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Related Concept Videos

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
Data Reporting and Recording01:24

Data Reporting and Recording

Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
Levels of Use of a GIS01:29

Levels of Use of a GIS

Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
Data: Types and Distribution01:19

Data: Types and Distribution

In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
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Manipulation and Analysis

GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...

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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

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Published on: November 22, 2019

Building a scientific data grid with DiGS.

Mark G Beckett1, Chris R Allton, Christine T H Davies

  • 1School of Physics and Astronomy, University of Edinburgh, Edinburgh EH9 3JZ, UK. george.beckett@ed.ac.uk

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|May 20, 2009
PubMed
Summary
This summary is machine-generated.

Scientific groups can manage petabytes of data using grid technology and the DiGS application. This infrastructure supports data sharing and curation for computational science.

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Area of Science:

  • Computational particle physics
  • Molecular biology
  • Scientific data infrastructure

Background:

  • Modern high-performance computing enables small scientific groups to generate massive datasets (petabytes).
  • Managing, publishing, sharing, and curating large scientific data volumes presents significant challenges.

Purpose of the Study:

  • To provide insights into building and supporting scientific data infrastructure.
  • To illustrate the use of grid technology for managing large scientific datasets.
  • To introduce the DiGS software application for smaller scientific communities.

Main Methods:

  • Leveraging experience with computational particle physics and molecular biology scientists.
  • Applying grid technology principles for data management.
  • Developing and describing the DiGS software application.

Main Results:

  • Demonstrated that even small groups can generate petabyte-scale data.
  • Highlighted the utility of grid technology for scientific data lifecycle management.
  • Detailed the key functionalities of the DiGS application.

Conclusions:

  • Effective scientific data infrastructure is crucial for modern research.
  • Grid technology offers a viable solution for managing large-scale scientific data.
  • The DiGS application provides a tailored solution for smaller scientific communities needing robust data management.