Abstract
in large corpuses of metadata harvested under Open Archives Initiative (OAI) protocols. Along with some discussion of why and how metadata quality is important, an approach to conceptualizing, measuring, and assessing metadata quality is presented. The approach given in this paper is based on a more general model of information quality (IQ) for many kinds of information beyond just metadata. A key feature of the general model is its ability to condition quality assessments by context of information use, such as the types of activities that use the information, and the typified norms and values of relevant information-using communities. The paper presents a number of statistical characterizations of analyzed samples of metadata from a large corpus built as part of the Institute of Museum and Library Services Digital Collections and Contents (IMLS DCC)2 project containing OAI-harvested metadata and links these statistical assessments to the quality measures, and interprets them. Finally the paper discusses several approaches to quality improvement for metadata based on the study findings.
| Original language | English (US) |
|---|---|
| Pages | 111-125 |
| State | Published - 2004 |
| Externally published | Yes |
| Event | Ninth International Conference on Information Quality - Massachusetts Institute of Technology, Cambridge, United States Duration: Nov 5 2004 → Nov 7 2004 |
Conference
| Conference | Ninth International Conference on Information Quality |
|---|---|
| Country | United States |
| City | Cambridge |
| Period | 11/5/04 → 11/7/04 |
Fingerprint
Keywords
- Information quality
- Metadata aggregation
ASJC Scopus subject areas
- Library and Information Sciences
Cite this
Metadata quality for federated collections. / Stvilia, Besiki; Gasser, Les; Twidale, Michael B.; Cole, Timothy W.; Shreeves, Sarah L.
2004. 111-125 Paper presented at Ninth International Conference on Information Quality, Cambridge, United States.Research output: Contribution to conference › Paper
}
TY - CONF
T1 - Metadata quality for federated collections
AU - Stvilia, Besiki
AU - Gasser, Les
AU - Twidale, Michael B.
AU - Cole, Timothy W.
AU - Shreeves, Sarah L.
PY - 2004
Y1 - 2004
N2 - This paper presents early results from our empirical studies of metadata qualityin large corpuses of metadata harvested under Open Archives Initiative (OAI) protocols. Along with some discussion of why and how metadata quality is important, an approach to conceptualizing, measuring, and assessing metadata quality is presented. The approach given in this paper is based on a more general model of information quality (IQ) for many kinds of information beyond just metadata. A key feature of the general model is its ability to condition quality assessments by context of information use, such as the types of activities that use the information, and the typified norms and values of relevant information-using communities. The paper presents a number of statistical characterizations of analyzed samples of metadata from a large corpus built as part of the Institute of Museum and Library Services Digital Collections and Contents (IMLS DCC)2 project containing OAI-harvested metadata and links these statistical assessments to the quality measures, and interprets them. Finally the paper discusses several approaches to quality improvement for metadata based on the study findings.
AB - This paper presents early results from our empirical studies of metadata qualityin large corpuses of metadata harvested under Open Archives Initiative (OAI) protocols. Along with some discussion of why and how metadata quality is important, an approach to conceptualizing, measuring, and assessing metadata quality is presented. The approach given in this paper is based on a more general model of information quality (IQ) for many kinds of information beyond just metadata. A key feature of the general model is its ability to condition quality assessments by context of information use, such as the types of activities that use the information, and the typified norms and values of relevant information-using communities. The paper presents a number of statistical characterizations of analyzed samples of metadata from a large corpus built as part of the Institute of Museum and Library Services Digital Collections and Contents (IMLS DCC)2 project containing OAI-harvested metadata and links these statistical assessments to the quality measures, and interprets them. Finally the paper discusses several approaches to quality improvement for metadata based on the study findings.
KW - Information quality
KW - Metadata aggregation
M3 - Paper
SP - 111
EP - 125
ER -