On Monday I was at the BioLINK Special Interest Group at the Intelligent Systems for Molecule Biology meeting in Stockholm. Amongst the many thought-provoking talks was one by Phil Bourne, he of the Protein Data Bank, SciVee and other goodies. Phil made a cogent plea for a system of unique identifiers for scientists.
On Monday I was at the BioLINK Special Interest Group at the Intelligent Systems for Molecule Biology meeting in Stockholm. Amongst the many thought-provoking talks was one by Phil Bourne, he of the Protein Data Bank, SciVee and other goodies. Phil made a cogent plea for a system of unique identifiers for scientists.
We and our partners at the U.S National Cancer Institute recently had an article describing our Pathway Interaction Database accepted by Nucleic Acids Research. I’m not posting to puff that up: during the submission process, the NAR editors raised a couple of perfectly reasonable questions about preprints and unique identifiers.
We had previously put a preprint of the article in Nature Precedings. We’ve now updated that article to point to the final peer-reviewed version — and, as you’d hope, the NAR editors were happy with that. There’s a general point in that:
we encourage all authors to update their articles in Nature Precedings to point to new versions subsequently published elsewhere.
The NAR editors also raised a question about unique identifiers. Many journal publishers assign CrossRef-managed Digital Object Identifiers (DOIs) to published articles. The DOI for our NAR article, for example, is 10.1093/nar/gkn653. In Nature Precedings, we also assign unique identifiers but have chosen those managed by Handle.net to avoid any complications/confusion caused by two versions of the same article having different or the same DOI. The Handle for our Nature Precedings article, then, is 10101/npre.2008.2243.1.
I think a large fraction of the scientific research community doesn’t know about DOIs and other unique identifiers. They prefer to stick to traditional journal citations and PubMed IDs — either not knowing or not caring that PubMed IDs aren’t, strictly speaking, a stable identifier for the actual research article. DOIs are now a standard of the publishing industry, and authors are increasingly likely to need to understand what they are and how to use them.
We and our partners at the U.S National Cancer Institute recently had an article describing our Pathway Interaction Database accepted by Nucleic Acids Research. I’m not posting to puff that up: during the submission process, the NAR editors raised a couple of perfectly reasonable questions about preprints and unique identifiers.
We had previously put a preprint of the article in Nature Precedings. We’ve now updated that article to point to the final peer-reviewed version — and, as you’d hope, the NAR editors were happy with that. There’s a general point in that:
we encourage all authors to update their articles in Nature Precedings to point to new versions subsequently published elsewhere.
The NAR editors also raised a question about unique identifiers. Many journal publishers assign CrossRef-managed Digital Object Identifiers (DOIs) to published articles. The DOI for our NAR article, for example, is 10.1093/nar/gkn653. In Nature Precedings, we also assign unique identifiers but have chosen those managed by Handle.net to avoid any complications/confusion caused by two versions of the same article having different or the same DOI. The Handle for our Nature Precedings article, then, is 10101/npre.2008.2243.1.
I think a large fraction of the scientific research community doesn’t know about DOIs and other unique identifiers. They prefer to stick to traditional journal citations and PubMed IDs — either not knowing or not caring that PubMed IDs aren’t, strictly speaking, a stable identifier for the actual research article. DOIs are now a standard of the publishing industry, and authors are increasingly likely to need to understand what they are and how to use them.
Yesterday we launched a new structural biology website, the PSI-Nature Structural Genomics Knowledgebase.
The site is a great addition to our existing collection of Gateways and Databases. The project is a collaboration with the Protein Structure Initiative, a large scale NIH-funded consortium to develop and apply high-throughput techniques for protein structure determination. They’ve been highly successful in generating new technologies that are available for others to use, and they’ve shown that structure determination work can be scaled up significantly.
Now that the site is launched, we’ll be providing monthly editorial updates that put developments in structural work into context for a wide range of biomedical researchers — for a little more about that read our press release.
The website is hosted at Rutgers University by the same team that hosts one of most significant and long-established databases, the Protein Data Bank, and we’re very pleased to be working them.
Yesterday we launched a new structural biology website, the PSI-Nature Structural Genomics Knowledgebase.
The site is a great addition to our existing collection of Gateways and Databases. The project is a collaboration with the Protein Structure Initiative, a large scale NIH-funded consortium to develop and apply high-throughput techniques for protein structure determination. They’ve been highly successful in generating new technologies that are available for others to use, and they’ve shown that structure determination work can be scaled up significantly.
Now that the site is launched, we’ll be providing monthly editorial updates that put developments in structural work into context for a wide range of biomedical researchers — for a little more about that read our press release.
The website is hosted at Rutgers University by the same team that hosts one of most significant and long-established databases, the Protein Data Bank, and we’re very pleased to be working them.
This week’s issue of Nature is a special on Big Data. It has a good (we think) collection of pieces about the excitement, opportunities and challenges of massive data growth.
This week’s issue of Nature is a special on Big Data. It has a good (we think) collection of pieces about the excitement, opportunities and challenges of massive data growth.
A couple of days ago Barend Mons and colleagues published an article in Genome Biology about WikiProteins – a new way of asking a “million minds to annotate a million [biomedical] concepts”. On the face of it, it seems like an fine idea: combine text mining and other database trawling (Medline, GO, UniProt and others), distill some concept maps from that (Knowlets, they call them) and invite scientists to chip in via a wiki.
A couple of days ago Barend Mons and colleagues published an article in Genome Biology about WikiProteins – a new way of asking a “million minds to annotate a million [biomedical] concepts”. On the face of it, it seems like an fine idea: combine text mining and other database trawling (Medline, GO, UniProt and others), distill some concept maps from that (Knowlets, they call them) and invite scientists to chip in via a wiki.