Name
Title | ||
|---|---|---|
Data rescue archive weather (DRAW): Preserving the complexity of historical climate data. |
Abstract | ||
|---|---|---|
PurposeTo rescue at-risk historical scientific data stored at the McGill Observatory, the objectives of the Data Rescue Archive Weather (DRAW) project are: to build a repository; to develop a protocol to preserve the data in weather registers; and to make the data available to research communities and the public. The paper aims to discuss these issues.Design/methodology/approachThe DRAW project adopts an open archive information system compliant model as a conceptual framework for building a digital repository. The model consists of data collection, conversion, data capture, transcription, arrangement, description, data extraction, database design and repository setup.FindingsA climate data repository, as the final product, is set up for digital images of registers and a database is designed for data storage. The repository provides dissemination of and access to the data for researchers, information professionals and the public.Research limitations/implicationsDoing a quality check is the most important aspect of rescuing historical scientific data to ensure the accuracy, reliability and consistency of data.Practical implicationsThe DRAW project shows how the use of historical scientific data has become a key element in research analysis on scientific fields, such as climatology and environmental protection.Originality/valueThe historical climate data set of the McGill Observatory is by nature unique and complex for preservation and research purposes. The management of historical scientific data is a challenge to rescue and describe as a result of its heterogeneous and non-standardized form. |
Year | Venue | Field |
|---|---|---|
2018 | Journal of Documentation | Data science,Information system,Metadata,Data collection,Information retrieval,Computer science,Database design,Information repository,Automatic identification and data capture,Data extraction,Conceptual framework |
DocType | Volume | Issue |
Journal | 74 | 4 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eun G. Park | 1 | 30 | 6.66 |
| Gordon Burr | 2 | 0 | 0.34 |
| Victoria Slonosky | 3 | 0 | 0.34 |
| renee e sieber | 4 | 33 | 2.85 |
| Lori Podolsky | 5 | 0 | 0.34 |