GBIF and impact: CrossRef, FundRef, and Altmetric

For anyone doing research or involved in scientific infrastructure, demonstrating the "impact" of those activities is becoming increasingly important. This has fostered a growth industry in "alt metrics", tools to track how research gathers attention outside academia (of course, we can argue whether attention is the same as impact).

For an organisation such as GBIF there's a clear need to show that it has impact on the field of biodiversity (and beyond), especially to its funders (which are ultimately national governments). To do this GBIF needs to track how its data is used by the research communities, both to do science and to inform policy. This is hard to do, especially if there's a limited culture of data citation. It occurs to me that another way to tackle this problem is to invert it by looking not at the impact of GBIF, but at GBIF as a source of impact.

For a moment let's replace GBIF with Wikipedia. We can ask "what is the impact of Wikipedia on the research community?" For example, Wikipedia is the 8th largest referrer of DOIs, which means that Wikipedia is a major source traffic to academic publishing sites. All those Wikipedia pages which cite the primary literature are driving traffic to those articles.

Conversely, if we regard Wikipedia as important we can use citations of articles in Wikipedia pages as a measure of a researcher's impact. For example, according to Impact story I am "Wikitastic" as 11 Wikipedia pages cite articles that I am an author of (authorship is discovered by using my ORCID 0000-0002-7101-9767).

Likewise, altmetric tracks citations on Wikipedia, so that a paper like the one below may have minimal social media impact but as the gray donut rings signifying that it's been cited on Wikipedia.

JENKINS, P. D., & ROBINSON, M. F. (2002, June). Another variation on the gymnure theme: description of a new species of Hylomys (Lipotyphla, Erinaceidae, Galericinae). Bulletin of The Natural History Museum. Zoology Series. Cambridge University Press (CUP) doi:10.1017/S0968047002000018

Hence, we can look at Wikipedia in two different ways. The first is to ask "what is the impact of Wikipedia?", the second is to assume that Wikipedia has impact, and then use that as one measure of the impact of researchers (how "Wikitastic" you are).

So, let's go back to GBIF. Imagine we leave aside the question of whether GBIF has impact and imagine that we can use GBIF as a measure of impact ("GBIFtastic", sorry, that was unforgivable).

Example 1: From DOI to FundRef to GBIF

In a previous post I discussed the lack of mosquito data in GBIF and how I plugged this gap by using open data cite by a paper in eLife. This paper has the DOI 10.7554/elife.08347 and if I plug that into CrossRef's search engine I can get back some information on the funders of that paper:

Research funded by Sir Richard Southwood Graduate Scholarship | Rhodes Scholarships | National Institutes of Health (RAPIDD program, R01-AI069341, R01-AI091980, R01-GM08322, N01-A1-25489) | Wellcome Trust (#095066, Vecnet, #099872) | National Aeronautics and Space Administration (#NNX15AF36G) | Biotechnology and Biological Sciences Research Council | Bill and Melinda Gates Foundation (#OPP1053338, #OPP52250) | Studienstiftung des Deutschen Volkes | Directorate-General for Research and Innovation (#21803) | European Centre for Disease Prevention and Control (ECDC/09/018)

Now, this gives me a connection between funding agencies, a paper they funded, and the data in GBIF. For example, the Bill and Melinda Gates Foundation (doi:10.13039/100000865) funded doi:10.7554/elife.08347 which generated data in GBIF doi:10.15468/7apj8n.

I suspect that the Bill and Melinda Gates Foundation don't know that they've funded data gathering that has ended up in GBIF, but I suspect they'd be interested. Especially if that could be quantified (een better if we can demonstrate reuse). The process of linking funders to data can be largely automated, especially as more and more papers are now automatically linked to funder information. The link between publications and data in GBIF can be harder to establish, but at least one publisher (Pensoft) has establish a direct feed from publication to GBIF.

So, what if GBIF could computationally discover the funders of the data it holds, and could then communicate that to the funders. I think there's scope here for funders to take an interest in GBIF and it's role in expanding the reuse (and hence impact) of data that funders have paid for. Demonstrating to governments that national funding agencies are supporting research that generates data that ends up in GBIF may help make the case that GBIF is worth supporting.

Example 2: GBIF as altmetric source

The little altmetric donuts that we see on papers require sources of data, such as Twitter, Wikipedia, blogs, etc. For example, the Plant List dataset I recently put into GBIF has a DOI (doi:10.15468/btkum2)and this has received some attention so it has a altimetric donut (wouldn't it be nice if GBIF showed these on dataset pages?):

What if GBIF itself became a source that altimetric scanned when measuring impact? What if having your papers mentioned in GBIF (for example, as a source of distributional data or a taxonomic name) contributed to the visible impact of that work. Wouldn't that encourage people to mobilise their data? Wouldn't that help people discover the wider conversation about the data and associated publications? Wouldn't that help generate more impact for papers that might otherwise gather less attention?

Summary

I realise that I've somewhat avoided the question of the impact of GBIF itself, which is something that also needs to be tackled (and this is one reason why GBIF assigns DOIs to datasets and downloads to support data citation), but I think that may be only a part of the bigger picture. If we assume GBIF is impactful to start with, then I think we can start to think how GBIF can help persuade researchers and funders that contributing to GBIF is a good thing.

The Biodiversity Heritage Library at 10: Let's talk impact interview by @UDCMRK

As part of BHL's "Celebrating 10 years of inspiring discovery through free access to biodiversity knowledge" at the NHM and Kew Gardens in London, I was interviewed by Martin Kalfatovic (@UDCMRK). We chatted about BHL, the work I've been doing on BioStor, and the future of BHL. I haven't had the courage to watch it myself, but if you want to watch an academic giving Roger Hyam a run for his money in the "flappy hands" stakes, and not knowing whether to look at the camera, Martin, or towards the distant horizon, then here is the video.

The impact of museum collections: one collection ≈ one Nobel Prize

iPhylo: GBIF specimens in BioStor: who are the top ten museums?(Unfortunately not @amnh) iphylo.blogspot.com/2012/02/gbif-s…

— Susan Perkins (@NYCuratrix) May 14, 2013

Ideas on measuring the "impact" of a natural history collection have been bubbling along, as reflected in recent comments on iPhylo, and some offline discussions I've been having with David Blackburn and Alan Resetar.

My focus has been at the specimen-level, with a view to motivation the adoption of persistent specimen-level identifiers so that we can citations of specimens over time (e.g., in publications and databases such as GenBank). Not only does this provide a measure of the "impact" of a collection, it helps with provenance. If we sequence a specimen that is subsequently assigend to a different taxon and we have a way of tracking that specimen via its identifier, then we can transmit that new identification to other consumers of data based on that specimen. For example, we could automatically notify GenBank that what we thought was an x is actually a y.

So I made a simple "league table" of museum collections based on specimens cited in BioStor. There are all sorts of issues with this approach. Once you rank collections, people may use that to argue some can be axed and more resources funnelled into others. A more positive approach would be to indetify collections that are underused, and try and figure out why. And in the same way that taxonomic papers may have a citation long life, specimens may sit in a museum for a long time before being cited (for example, when eventually recognised as a new species doi:10.1016/j.cub.2012.10.029). So, metrics can be a double-edged sword.

Citing specimens is a useful metric, but not all citations are equal, and not all citations are immediate. A specimen that yields DNA sequences that are published in, say, Nature, arguably has more weight than a specimen listed in a rarely cited paper. Likewise, subsequent citations of a paper that cites a specimen should confer more weight on the value of that specimen. Elsewhere (doi:10.1093/bib/bbn022, preprint here: hdl:10101/npre.2008.1760.1) I've argued for a Google PageRank-style way to measure the impact of a specimen that takes into account papers and other objects derived from a specimen (e.g., images, sequences).

Meanwhile, Morgan Jackson alerted me to a quicker way to get a measure of the impact of the collection.

@rdmpage @nycuratrix Check Nature for a recent note about this. A bird museum calc. their collection's h-score from papers citing specimens

— Morgan Jackson (@BioInFocus) May 16, 2013

The "short note" Morgan refers to is by Kevin Winker and Jack J. Withrow:

Winker, K., & Withrow, J. J. (2013). Natural history: Small collections make a big impact. Nature, 493(7433), 480–480. doi:10.1038/493480b

They constructed a Google Scholar profile and collected papers that cite the University of Alaska Museum's bird collection (see here for full details). The h-score of this collection of papers is 42, which Winkler and Withrow note is "equivalent to an average Nobel laureate in physics". Here's the graph of citations over time:


It's a neat trick, if a little time consuming. But one advantage it has is that it puts collections on a similar footing to individual researchers. You could imagine asking the question "how much money would you spend supporting a researcher at this level?" How does this compare to the resources actually being spent?

One thing I hope will emerge from discussions like this is a desire to make specimens first-class citizens of the web, with stable identifiers that enable them to be cited in the same way we cite papers and, increasingly, data sets.