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| Heather A. Piwowar; Douglas B. Fridsma. |
| Background
 Many initiatives and repositories exist to encourage the sharing of research data, and thousands of microarray gene expression datasets are publicly available. Many studies reuse this data, but it is not well understood which datasets are reused and for what purpose.

 Materials and Methods
 We trained a machine-learning algorithm to automatically classify full-text gene expression microarray studies into two classes: those that generated original microarray data (n=900) and those which only reused data (n=250). We then compared the Medical Subject Heading (MeSH) terms of two classes to identify MeSH topics which were over- or under-represented by publications with... |
| Tipo: Poster |
Palavras-chave: Bioinformatics. |
| Ano: 2007 |
URL: http://precedings.nature.com/documents/425/version/1 |
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| Heather A. Piwowar; Douglas B. Fridsma. |
| Does your research area re-use shared datasets?
* Re-using data has many benefits, including research synergy and efficient resource use
* Some research areas have tools, communities, and practices which facilitate re-use
* Identifying these areas will allow us to learn from them, and apply the lessons to areas which underutilize the sharing and re-purposing of scientific data between investigators

 Which datasets?
This preliminary analysis examines the re-use of microarray gene expression datasets. Thousands of microarray gene expression datasets have been deposited in publicly available databases. 
Many studies reuse this data,... |
| Tipo: Poster |
Palavras-chave: Bioinformatics. |
| Ano: 2007 |
URL: http://precedings.nature.com/documents/425/version/3 |
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| Heather A. Piwowar; Douglas B. Fridsma. |
| Background
 Many initiatives and repositories exist to encourage the sharing of research data, and thousands of microarray gene expression datasets are publicly available. Many studies reuse this data, but it is not well understood which datasets are reused and for what purpose.

 Materials and Methods
 We trained a machine-learning algorithm to automatically classify full-text gene expression microarray studies into two classes: those that generated original microarray data (n=900) and those which only reused data (n=250). We then compared the Medical Subject Heading (MeSH) terms of two classes to identify MeSH topics which were over- or under-represented by publications with... |
| Tipo: Poster |
Palavras-chave: Bioinformatics. |
| Ano: 2007 |
URL: http://precedings.nature.com/documents/425/version/2 |
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