Population connectivity research involves investigating the presence, strength and characteristics of spatial and temporal relationships between populations. These data can be used in many different ways: to identify source-sink relationships between populations; to detect critical pathways or keystone habitats; to find natural clusters or biogeographic regions; or to investigate the processes underlying population genetic structure, among others. This information can be of significant value for managers and decision-makers when designing reserve networks, evaluating the potential spread of invasive species. This database represents the first publicly-available collection of national/continental-scale marine connectivity data.

You can also purchase hard copies of Geoscience Australia data and other products at http://www.ga.gov.au/products-services/how-to-order-products/sales-centre.html

Field data used to support numerical simulations of variably-saturated flow focused on variability in soil-water retention properties for the U.S. Geological Survey Bay Area Landslide Type (BALT) Site #1 in the East Bay region of California, USA

We present a novel methodology to assess theoretically physiological computed indices and algorithms based on pulse wave analysis in the large arteries of the cardiovascular system.

We have created a database of virtual healthy adult subjects using a validated one-dimensional numerical model of the arterial hemodynamics, which cardiac and arterial parameters are varied within physiological healthy ranges. The generated set of simulations encloses more than 3300 cases which could be encountered in a clinical study. For each simulation, hemodynamic signals (e.g. pressure, flow and distension waveforms) are available at all arterial locations, and allow the computation of indices of interest.

The database has been efficiently used to assess the accuracy of the foot-to-foot pulse wave velocities for estimation of aortic stiffness [1] and other physiological indices. It is an efficient way to validate an algorithm based on pressure and flow signals without suffering from experimental error. Finally, the database can be used to understand the theoretical mechanisms of wave propagation: since all arterial parameters are known, one can easily post-process the pressure and flow waveforms.

[1] M. Willemet, P. Chowienczyk and J. Alastruey. A database of virtual healthy subjects to assess the accuracy of foot-to-foot pulse wave velocities for estimation of aortic stiffness. American Journal of Physiology - Heart and Circulatory Physiology, 309(4):H663-H675, 2015

Data is saved in Matlab formatted files, with results sorted by arterial location, and physiology of the results (each file is about 300 MB). In addition, the Fictive_database.mat file stores a description of the arterial network geometry, and values of computed physiological indices (e.g. Cardiac Output, PWV, Pulse Pressure). The structure of the database is explained in details in the manual document.

Numerical Database from Large-Eddy Simulations of a Supersonic Jet Flow (Re= 1.6x10E6 , M=1.4) - Database 3 / 6 (Continuation of https://doi.org/10.5281/zenodo.13902381 database)
Authors: Diego F. Abreu, João Luiz F. Azevedo, Carlos Junqueira-Junior

The operational parameters for the jet flow include a Mach number of 1.4 and a Reynolds number of 1.58E6 referenced to the nozzle exit diameter, corresponding to a perfectly expanded supersonic condition. The pressure and temperature of the jet flow match those of the surrounding ambient conditions.

The dataset originates from six numerical simulations employing various mesh resolutions and polynomial orders, along with different boundary conditions. These calculations were performed to investigate the impact of mesh resolution, polynomial order, and boundary conditions on LES of the supersonic jet flow in the absence of nozzle effects. The database encompasses a collection of probes and planes extracted from the 3-D domain as outlined in the attached README.md file.

For further details regarding these probes and planes, as well as information on the numerical simulations, please refer to the supplemental-material-database.pdf file.
The database is divided into six parts. The present set of data is number one.

This database is associated with the manuscript entitled "Assessment of Jet Inflow Condition on the Development of Supersonic Jet Flows". The numerical data presented herein were previously published in the work entitled "Accuracy Assessment of Discontinuous Galerkin Spectral Element Method in Simulating Supersonic Free Jets" (https://doi.org/10.1007/s40430-024-04788-z) and the Ph.D. Thesis "Study of Turbulent Supersonic Jet Flows and the Influence of Nozzle-Exit Boundary Conditions on the Jet Initial Development".

Macau Gaming: Number of Gaming Table data was reported at 6,598.000 Unit in Sep 2018. This records an increase from the previous number of 6,588.000 Unit for Jun 2018. Macau Gaming: Number of Gaming Table data is updated quarterly, averaging 5,302.000 Unit from Mar 2005 (Median) to Sep 2018, with 55 observations. The data reached an all-time high of 6,598.000 Unit in Sep 2018 and a record low of 1,226.000 Unit in Mar 2005. Macau Gaming: Number of Gaming Table data remains active status in CEIC and is reported by Gaming Inspection and Coordination Bureau. The data is categorized under Global Database’s Macau SAR – Table MO.Q019: Number of Casinos and Gaming Tables.

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United States Number of Housing Unit: Vacant data was reported at 17,231.000 Unit th in Sep 2018. This records an increase from the previous number of 17,073.000 Unit th for Jun 2018. United States Number of Housing Unit: Vacant data is updated quarterly, averaging 12,114.000 Unit th from Mar 1965 (Median) to Sep 2018, with 215 observations. The data reached an all-time high of 19,061.000 Unit th in Mar 2009 and a record low of 5,980.000 Unit th in Dec 1968. United States Number of Housing Unit: Vacant data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.EB011: Number of Housing Units. Series Remarks Data for 1979 Q1 to Q4 was revised to reflect changes made in 1980. Data for 1989 Q1 to Q4 was revised to include year-round vacant mobile homes. Data for 1993 Q1 to Q4 was revised based on the 1990 Census. Data for 2002 Q1 to Q4 was revised based on the 2000 Census.

Luxembourg Number of Enterprises: NACE 2: New data was reported at 4,747.000 Unit in 2022. This records an increase from the previous number of 3,584.000 Unit for 2021. Luxembourg Number of Enterprises: NACE 2: New data is updated yearly, averaging 3,179.500 Unit from Dec 2003 (Median) to 2022, with 20 observations. The data reached an all-time high of 4,747.000 Unit in 2022 and a record low of 2,366.000 Unit in 2004. Luxembourg Number of Enterprises: NACE 2: New data remains active status in CEIC and is reported by Statistics Portal of Luxembourg. The data is categorized under Global Database’s Luxembourg – Table LU.O005: Number of Enterprises: Statistical Classification of Economic Activities Rev 2.

Excel spreadsheet containing, in separate sheets, the underlying numerical data for figure panels.

This dataset contains synthetic lidar measurements from a pulsed lidar mounted on the hub / spinner of the DTU 10MW wind turbine model. The data was generated using the numerical hub-lidar sensor feature in HAWC2 v13.1.For the inflow conditions, Mann-generated turbulence boxes for wind speeds 8.0, 11.4 and 18.0 m/s were used, where three different seeds were considered, generating nine inflow cases.For more detailed information, please refer to the documentation.The dataset and repository were curated at the Technical University of Denmark by Esperanza Soto Sagredo (ORCID 0000-0002-5645-2335, espa@dtu.dk) and Jennifer M. Rinker (ORCID 0000-0002-1122-1891, rink@dtu.dk).Utilization of this database, in whole or in part, necessitates proper acknowledgment.This work is licensed under a CCBY 4.0 license. Please cite as:Soto Sagredo, Esperanza Andrea; Rinker, Jennifer (2024). Numerical hub-lidar data from Mann-generated turbulence boxes using HAWC2 v13.1 and DTU 10MW reference wind turbine. Technical University of Denmark. Dataset. https://doi.org/10.11583/DTU.23904990This work is part of the CONTINUE project, which has received funding from the Danish Energy Technology Development and Demonstration Programme (EUDP), under grant agreement 640222-496980.

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Instance Data for Numerical Study on Planning of Inductive Charging Infrastructures for Electric Service Vehicles on AIrport Aprons

Number of Immigrants: Former Yugoslavia: Yugoslavia data was reported at 1,398.000 Person in 2017. This records a decrease from the previous number of 1,429.000 Person for 2016. Number of Immigrants: Former Yugoslavia: Yugoslavia data is updated yearly, averaging 1,393.000 Person from Dec 1995 (Median) to 2017, with 23 observations. The data reached an all-time high of 7,305.000 Person in 1995 and a record low of 783.000 Person in 2006. Number of Immigrants: Former Yugoslavia: Yugoslavia data remains active status in CEIC and is reported by Statistics Netherlands. The data is categorized under Global Database’s Netherlands – Table NL.G005: Number of Immigrants: by Country.

All numerical raw data associated with Fig 2 and S2 Fig. The file contains multiple tabs with labels corresponding to the relevant figure. (XLSX)

Abstract: This code has been used for the numerical experiments in the thesis "Numerical homogenization of time-dependent Maxwell's equations with dispersion effects" by Jan Philip Freese, see https://www.doi.org/10.5445/IR/1000129214. TechnicalRemarks: # Readme This code was used for the numerical experiments of the PhD thesis "Numerical homogenization of time-dependent Maxwell's equations with dispersion effects" by P. Freese (cf. Section 7.2, Section 7.3) https://www.doi.org/10.5445/IR/1000129214. The computations are done in C++ using the Finite Element library deal.II. Requirements

This vast repository houses crucial information on international trade transactions, capturing the intricate details of both export and import activities of Zimbabwe. The Export Database contains meticulous records of outbound shipments, offering valuable insights into the products, exporters, and destinations involved in each transaction. On the other hand, the Import Database provides a comprehensive view of inbound shipments, shedding light on the importers, origins, and details of the products acquired. Together, these two databases present a holistic perspective on global trade dynamics, encompassing critical metadata such as dates, product descriptions, quantities, values, and transportation specifics. Whether you are an analyst, researcher, or business professional, this comprehensive database will undoubtedly prove to be an invaluable resource for gaining a deep understanding of international trade patterns and market dynamics. Explore the wealth of information within and unlock new opportunities in the world of trade and commerce.

The Export Database contains information related to export transactions. Each entry in the database represents a specific export event. The metadata fields in this database hold crucial details about the exported products and the transaction itself. The "DATE" field indicates the date of the export. "EXPORTER NAME" refers to the name of the entity or company responsible for exporting the goods. "DESTINATION COUNTRY" indicates the country to which the products are being shipped. The "HS CODE" represents the Harmonized System code, a standardized numerical system used to classify traded products. The "PRODUCT DESCRIPTION" field provides a brief description of the exported item. The "BRAND" field specifies the brand associated with the product. "QUANTITY" indicates the total quantity of the product being exported, and "UNIT OF QUANTITY" represents the measurement unit used for quantity. "SUBITEM QUANTITY" refers to the quantity of a subitem within the main exported product. The "PACKAGES" field indicates the number of packages used for shipment. "GROSS WEIGHT" represents the total weight of the exported products. "SUBITEM FOB VALUE" and "TOTAL FOB VALUE" denote the Free on Board (FOB) value of the subitem and the total FOB value of the export, respectively. "TOTAL CIF VALUE" indicates the total cost, insurance, and freight value. "ITEM NUMBER" is a unique identifier for each product item. "TRANSPORT TYPE" specifies the mode of transportation used for the export. "INCOTERMS" refers to the standardized international trade terms defining the responsibilities of buyers and sellers during transportation. "CUSTOMS" indicates the customs information related to the export. "VARIETY" and "ATTRIBUTES" hold additional details about the product. The "OPERATION TYPE" field indicates the type of export operation, such as direct export or re-export. "MONTH" and "YEAR" represent the month and year when the export occurred.

The Import Database contains information related to import transactions. Each entry in the database represents a specific import event. The metadata fields in this database hold crucial details about the imported products and the transaction itself. The "DATE" field indicates the date of the import. "IMPORTER NAME" refers to the name of the entity or company responsible for importing the goods. "SALES COUNTRY" indicates the country from which the products are being purchased. "ORIGIN COUNTRY" denotes the country where the imported products originate. The "HS CODE" represents the Harmonized System code, a standardized numerical system used to classify traded products. The "PRODUCT DESCRIPTION" field provides a brief description of the imported item. "QUANTITY" indicates the total quantity of the product being imported, and "UNIT OF QUANTITY" represents the measurement unit used for quantity. "SUBITEM QUANTITY" refers to the quantity of a subitem within the main imported product. The "PACKAGES" field indicates the number of packages used for shipment. "GROSS WEIGHT" represents the total weight of the imported products. "TOTAL CIF VALUE" indicates the total cost, insurance, and freight value. "TOTAL FREIGHT VALUE" and "TOTAL INSURANCE VALUE" represent the respective values for freight and insurance. "ITEM FOB VALUE," "SUBITEM FOB VALUE," and "ITEM CIF VALUE" denote the Free on Board (FOB) value of the item, subitem, and the cost, insurance, and freight value of the item, respectively. "ORIGIN PORT" specifies the port from which the products were shipped. "TRANSPORT TYPE" specifies the mode of transportation used for the import. "INCOTERMS" refers to the standardized international trade terms defining the responsibilities of buyers and sellers during transportation. "ITEM NUMBER" is a unique identifier for each product item. "CUSTOMS" indicates the customs information related to the import. "OPERATION TYPE" field indicates the type of import operation, such as direct...

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This replication package reproduces the results for the paper entitled "Adding measurement error to location data to protect subject confidentiality while allowing for consistent estimation of exposure effects," which is published in The Journal of the Royal Statistical Society: Series C (Applied Statistics), DOI: https://doi.org/10.1111/rssc.12439. This package contains 2 Stata Do-Files (.do) that produce the simulated dataset and run one replication of the simulation (the main paper runs 1,000 replications), and 2 Stata Data Files (.dta) that are used for the simulation in the main paper. (2020-02-29).

During the fourth quarter of 2024, data breaches exposed more than a million user data records in the United Kingdom (UK). The figure decreased significantly from nearly 41 million in the quarter prior. Overall, the time between the first quarter of 2022 and the fourth quarter of 2023, saw the lowest number of exposed user data accounts.