PHL Open Data Testing

This data is a test only.. Visit https://dataone.org/datasets/sha256%3A497b005e0f17751ced46a1b3a24dd54def8c3763e417856cd21a054832612522 for complete metadata about this dataset.

Geoportal Rhineland Palatinate. Administrative limits:

The OSNI Large-scale boundaries is a polygon dataset consisting of the land area of Northern Ireland. The data has been extracted from OSNI Largescale database and has been topologically cleansed and attributed to create a seamless dataset. This service is published for OpenData By download or use of this dataset you agree to abide by the LPS Open Government Data Licence.

Test old region view

The community plan area dataset contains polygons that outline the boundaries of each community plan area, as set out in each individual community plan.

this is a test

Here we provide the molecular datasets and metadata associated with specimens used in the investigation of species boundaries and phylogeographic structure of two freshwater mussels, one common (Pleurobema sintoxia) and one being considered for protection under the Endangered Species Act (Pleurobema rubrum).

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This paper considers the significance of early time data for detecting linear boundaries using interference testing. When the ratio rz/r1 is greater than 5, existingmethods of analysis may be used. For ratios of rz/rl smaller than 5, special considerations are needed. When the ratio of rz/rl is smaller than 2, there is no significant indication of the presence of a linear boundary in the pressureresponse. The effects of missing pressure data during the early time flow period, and earth tides on the linear boundary analysis are described and demonstrated with a flow test in the Ohaaki geothermal field in New Zealand.

Data from an interference test performed in the Ohaaki geothermal field hetween wells Br13 and Br'23 in December 1979 are analysed. The drawdown and buildup portions of the test are analysed using log-log and semi-log curve matching techniques. Horner semi-log type curve matching is also used in the analysis of the buildup portion of the data. Results indicate the presence of a no flow boundary for whicli the inference ellipse has been located. A pressure support boundary is indicated at greater distance than the no flow boundary.

Geospatial file for PA Municipality Boundaries

The global boundary scan testing service market is projected to reach $1.2 billion by 2033, with a CAGR of 5.6% from 2025 to 2033. This growth can be attributed to the increasing demand for electronic devices, the growing trend of miniaturization in the electronics industry, and the need for efficient and reliable testing methods for complex electronic systems. The chip application segment is expected to hold the largest market share over the forecast period due to the increasing use of chips in various electronic devices. North America is expected to be the largest regional market, with the United States being the major contributor to the region's growth. The growth in this region is attributed to the presence of major electronics manufacturers and the increasing adoption of advanced testing technologies. Key trends in the boundary scan testing service market include the increasing adoption of cloud-based testing services, the growing demand for automated testing solutions, and the emergence of new testing technologies such as artificial intelligence (AI) and machine learning (ML). These trends are expected to drive market growth in the coming years. However, the market faces certain restraints, such as the high cost of testing equipment and the lack of skilled technicians. Key companies operating in the boundary scan testing service market include GÖPEL electronic, JTAG Technologies, Test Coach, Global Electronics Testing Services, TT Electronics, Surmotech, QueteQ, M.I.S. Electronics, Acculogic, and Datest. These companies are focusing on developing new technologies, expanding their geographic reach, and offering customized solutions to meet the evolving needs of their customers.

This dataset is used in the paper Analyzing LLMs' Knowledge Boundary Cognition Across Languages Through the Lens of Internal Representations. It contains questions and responses in multiple languages to analyze how LLMs recognize knowledge boundaries.

This layer shows population broken down by race and Hispanic origin. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the predominant race living within an area. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B03002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Several studies have explored the relationship between socially constructed neighbourhood boundaries (henceforth social boundaries) and ethnic tensions. To measure these relationships, studies have used area-level demographic data to predict the location of social boundaries and their characteristics. The most common approach uses areal wombling to locate neighbouring areas with large differences in residential characteristics. Areas with large differences (or higher boundary values) are used as a proxy for well-defined social boundaries. However, to date, the results of these predictions have never been empirically validated. This article presents results from a simple discrete choice experiment designed to test whether the areal wombling approach to boundary detection produces social boundaries that are recognisable to local residents and experts as such. We conducted a small feasibility trial with residents and experts in Rotherham, England. Our results shows that participants were more likely to recognise boundaries with higher boundary values as local community borders. We end with a discussion on the scalability of the design and suggest future improvements.

Morven_Site_A from RAS

The Boundary Scan Tester market is experiencing steady growth, with a market size of $26.9 million in 2025 and a projected Compound Annual Growth Rate (CAGR) of 3.5% from 2025 to 2033. This growth is driven by the increasing complexity of electronic systems, the rising demand for higher quality control in manufacturing, and the growing adoption of automated testing solutions across various industries, including automotive, aerospace, and consumer electronics. Key players like Keysight, Agilent Technologies, Teradyne, and JTAG Technologies are shaping the market landscape through continuous innovation in test methodologies and hardware capabilities. The market is segmented by various factors, including testing type (functional, in-circuit, etc.), application (consumer electronics, automotive, etc.), and geographic location. The increasing adoption of Industry 4.0 principles and the rising demand for faster and more efficient testing solutions are further propelling market expansion. Despite the positive growth trajectory, challenges exist. These include the high initial investment costs associated with implementing boundary scan testing solutions, the need for specialized expertise to operate and maintain these systems, and the potential for incompatibility issues between different testing equipment. However, the long-term benefits of improved product quality, reduced production costs, and enhanced product reliability outweigh these challenges. This is encouraging significant investment and innovation within the sector, and continued market expansion is expected throughout the forecast period. The rising adoption of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to improve testing efficiency and accuracy will also play a crucial role in shaping future market dynamics.

We consider a confirmatory clinical trial where a primary and a secondary endpoints are tested hierarchically to control the family-wise error rate at level α. The trial uses a group sequential design with an interim and a final analysis. When the information times at the interim analysis are the same for the primary and the secondary hypotheses, it has been shown in the literature that the secondary hypothesis has to be tested using a group sequential boundary at a level no more than α. In many event-driven trials, however, the information times are usually different because of different event rates for the two endpoints. The information times may also be different for a noninferiority hypothesis and a superiority hypothesis due to different analysis sets. We consider this general setup and derive a sharp upper bound on the probability of rejecting the secondary hypothesis at an interim and a final analysis. This bound suggests that the secondary boundary can be refined so that the group sequential design can be tested at a significance level greater than α while still controlling the family-wise error rate at level α. We carry out a simulation study to illustrate the power gain by using the refined boundary for different choices of boundaries for the two hypotheses. The proposed approach is illustrated in two oncology clinical trials with more than two analyses.

Matching file for applying coordinates to districts when crating boundary maps in Socrata. See first two columns for corrections to districts where calculated centroid is not within boundaries.