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39 results for “POIs”
Values of the reference prior for the Poi(s+b) model from JINST 7 (2012) P01012
<p>Plain text table with the values of the reference prior π(s) for the Poi(s+b) model used in the statistical inference about counting experiments, as explained in JINST 7 (2012) P01012, doi:10.1088/1748-0221/7/01/P01012, http://arxiv.org/abs/1108.4270. The values are useful to find approximate expressions which are quicker to compute than the original prior, as explained in http://arxiv.org/abs/1407.5893 (where this dataset is referred to).</p> <p>Each line is a sequence of spaces-separated values, and the file can be considered a table. The first line starts with two strings "shape" and "rate" which represent the titles of the corresponding columns in the data table. They refer to the shape and rate parameters defining the background prior. Next, N signal values starting from s=0 to s=70 are reported. They are the values at which π(s) is computed for any subsequent line.</p> <p>Starting from the second line, the format is always the same. The first two values are the shape and rate parameters defining the background prior used to compute π(s) in this line. Next, the N values π(s=0), ..., π(s=70) are reported. As π(0) = 1, the third column is constant (it might be useful to debug the data reading).</p> <p>As explained in http://arxiv.org/abs/1108.4270, simple functional forms may be used to fit the N points (s, π(s)). As the shape and rate parameters from the user's application may be different from those reported in this table, the following procedure shall give a very good approximation to π(s). In the (log(shape), log(rate)) parameters space, locate the neighboring points to the user's background parameter values (in log-log scale). Then interpolate each of the π(s) values to obtain a set of N values (a linear interpolation in log-log scale shall be sufficient). Finally, fit these interpolated values to find the reference prior for the user's application.</p>
A Standardized European Hexagon Gridded Dataset Based on OpenStreetMap POIs
<p>Point of interest (POI) data refers to information about the location and type of amenities, services, and attractions within a geographic area. This data is used in urban studies research to better understand the dynamics of a city, assess community needs, and identify opportunities for economic growth and development. POI data is beneficial because it provides a detailed picture of the resources available in a given area, which can inform policy decisions and improve the quality of life for residents. This paper presents a large-scale, standardized POI dataset from OpenStreetMap (OSM) for the European continent. The dataset's standardization and gridding make it more efficient for advanced modeling, reducing 7,218,304 data points to 988,575 without significant resolution loss, suitable for a broader range of models with lower computational demands. The resulting dataset can be used to conduct advanced analyses, examine POI spatial distributions, conduct comparative regional studies, enhancing understanding of the economic activity, distribution, attractions, and subsequently, economic health, growth potential, and cultural opportunities. The paper describes the materials and methods used in generating the dataset, including OSM data retrieval, processing, standardization, and hexagonal grid generation. The dataset can be used independently or integrated with other relevant datasets for more comprehensive spatial distribution studies in future research.</p>
poi
<p>This dataset uses the CK OO metrics.</p> <p>More information at http://openscience.us/repo/defect/ck/poi.html</p>
Mapillary POI-Neighborhood Street-Level Images (MPOINSLI)
<p><em><strong>Dataset Name:</strong></em> MPOINSLI Mapillary POI-Neighborhood Street-Level Images </p> <blockquote> <p>This is a repository of Mapillary street-view images of New York City that include any portion of POIs in their field of view. The repository is the outcome of a paper, the abstract of which is provided below. Please use the below citatin for using this dataset:</p> <p> </p> </blockquote> <p><strong>Citation: </strong></p> <p>N. Zarbakhsh and G. McArdle, "Points-of-Interest from Mapillary Street-level Imagery: A Dataset For Neighborhood Analytics," 2023 IEEE 39th International Conference on Data Engineering Workshops (ICDEW), Anaheim, CA, USA, 2023, pp. 154-161, doi: 10.1109/ICDEW58674.2023.00030.</p> <p><strong>Abstract: </strong></p> <p>The Sustainable Development Goals of the United Nations promote sustainable urban development to make cities more economically and socially liveable. Points of Interest (POIs) such as commercial properties and healthcare facilities are significant markers for these goals. Street-view images are becoming increasingly important for capturing cities' streetscapes. Existing studies provide city-level images, while there are few studies that provide images in the vicinity of certain POIs. Therefore, this paper develops a framework for filtering images so that a portion of a given POI is visible in their field of view (FOV). We contribute with Mapillary POI-Neighborhood Street-Level Images (MPOINSLI) dataset, a large street-view image of POIs and their neighborhood in New York City. First, all the images within a 35-meter radius of certain POIs are filtered. Then, the intersection technique is utilized to determine if the cameras' FOV triangular polygons intersect the POIs' polygons. Using 11,126 POIs from SafeGraph's Geometry and Place datasets in conjunction with 875,592 Mapillary images, we demonstrate the effectiveness of our approach. MPOINSLI contains 167,743 Mapillary street-view images of 6,732 unique POIs, defined by the standard identifiers (Placekeys) which are further classified into 23 general functionalities categories (top-categories) and 67 more specific categories (sub-categories) of the POIs. MPOINSLI provides an open-source repository that contains metadata such as raw and post-processed camera-related parameters, the Harvesian distance between the camera and the POI's coordinates, and the intersection area. MPOINSLI could provide promising future applications for both smart cities and computer vision, including scene recognition across POI neighborhoods and fine-grained land-use classification.</p>
Grid-based dataset of Barcelona POIs
<p>This is a processed dataset, generated by a) a set of datasets collected from the open portal of Barcelona city containing points-of-interest and b) image of Barcelona city collected from Copernicus Access Hub. </p> <p>The dataset contains a series of csv files, where each csv file describes a grid of square cells of different lengths (1, 2, 5, 10km). Each cell corresponds to a geographical area in the city, with the following attributes:</p> <ul> <li>Geometry, describing the spatial dimension of the cell</li> <li>min, max, avg, sum kurtosis, skewness, stddev, variation of the number of a given POI, such as bike stations, zoos, participation spaces, etc.</li> <li>not_vegetation, percentage of space in the cell with no vegetation</li> <li>vegetation, percentage of space in the cell with vegetation</li> <li>water, percentage of space in the cell with water</li> <li>other, percentage of space in the cell with other than vegetation, water</li> </ul>
Grid-based dataset of Paris-Saclay POIs
<p>This is a processed dataset, generated by a) a set of datasets collected from the open portal of Paris-Saclay area containing points-of-interest and b) image of Paris-Saclay collected from Copernicus Access Hub. </p> <p>The dataset contains a series of csv files, where each csv file describes a grid of square cells of different lengths (1, 2, 3, 5, 10km). Each cell corresponds to a geographical area in the city, with the following attributes:</p> <ul> <li>Geometry, describing the spatial dimension of the cell</li> <li>min, max, avg, sum kurtosis, skewness, stddev, variation of the number of a given POI, such as sports areas, military areas, bus stops, etc.</li> <li>not_vegetated, percentage of space in the cell with no vegetation</li> <li>vegetation, percentage of space in the cell with vegetation</li> <li>water, percentage of space in the cell with water</li> <li>other, percentage of space in the cell with other than vegetation, water</li> </ul>
Austin POIs and Geographic Features Dataset
<p>This dataset includes points of interest (POIs) along with their corresponding geographic features extracted from OpenStreetMap (OSM), as well as urban zone data specific to the city of Austin, Texas, USA. It comprises tables that associate POIs with their surrounding geographic features and others that link POIs and features to urban zones within the dataset. These tables are designed for academic research and are compatible with PostgreSQL for efficient data management and analysis.</p>
POI comments validation dataset
<p>This is the dataset produced during the validation of the Viarota scenario for analysing feedback with respect to specific POIs from certain user groups. We will collect and process online data, regarding the RADON validation activities for processing anonymised opinions with respect to experiences when receiving the services offered in a certain POI.</p>
Wikidata Dump location poi
<p>RDF dump of wikidata produced with <a href="//wdumps.toolforge.org/">wdumper</a>.</p><p><br><a href="//wdumps.toolforge.org/dump/2799">View on wdumper</a></p><p><b>entity count<b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0</b></b></p>
POIs from Open Nature Innovation Arena (Open311 standard)
<p>This dataset has been generated from the Open Nature Innovation Arena according to the Open311 standard. It contains all the point of interests registered from the users into the Open Nature Innovation Arena to publish problems and challenges.</p>
POI-based land use map for Africa
<p>The combination of spatial distribution, semantic characteristics, and sometimes temporal dynamics of POIs inside a geographic region can capture its unique land use characteristics. We developed a scalable POI-based land use modeling framework. By combining POIs with a neural network language model, we developed a spatially explicit approach to learn the embedding representation of POIs and AOIs. We trained supervised classifiers using AOI embeddings as input features to predict AOI land use at different semantic granularities. </p>
Study of Intravenous (IV) Methylnaltrexone Bromide (MNTX) in the Treatment of Post-Operative Ileus (POI)
ClinicalTrials.gov study NCT00401375. IPD Sharing: Not stated. Countries: 1. Publications: 1.
POI-based land use map for Africa
Open the record for dataset details and reuse information.
Apache POI pre-processed data for the first DocGen challenge at DySDoc 3
<p>Apache POI pre-processed data for the first DocGen challenge</p> <p>The pre-processed data for First Software Documentation Generation Challenge (DocGen), hosted at the <a href="https://dysdoc.github.io/">Third International Workshop on Dynamic Software Documentation (DySDoc 3)</a>, includes the following datasets for <a href="https://poi.apache.org/">Apache POI 3.17</a>:</p> <p><strong>Call graph between method and classes.</strong></p> <p>File: call-graph-poi-3.17-all.zip</p> <p>CSV file with the call graph between methods and between classes. Class A calls class B if there exists a call between amethod of class A and a method of class B. The call graph was produced by the tool <a href="https://github.com/gousiosg/java-callgraph/">java-callgraph</a>. </p> <p>The CSV file contains the following columns:</p> <ul> <li>call_type: call between (C)lasses or (M)ethods</li> <li>caller: the Fully Qualified Name (FQN) of the caller</li> <li>method_call_type: the type of method call: <ul> <li>M for invokevirtual calls</li> <li>I for invokeinterface calls</li> <li>O for invokespecial calls</li> <li>S for invokestatic calls</li> <li>D for invokedynamic calls</li> </ul> </li> <li>callee: the FQN of the callee</li> </ul> <p>For more details about the format and each type of method call, check the tool <a href="https://github.com/gousiosg/java-callgraph/">README</a>.</p> <p><strong>Inheritance hierarchy</strong></p> <p>File: poi-3.17-inheritance.zip</p> <p>A CSV file with the inheritance hierarchy of POI, which was extracted using bcel 6.2</p> <p>The CSV file contains the following columns:</p> <ul> <li>record_id: sequential number</li> <li>parent_class: the parent class</li> <li>child_class: the child class</li> <li>relationship_type: the type of relationship between classes, i.e., the child class 'extends' or 'implements' the parent class</li> </ul> <p><strong>Issues</strong></p> <p>File: bugzilla-poi-dump.zip</p> <p>CSV file with the list of issues of <a href="https://bz.apache.org/bugzilla/buglist.cgi?product=POI">Apache POI</a> (timestamp: Tue Feb 27, 2018, 18.41.40 UTC)</p> <p>The CSV file contains the following columns:</p> <ul> <li>record_id: sequential number</li> <li>issue_id: the ID that identifies the issue in the issue tracker</li> <li>issue_url: the URL of the issue in the issue tracker</li> <li>issue_title: the title of the issue</li> <li>xml_path: the path to the XML of the issue, which contains all the issue information provided by the issue tracker</li> </ul> <p>All the issues in XML format can be found in the "poi" folder in the ZIP file</p> <p><strong>Commits</strong></p> <p>File: poi-commits.zip</p> <p>A JSON file with commit information for POI 3.17 (until revision 219dff00e6, on Sept. 8, 2017). The information was extracted using the tools <a href="https://dl.acm.org/citation.cfm?doid=2024445.2024463">Historage</a> and <a href="https://dl.acm.org/citation.cfm?doid=2597073.2597125">Kataribe</a>.</p> <p>For each commit, we provide:</p> <ul> <li>Commit hash</li> <li>Parent commit hash (if exists)</li> <li>Commit message</li> <li>Commit time</li> <li>Committer name</li> <li>Method-level changes (addition/deletion/modification/renaming and method FQN). <ul> <li>The FQN contains information about the class (CN) and method (MT) or constructor (CS)</li> </ul> </li> </ul> <p><strong>StackOverflow posts</strong></p> <p>File: apache-poi-SO.zip</p> <p>JSON file with all 6,299 Stack Overflow threads with the <code>apache-poi</code> tag,</p>
Simo Decoction and Acupuncture on POI in Colorectal Cancer
ClinicalTrials.gov study NCT02813278. IPD Sharing: NO. Countries: 1. Publications: 4.
Autologous ADMSC Transplantation in Patients With POI
ClinicalTrials.gov study NCT06132542. IPD Sharing: NO. Countries: 1. Publications: 10.
A Study Evaluating Intravenous (IV) MOA-728 for the Treatment of Postoperative Ileus (POI) in Participants After Ventral Hernia Repair
ClinicalTrials.gov study NCT00528970. IPD Sharing: Not stated. Countries: 11. Publications: 0.
High resolution X chromosome copy number analysis in fertile and POI females
GEO Series GSE127453. Homo sapiens. 369 samples. Type: Genome variation profiling by array.
Self-actualisation data_POI_and_interviews
<p>The two files include</p> <p>1) POI database pre- and post- intervention</p> <p>2) Interviews</p>
Use of Beetroot Juice to Protect Against Postoperative Ileus (POI) Following Colorectal Surgery: a Pilot Study.
ClinicalTrials.gov study NCT03772444. IPD Sharing: NO. Countries: 1. Publications: 0.
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