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| ==Research Plan== | | ==Aim== |
| ===Overview===
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| * Query GEO for all GDS and GDS accession numbers for datasets submitted in 2007
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| * Query PubMed Central for these accession numbers in the full text of PMC papers published between 1900 and 2009
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| * Enumerate the PMC papers that reused GEO data
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| * Estimate what percent of these papers depended on the GEO data for their scientific contribution
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| ===Query details=== | | ==Background== |
| * accession number formats:
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| ** look at both GSE and GDS accession numbers
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| ** use both the raw ID number like 200007572 and the stripped version without the 200... prefix. For example, search for both 200007572 and 7572
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| ** search for both accession number right beside the prefix, and with one space in between, so "GSE 7572" and "GSE7572"
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| ===Exclude data creation studies=== | | ==Methods== |
| * spot-check to make sure accession number is in the context of reuse... looks like there may be a few mentions in the context of depost in which the article is not tagged with pmc_gds[filter] (example: PMCID 2396644) | | ===Overview=== |
| ** do this for all the PMC article hits? looks like there are a few missing the filter, and it matters because it would erroneously inflate our reuse estimate | | * Using the method outlined at [[DataONE:Protocols/Find_GEO_reuses]]: |
| ** could use query from my BioLink paper:
| | ** Query GEO for all GDS and GDS accession numbers for datasets submitted in 2007 |
| (geo OR omnibus)
| | ** Query PubMed Central for these accession numbers in the full text of PMC papers published between 1900 and 2009 |
| AND microarray
| | ** Enumerate the PMC papers that reused GEO data |
| AND "gene expression"
| | ** Estimate what percent of these papers depended on the GEO data for their scientific contribution |
| AND accession
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| NOT (databases
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| OR user OR users
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| OR (public AND accessed)
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| OR (downloaded AND published))
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| ** or the more simple:
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| "gene expression omnibus” AND (submitted OR deposited)
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| ** to do this transparently, query PMC results for each of these words: | |
| *** submitted
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| *** deposited
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| *** user*
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| *** public
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| *** accessed
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| *** downloaded
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| *** published
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| ===Estimate time lag for reuse===
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| To estimate time lag:
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| * extract year
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| ===Estimate what percentage of reusers weren't the original authors===
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| * see if AND pubmed_gds and NOT pmc_gds have any author overlaps? (note AND should be pubmed!)
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| * other idea: institution comparison using medline info | |
| * better than submitter, because submitter not the whole story | |
| * better than institution, because institution not precise in submission | |
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| ===Estimate what percent of reuse created "new science"===
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| * classify if methods or informatics:
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| ** journal name has informatics
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| ** mesh term for methods?
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| * look at mesh overlap?
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| * look for metaanalysis mesh term? | |
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| ===Estimate what percent of these papers depended on the GEO data for their scientific contribution===
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| ** Any good ideas on how to do this efficiently?
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| *** find those which are/are not in informatics journals
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| *** that use "methods" MeSH terms
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| *** ??
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| ===Estimate the fraction of all papers that are in PMC===
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| * use "gene expression profiling"[mesh] query in PMC vs PubMed over time period in question to get relevant estimate
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| ** restrict from 2007 to 2009
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| ** result:
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| number of articles in PMC: 6311,
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| number of articles in PubMed: 21569,
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| so PMC contains 29.26% of related papers
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| * so we should multiply our number of scientific papers by about 3 to get estimate for all of scientific publishing
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| ==Limitations==
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| ===Important for argument===
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| This is a conservative estimate because:
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| * our estimates do not consider reuses after our study timeframe
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| ** many datasets we are considering will continue to be used in the future... these reuses are obviously not continued in our estimate
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| ** could estimate this impact if we examine data deposited 7 years ago?
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| * Many papers not in PubMed Central
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| ** using "gene expression profiling"[mesh] query in PMC vs PubMed over 2007-2009 suggests PMC contains 30% of all related papers in PubMed
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| * our methods do not find studies that both create and reuse data
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| ** to narrow down our query results, we automatedly eliminate studies that create data... even though these same studies may also reuse data
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| ** we don't have an estimate of how many this is, would require manual inventory
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| * Many data citations not attributed using accession numbers
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| ** don't have a good way to estimate this yet
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| ** would require a manual inventory, similar to Sarah's data citation inventory in DataONE summer 2010 project
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| ** maybe out-of-scope to get this estimate for this project, just admit it is an underestimate
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| ===Less important for argument===
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| * Doesn't capture reuse outside the peer-reviewed literature (for example, reuse during training)
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| * Deposits into PMC not stable over time, distribution may change over time
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| ==Additional uses for this data collection== | | ===Details=== |
| * could use this data to see how many publications use any one dataset | | * see [[DataONE:Protocols/Find_GEO_reuses]] |
| * could use this data to look at average elapsed time between data submission and reuse, but only have short time period to consider... better off with data deposited longer ago
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| * can't use this particular data to see how many datasets each publication uses, because only looking at datasets from a given year
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| ==Open Questions== | | ==Results== |
| None right now
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| ==Notes== | | ==Discussion== |
| * this query PMC full-text approach is similar (as per correspondence with GEO team) to that used by the GEO team to compile the 3rd party reuse page: http://www.ncbi.nlm.nih.gov/projects/geo/info/ucitations.html
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| * would be nice to figure out how to write all of these columns to google docs directly from code
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