

• Woods Cross, UT Age Group Population Dataset: A complete breakdown of Woods Cross age demographics from 0 to 85 years, distributed across 18 age groups
• Woods Cross, UT Age Cohorts Dataset: Children, Working Adults, and Seniors in Woods Cross - Population and Percentage Analysis
• Woods Cross, UT Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Historical chart and dataset showing Ireland GNI by year from 1962 to 2023.

About this dataset

Context

The dataset tabulates the Valley Springs population by age. The dataset can be utilized to understand the age distribution and demographics of Valley Springs.

Content

The dataset constitues the following three datasets

• Valley Springs, SD Age Group Population Dataset: A complete breakdown of Valley Springs age demographics from 0 to 85 years, distributed across 18 age groups
• Valley Springs, SD Age Cohorts Dataset: Children, Working Adults, and Seniors in Valley Springs - Population and Percentage Analysis
• Valley Springs, SD Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

The dataset tabulates the West Farmington population by age. The dataset can be utilized to understand the age distribution and demographics of West Farmington.

Content

The dataset constitues the following three datasets

• West Farmington, OH Age Group Population Dataset: A complete breakdown of West Farmington age demographics from 0 to 85 years, distributed across 18 age groups
• West Farmington, OH Age Cohorts Dataset: Children, Working Adults, and Seniors in West Farmington - Population and Percentage Analysis
• West Farmington, OH Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Historical chart and dataset showing Singapore hunger statistics by year from N/A to N/A.

About this dataset

Context

The dataset tabulates the Westfield population by age. The dataset can be utilized to understand the age distribution and demographics of Westfield.

Content

The dataset constitues the following three datasets

• Westfield, IL Age Group Population Dataset: A complete breakdown of Westfield age demographics from 0 to 85 years, distributed across 18 age groups
• Westfield, IL Age Cohorts Dataset: Children, Working Adults, and Seniors in Westfield - Population and Percentage Analysis
• Westfield, IL Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

The dataset tabulates the Walton County population by age. The dataset can be utilized to understand the age distribution and demographics of Walton County.

Content

The dataset constitues the following three datasets

• Walton County, GA Age Group Population Dataset: A complete breakdown of Walton County age demographics from 0 to 85 years, distributed across 18 age groups
• Walton County, GA Age Cohorts Dataset: Children, Working Adults, and Seniors in Walton County - Population and Percentage Analysis
• Walton County, GA Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

The dataset tabulates the Wisner population by age. The dataset can be utilized to understand the age distribution and demographics of Wisner.

Content

The dataset constitues the following three datasets

• Wisner, LA Age Group Population Dataset: A complete breakdown of Wisner age demographics from 0 to 85 years, distributed across 18 age groups
• Wisner, LA Age Cohorts Dataset: Children, Working Adults, and Seniors in Wisner - Population and Percentage Analysis
• Wisner, LA Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.