User's guide for the Littoral Environment Observation (LEO) PC Data Retrieval and Analysis System /

"August 1994."

National Education Longitudinal Study of 1988 : first follow-up : student component data file user's manual /

"May 1992."

Highway user fees: updated data needed to determine whether all users pay their fair share.

Mode of access: Internet.

Wet weather highway accident analysis and skid resistance data management system. Volume 2: user's manual. Final report.

Louisiana Transportation Research Center, Baton Rouge

Management information systems : user's guide for indentifying automatic data processing equipment (ADPE) requirements.

Issued Feb. 23, 1976.

The documentation of clinical psychotropic drug trials; [a workbook and user's guide to understanding and interpreting the data processing and output of the Biometric Laboratory Information Processing System (BLIPS), George Washington University, and the Early Clinical Drug Evaluation Unit (ECDEU), NIMH.

On cover of v. 2: Early clinical drug evaluation units, analyses.

Meteorological data for radiological defense : user's manual.

July 1970.

What is user friendly? : Clinic on Library Applications of Data Processing, 1986 /

Includes bibliographies and index.

Data user news /

Latest issue consulted: Vol. 16, no. 12 (Dec. 1981).

Ex ante evaluations of alternate data structures for end user queries: Theory and experimental test

The data structure of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. This research develops a methodology for evaluating, ex ante, the relative desirability of alternative data structures for end user queries. This research theorizes that the data structure that yields the lowest weighted average complexity for a representative sample of information requests is the most desirable data structure for end user queries. The theory was tested in an experiment that compared queries from two different relational database schemas. As theorized, end users querying the data structure associated with the less complex queries performed better Complexity was measured using three different Halstead metrics. Each of the three metrics provided excellent predictions of end user performance. This research supplies strong evidence that organizations can use complexity metrics to evaluate, ex ante, the desirability of alternate data structures. Organizations can use these evaluations to enhance the efficient and effective retrieval of information by creating data structures that minimize end user query complexity.

DVMS 1.5: A user manual (the data visualization and modeling system)

The data available during the drug discovery process is vast in amount and diverse in nature. To gain useful information from such data, an effective visualisation tool is required. To provide better visualisation facilities to the domain experts (screening scientist, biologist, chemist, etc.),we developed a software which is based on recently developed principled visualisation algorithms such as Generative Topographic Mapping (GTM) and Hierarchical Generative Topographic Mapping (HGTM). The software also supports conventional visualisation techniques such as Principal Component Analysis, NeuroScale, PhiVis, and Locally Linear Embedding (LLE). The software also provides global and local regression facilities . It supports regression algorithms such as Multilayer Perceptron (MLP), Radial Basis Functions network (RBF), Generalised Linear Models (GLM), Mixture of Experts (MoE), and newly developed Guided Mixture of Experts (GME). This user manual gives an overview of the purpose of the software tool, highlights some of the issues to be taken care while creating a new model, and provides information about how to install & use the tool. The user manual does not require the readers to have familiarity with the algorithms it implements. Basic computing skills are enough to operate the software.

miniDVMS v1.8 : A user manual(The data visualization and modeling system)

Today, the data available to tackle many scientific challenges is vast in quantity and diverse in nature. The exploration of heterogeneous information spaces requires suitable mining algorithms as well as effective visual interfaces. miniDVMS v1.8 provides a flexible visual data mining framework which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualisation domain. The advantage of this interface is that the user is directly involved in the data mining process. Principled projection methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), are integrated with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates, and user interaction facilities, to provide this integrated visual data mining framework. The software also supports conventional visualisation techniques such as principal component analysis (PCA), Neuroscale, and PhiVis. This user manual gives an overview of the purpose of the software tool, highlights some of the issues to be taken care while creating a new model, and provides information about how to install and use the tool. The user manual does not require the readers to have familiarity with the algorithms it implements. Basic computing skills are enough to operate the software.

GEO Label:user and producer perspectives on a label for geospatial data

One of the aims of the Science and Technology Committee (STC) of the Group on Earth Observations (GEO) was to establish a GEO Label- a label to certify geospatial datasets and their quality. As proposed, the GEO Label will be used as a value indicator for geospatial data and datasets accessible through the Global Earth Observation System of Systems (GEOSS). It is suggested that the development of such a label will significantly improve user recognition of the quality of geospatial datasets and that its use will help promote trust in datasets that carry the established GEO Label. Furthermore, the GEO Label is seen as an incentive to data providers. At the moment GEOSS contains a large amount of data and is constantly growing. Taking this into account, a GEO Label could assist in searching by providing users with visual cues of dataset quality and possibly relevance; a GEO Label could effectively stand as a decision support mechanism for dataset selection. Currently our project - GeoViQua, - together with EGIDA and ID-03 is undertaking research to define and evaluate the concept of a GEO Label. The development and evaluation process will be carried out in three phases. In phase I we have conducted an online survey (GEO Label Questionnaire) to identify the initial user and producer views on a GEO Label or its potential role. In phase II we will conduct a further study presenting some GEO Label examples that will be based on Phase I. We will elicit feedback on these examples under controlled conditions. In phase III we will create physical prototypes which will be used in a human subject study. The most successful prototypes will then be put forward as potential GEO Label options. At the moment we are in phase I, where we developed an online questionnaire to collect the initial GEO Label requirements and to identify the role that a GEO Label should serve from the user and producer standpoint. The GEO Label Questionnaire consists of generic questions to identify whether users and producers believe a GEO Label is relevant to geospatial data; whether they want a single "one-for-all" label or separate labels that will serve a particular role; the function that would be most relevant for a GEO Label to carry; and the functionality that users and producers would like to see from common rating and review systems they use. To distribute the questionnaire, relevant user and expert groups were contacted at meetings or by email. At this stage we successfully collected over 80 valid responses from geospatial data users and producers. This communication will provide a comprehensive analysis of the survey results, indicating to what extent the users surveyed in Phase I value a GEO Label, and suggesting in what directions a GEO Label may develop. Potential GEO Label examples based on the results of the survey will be presented for use in Phase II.

Spatio-temporal techniques for user identification by means of GPS mobility data

One of the greatest concerns related to the popularity of GPS-enabled devices and applications is the increasing availability of the personal location information generated by them and shared with application and service providers. Moreover, people tend to have regular routines and be characterized by a set of “significant places”, thus making it possible to identify a user from his/her mobility data. In this paper we present a series of techniques for identifying individuals from their GPS movements. More specifically, we study the uniqueness of GPS information for three popular datasets, and we provide a detailed analysis of the discriminatory power of speed, direction and distance of travel. Most importantly, we present a simple yet effective technique for the identification of users from location information that are not included in the original dataset used for training, thus raising important privacy concerns for the management of location datasets.