Semantics Shememantics

In science we work, more often than not, in teams.  Whether we work with one other individual, five individuals, or interact at workshops with hundreds of strangers, it’s important that we are clearly understood.  Clarity is critical, especially when explaining complex concepts.  KISS is my second favorite acronym, even if I can’t keep to the principle (NEFLIS, a camping acronym, is my favorite – No Excuse for Living in Squalor just because you’re out in the woods).

A recently funded project I’m working on, under the aegis of EarthCube, is the harmonization of the Neotoma Paleoecological Database and the Paleobiology Database. Neotoma is a database of Quaternary fossils (mammals and microfossils such as pollen and ostracodes), and the Paleobiology Database is a database of every other kind of fossil. Both are incredible repositories for their respective communities, and powerful research tools in their own right.  My Distinguished Lecture talk at the University of Wisconsin’s Rebecca J. Holz Research Data Symposium was about the role of Community Databases in connecting researchers to Big Data tools, while getting their data into a curated form so that others could easily access and transform their data to undertake innovative research projects.

Superman Card Game by Whitman (1978) - G by andertoons, on Flickr
Figure 1. Semantic differences can be kryptonite for a project.  Especially a project that has very short arms relative to the rest of its body like Superman does in this picture. [ credit: andertoons ]
Our recent joint Neotoma-PBDB workshop, in Madison WI, showed me that, even with such closely allied data and disciplinary backgrounds, semantics matter.  We spent the first morning of the meeting having a back and forth discussion, where it kept seeming like we agreed on core concepts, but then, as the conversations progressed, we’d fall back into some sort of confusion.  As it began to seem unproductive we stepped back and checked in to see if we really were agreeing on core concepts.

While both databases contain fossil data, there is a fundamental difference in how the data are collected.  Typically Paleobiology DB data is collected in isolation, a fossil whale, discovered & reported is more common than a vertical stratigraphic survey on a single outcrop at a specific Latitude and Longitude.  In Neotoma, so much of our data comes from lake sediment cores that it makes sense that much of our data (and data collection) is described from stratigraphic sequences.

This difference may not seem like much, especially when the Holocene (the last 11,700 years) is basically an error term in much of the Paleobiology Database, but it’s enough of a difference that we were apparently agreeing on much, but then, inexplicably, disagreeing on followup discussions.

This is one of the fundamental problems in interdisciplinary research. Interdisciplinarity is as much understanding the terms another discipline uses as it is understanding the underlying philosophy and application of those terms in scientific discourse.  Learning to navigate these differences is time consuming, and requires a skill set that many of us don’t have.  At the very least, recognizing this is a problem, and learning to address this issue is a skill that is difficult to master.  It took us a morning of somewhat productive discussion before we really looked at the problem.  Once addressed we kept going back to our draft semantic outline to make sure we knew what we were talking about when discussing each other’s data.

This is all to say, we had a great workshop and I’m really looking forward to the developments on the horizon.  The PBDB development team is great (and so is the Neotoma team) and I think it’s going to be a very fruitful collaboration.

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Helping to fill the cloud from the bottom up.

Open data in the sciences is an aspirational goal, and one that I wholeheartedly agree with. The efforts of EarthCube (among others) to build an infrastructure of tools to help facilitate data curation and discovery in the Earth Sciences have been fundamental in moving this discussion forward in the geosciences, and at the most recent ESA meeting saw the development of a new section of the society dedicated to Open Science.

One of the big challenges to open science is that making data easily accessible and easily discoverable can be at odds with one another.  Making data “open” is as easy as posting it on a website, but making it discoverable is much more complex.  Borgman and colleagues (2007) very clearly lay out a critical barrier to data sharing in an excellent paper examining practices in “habitat ecology” (emphasis mine):

Also paradoxical is researchers’ awareness of extant metadata standards for reconciling, managing, and sharing their data, but their lack of use of such standards. The present dilemma is that few of the participating scientists see a need or ability to use others’ data, so they do not request data, they have no need to share their own data, they have no need for data standards, and no standardized data are available. . .

The issue, as laid out here, is that people know that metadata standards exist, but they’re not using them from the ground up because they’re not using other people’s data.  Granted this paper is now eight years old, but, for the vast majority of disciplinary researchers in the geosciences and biological sciences the extent of data re-use is most likely limited to using data tables from publications, if that. [a quick plug, if you’re in the geosciences, please take a few minutes to complete this survey on data sharing and infrastructure as part of the EarthCube Initiative]

So, for many people who are working with self-styled data formats, and metadata that is largely implicit (they’re the only ones who really understand their multi-sheet excel file), getting data into a new format (one that conforms to explicit metadata standards) can be formidable, especially if they’re dealing with a large number of data products coming out of their research.

Right now, for just a single paper I have ten different data products that need to have annotated metadata.  I’m fully aware that I need to do it, I know it’s important, but I’ve also got papers to review and write, analysis to finish, job applications to write, emails to send, etc., etc., etc., and while I understand that I can now get DOIs for my data products, it’s still not clear to me that it really means anything concrete in terms of advancement.

Don’t get me wrong, I am totally for open science, all my research is on GitHub, even partial papers, and I’m on board with data sharing.  My point here is that even for people who are interested in open science, correctly annotating data is still a barrier.

How do we address this problem? We have lots of tools that can help generate metadata, but many, if not all, of these are post hoc tools. We talk extensively, if colloquially, about the need to start metadata creation at the same time as we collect the data, but we don’t incentivise this process.  The only time people realize that metdata is important is at the end of their project, and by then they’ve got a new job to start, a new project to undertake, or they’ve left academia.

Making metadata creation a part of the research workflow is something I am working toward as part of the Neotoma project. Where metadata is a necessary component of the actual data analysis.   The Neotoma Paleoecological Database is a community curated database that contains sixteen different paleoecological proxies, ranging from water chemistry to pollen to diatoms to stable isotope data (see Pilaar Birch and Graham 2015). Neotoma has been used to study everything from modern patterns of plant diversity, rates of migration for plant and mammals, rates of change in community turnover through time, and species relationships to climate.  It acts as both a data repository and a research tool in and of itself.  A quick plug as well, the completion of a workshop this past week with the Science Education Resource Center at Carleton College in Minnesota has resulted in the development of teaching tools to help bring paleoecology into the classroom (more are on their way).

Neotoma has a database structure that includes a large amount of metadata.  Due in no small part to the activities of Eric Grimm, the metadata is highly curated, and, Tilia, a GUI tool for producing stratigraphic diagrams and age models from paleoecological data, is designed to store data in a format that is largely aligned with the Neotoma database structure.

In designing the neotoma package for R I’ve largely focused on its use as a tool to get data out of Neotoma, but the devastating closure of the Illinois State Museum by the Illinois Governor (link) has hastened the devolution of data curation for the database. The expansion of the database to include a greater number of paleoecological proxies has meant that a number of researchers have already become data stewards, checking to ensure completeness and accuracy before data is uploaded into the database.

Having the R package (and the Tilia GUI) act as a tool to get data in as well as out serves an important function, it acts as a step to enhance the benefits of proper data curation immediately after (or even during) data generation because the data structures in the applications are so closely aligned with the actual database structure.

We are improving this data/metadata synergy in two ways:

  1. Data structures: The data structures within the R package (detailed in our Open Quaternary paper) remain parallel to the database.  We’re also working with Mark Uhen, Shanan Peters and others at the Paleobiology Database (as part of this funded NSF EarthCube project) and, elsewhere, for example, the LiPD Project, which is itself proposing community data standards for paleoclimatic data (McKay and Emile-Geay, 2015).
  2. Workflow: Making paleoecological analysis easier through the use of the R package has the benefit of reducing the ultimate barrier to data upload.  This work is ongoing, but the goal is to ensure that by creating data objects in neotoma, data is already formatted correctly for upload to Neotoma, reducing the burden on Data Stewards and on the data generators themselves.

This is a community led initiative, although development is centralized (but open, anyone can contribute to the R package for example), the user base of Neotoma is broad, it contains data from over 6500 researchers, and data is contributed at a rate that continues to increase.  By working directly with the data generators we can help build a direct pipeline into “big data” tools for researchers that have traditionally been somewhere out on the long tail.

Jack Williams will be talking a bit more about our activities in this Middle Tail, and why it’s critical for the development of truly integrated cyberinfrastructure in the geosciences (the lessons are applicable to ecoinformatics as well) at GSA this year (I’ll be there too, so come by and check out our session: Paleoecological Patterns, Ecological Processes and Modeled Scenarios, where we’re putting the ecology back in ge(c)ology!).

Explorations in outreach – Creating a Twitter bot for the Neotoma Paleoecological Database.

If you’ve ever been in doubt about whether you chose the right programming language to learn I want to lay those concerns to rest here.

For many scientists, particularly in Biology or the Earth Sciences, there is often a question about whether you should be learning R, Python, Matlab or something else.  Especially when you’re coming into scientific programming in grad school with little prior experience this might seem like a daunting proposal.  You already don’t know anything about anything, and ultimately you wind up learning whatever you’re taught, or whatever your advisor is using and you wonder. . . Is the grass greener over in Python-land? Those figures look nice, if only I had learned R. . . Why did I learn on an expensive closed platform?

I am here to say “Don’t worry about it”, and I want to emphasize that with an example centered around academic outreach:

The Neotoma Paleoecological Database has had an issue for several years now.  We have had a large number of datasets submitted, but very few people could actively upload datasets to the database.  Neotoma is a live database, which means that not only do new datasets get added, but, as new information becomes available (for example, new taxonomic designations for certain species) datasets get updated.  This means that maintaining the database is very time intensive and there has traditionally been a gap between data ingest and data publication.  To make up for this there has been a data “Holding Tank” where individual records have been available, but this wasn’t the best solution.

Fast forward to about a year ago. Eric Grimm at the Illinois State Museum update the software package Tilia to provide greater access to the database to selected data stewards.  Each data type (including insets, pollen, mammal fossils, XRF, ostracodes, lake chemistry) has one or a few stewards who can vet and upload datasets directly to the database using the Tilia platform. This has increased the speed at which datasets have entered Netoma rapidly — over the last month there have been more than 200 new datasets entered — but it’s still hard to get a sense of this as an outsider since people don’t regularly check the database unless they need data from it.

Which brings us to Twitter. Academics have taken to Twitter like academics on a grant .  Buzzfeed has posted a list of 25 twitter feeds for nerds, Science published a somewhat contentious list of scientists to follow, and I’m on twitter, so obviously all the cool kids are there. This led me to think that twitter could be a good platform for publicizing new data uploads to Neotoma.  Now I just needed to learn how.

The process is fairly straightforward:

  1. Figure out what the most recently posted Neotoma datasets are:
    • This is made easier with the Neotoma API, which has a specific method for returning datasets: http://ceiwin10.cei.psu.edu/NDB/RecentUploads?months=1
    • You’ll notice (if you click) that the link returns data in a weird format.  This format is called JSON and it has been seen by many as the successor to XML (see here for more details).
  2. Check it against two files, (1) a file of everything that’s been tweeted already, and (2) a file with everything that needs to be tweeted (since we’re not going to tweet everything at once)
  3. Append the new records to the queue of sites to tweet.
  4. Tweet.

So that’s it (generally).  I’ve been working in R for a while now, so I have a general sense of how these things might happen. The thing is, these same mechanics translate to other languages as well. The hardest thing about programming (in my opinion) is figuring out how the program ought to flow. Everything else is just window dressing. Once you get more established with a programming language you’ll learn the subtleties of the language, but for hack-y programming, you should be able to get the hang of it regardless of your language background.

As evidence, Neotomabot. The code’s all there, I spent a day figuring out how to program it in Python. But to help myself out I planned it all first using long-hand notes, and then hacked it out using Google, StackOverflow and the Python manual.  Regardless, it’s the flow control that’s key. With my experience in R I’ve learned how “for” loops work, I know about “while” loops, I know try-catch methods exist and I know I need to read JSON files and push out to Twitter. Given that, I can map out a program and then write the code, and that gives us Neotomabot:

All the code is available on the GitHub repository here, except for the OAuth handles, but you can learn more about that aspect from this tutorial: How to Write a Twitter Bot. I found it very useful for getting started.  There is also a twittR, for R, there are several good tutorials for the package available (here, and here).

So that’s it.  You don’t need to worry about picking the wrong language. Learning the basics of any language, and how to map out the solution to a problem is the key.  Focus on these and you should be able to shift when needed.

The advantages of taking a chance with a new journal – OpenQuaternary

Full disclosure: I’m on the editorial board of Open Quaternary and also manage the blog, but I am not an Editor in Chief and have attempted to ensure that my role as an author and my role as an editor did not conflict.

Figure 1.  Neotoma and R together at last!
Figure 1. Neotoma and R together at last!

We (myself, Andria Dawson, Gavin L. SimpsonEric GrimmKarthik Ram, Russ Graham and Jack Williams) have a paper in press at a new journal called Open Quaternary.  The paper documents an R package that we developed in collaboration with rOpenSci to access and manipulate data from the Neotoma Paleoecological Database.  In part the project started because of the needs of the PalEON project.  We needed a dynamic way to access pollen data from Neotoma, so that analysis products could be updated as new data entered the database.  We also wanted to exploit the new API developed by Brian Bills and Michael Anderson at Penn State’s Center for Environmental Informatics.

There are lots of thoughts about where to submit journal articles.  Nature’s Research Highlights has a nice summary about a new article in PLoS One (Salinas and Munch, 2015) that looks to identify optimum journals for submission, and Dynamic Ecology discussed the point back in 2013, a post that drew considerable attention (here, here, and here, among others).  When we thought about where to submit I made the conscious choice to choose an Open Source journal. I chose Open Quaternary partly because I’m on the editorial board, but also because I believe that domain specific journals are still a critical part of the publishing landscape, and because I believe in Open Access publishing.

The downside of this decision was that (1) the journal is new, so there’s a risk that people don’t know about it, and it’s less ‘discoverable’; (2) even though it’s supported by an established publishing house (Ubiquity Press) it will not obtain an impact factor until it’s relatively well established.  Although it’s important to argue that impact factors should not make a difference, it’s hard not to believe that they do make a difference.

Figure 2.  When code looks crummy it's not usable.  This has since been fixed.
Figure 2. When code looks crummy it’s not usable. This has since been fixed.

That said, I’m willing to invest in my future and the future of the discipline (hopefully!), and we’ve already seen a clear advantage of investing in Open Quaternary.  During the revision of our proofs we noticed that the journal’s two column format wasn’t well suited the the blocks of code that we presented to illustrate examples in our paper.  We also lost the nice color syntax highlighting that pandoc offers when it renders RMarkdown documents (see examples in our paper’s markdown file).  With the help of the journal’s Publishing Assistant Paige MacKay, Editor in Chief Victoria Herridge and my co-authors we were able to get the journal to publish the article in a single column format, with syntax highlighting supported using highlight.js.

I may not have a paper in Nature, Science or Cell (the other obvious option for this paper /s) but by contributing to the early stages of a new open access publishing platform I was able to change the standards and make future contributions more readable and make sure that my own paper is accessible, readable and that the technical solution we present is easily implemented.

I think that’s a win.  The first issue of Open Quaternary should be out in March, until then you can check out our GitHub repository or the PDF as submitted (compleate with typoes).

Recovering dark data, the other side of uncited papers.

A little while ago, on Dynamic Ecology, a question was posed about how much self-promotion was okay, and what kinds of self promotion were acceptable.  The results were interesting, as was the discussion in the comments.  Two weeks ago I also noticed a post by Jeff Ollerton (at the University of Northampton, HT Terry McGlynn at Small Pond Science) who also weighed in on his blog, presenting a table showing that up to 40% of papers in the Biological Sciences remain uncited within the first four years since publication, with higher rates in Business, the Social Sciences and the Humanities.  The post itself is written more for the post-grad who is keen on getting their papers cited, but it presents the opportunity to introduce an exciting solution to the secondary issue: What happens to data after publication?

In 1998 the Neotoma Paleoecological Database published an ‘Unacquired Sites Inventory‘.  These were paleoecological sites (sedimentary pollen records, representing vegetation change over centuries or millennia) for which published records existed, but that had not been entered into the Neotoma Paleoecological Database or the North American Pollen Database.  Even accounting for the fact that the inventory represents a snapshot that ends in 1998, it still contains sites that are, on average, older than sites contained within the Neotoma Database itself (see this post by yours truly).  It would be interesting to see the citation patterns of sites in the Unacquired Sites versus those in the Neotoma Database, but that’s a job for another time, and, maybe, a data rescue grant (hit me up if you’re interested!).

Figure 1.  Dark data.  There is likely some excellent data down this pathway, but it's too spooky for me to want to access it, let's just ignore it for now. Photo Credit: J. Illingworth.
Figure 1. Dark data. There is likely some excellent data down this dark pathway, but it’s too spooky for me to want to access it, let’s just ignore it for now. Photo Credit: J. Illingworth.

Regardless, citation patterns are tied to data availability (Piwowar and Vision, 2013), but the converse is also likely to be true.  There is little motivation to make data available if a paper is never cited, particularly an older paper, and little motivation for the community to access or request that data if no one knows about the paper.  This is how data goes dark.  No one knows about the paper, no one knows about the data, and large synoptic analyses miss whole swaths of the literature. If the citation patterns cited by Jeff Ollerton hold up, it’s possible that we’re missing 30%+ of the available published data when we’re doing our analyses.  So it’s not only imperative that post-grads work to promote their work, and that funding agencies push PIs to provide sustainable data management plans, but we need to work to unearth that ‘dark data’ in a way that provides sufficient metadata to support secondary analysis.

Figure 1. PaleoDeepDive body size estimates generated from a publication corpus (gray bars) versus estimates directly assimilated and entered by humans.  Results are not significantly different.
Figure 2. PaleoDeepDive body size estimates generated from a publication corpus (gray bars) versus estimates directly assimilated and entered by humans. Results are not significantly different.

Enter Paleodeepdive (Peters et al., 2014).  PaleoDeepDive is a project that is part of the larger, EarthCube funded, GeoDeepDive, headed by Shanan Peters at the University of Wisconsin and built on the DeepDive platform. The system is trained to look for text, tables and figures in domain specific publications, extract data, build associations and recognize that there may be errors in the published data (e.g., misspelled scientific names).  The system then then assign scores to the data extracted indicating confidence levels in the associations, which can act as a check on the data validity, and helps in building further relations as new data is accquired.  Paleodeepdive was used to comb paleobiology journals to pull out occurrence data and morphological characteristics for the Paleobiology Database.  In this way PaleoDeepDive brings uncited data back out of the dark and pushes it into searchable databases.

These kinds of systems are potentially transformative for the sciences. “What happens to your work once it is published” is transformed into a two part question: how is the paper cited, and how is the data used. More and more scientists are using public data repositories, although that’s not neccessarily the case as Caetano and Aisenberg (2014) show for animal behaviour studies, and fragmented use of data repositories (supplemental material vs. university archives vs. community lead data repositories) means that data may still lie undiscovered.  At the same time, the barriers to older data sets are being lowered by projects like PaleoDeepDive that are able to search disciplinary archives and collate the data into a single data storage location, in this case the Paleobiology Database. The problem still remains, how is the data cited?

We’ve run into the problem of citations with publications, not just with data but with R packages as well.  Artificial reference limits in some journals preclude full citations, pushing them into web-only appendices, that aren’t tracked by the dominant scholarly search engines.  That, of course, is a discussion for another time.

Opening access to age models through GitHub

As part of PalEON we’ve been working with a lot of chronologies for paleoecological reconstruction (primarily Andria Dawson at UC-Berkeley, myself, Chris Paciorek at UC-Berkeley and Jack Williams at UW-Madison). I’ve mentioned before the incredible importance of chronologies in paleoecological analysis. Plainly speaking, paleoecological analysis means little without an understanding of age.  There are a number of tools that can be used to analyse, display and understand chronological controls and chronologies for paleoecological data. The Cyber4Paleo webinars, part of the EarthCube initiative, have done an excellent job of representing some of the main tools, challenges and advances in understanding and developing chronologies for paleoecological and geological data.  One of the critical issues is that the benchmarks we use to build age models change through time.  Richard Telford did a great job of demonstrating this in a recent post on his (excellent) blog.  These changes, and the diversity of age models out there in the paleo-literature means that tools to semi-automate the generation of chronologies are becoming increasingly important in paleoecological research.

Figure 1.  Why is there a picture of clams with bacon on them associated with this blog post?  Credit: Sheri Wetherell (click image for link)
Figure 1. Why is there a picture of clams with bacon on them associated with this blog post? Credit: Sheri Wetherell (click image for link)

Among the tools available to construct chronologies is a set of R scripts called ‘clam‘ (Blaauw, 2010). This is heavily used software and provides the opportunity to develop age models for paleo-reconstructions from a set of dates along the length of the sedimentary sequence.

One of the minor frustrations I’ve had with this package is that it requires the use of a fixed ‘Cores’ folder. This means that separate projects must either share a common ‘clam’ folder, so that all age modelling happens in the same place, or that the ‘clam’ files need to be moved to a new folder for each new project. Not a big deal, but also not the cleanest.

Working with the rOpenSci folks has really taught me a lot about building and maintaining packages. To me, the obvious solution to repeatedly copying files from one location to the other was to put clam into a package. That way all the infrastructure (except a Cores folder) would be portable. The other nice piece was that this would mean I could work toward a seamless integration with the neotoma package. Making the workflow: “data discovery -> data access -> data analysis -> data publication” more reproducible and easier to achieve.

To this end I talked with Maarten several months ago, started, stopped, started again, and then, just recently, got to a point where I wanted to share the result. ‘clam‘ is now built as an R package. Below is a short vignette that demonstrates the installation and use of clam, along with the neotoma package.


#  Skip these steps if one or more packages are already installed.  
#  For the development packages it's often a good idea to update frequently.

install.packages("devtools")
require(devtools)
install_github("clam", "SimonGoring")
install_github("neotoma", "ropensci")
require(clam)
require(neotoma)

# Use this, and change the directory location to set a new working directory if you want.  We will be creating
# a Cores folder and new files & figures associated with clam.
setwd('.')  

#  This example will use Three Pines Bog, a core published by Diana Gordon as part of her work in Temagami.  It is stored in Neotoma with
#  dataset.id = 7.  I use it pretty often to run things.

threepines <- get_download(7)

#  Now write a clam compatible age file (but make a Cores directory first)
if(!'Cores' %in% list.files(include.dirs=TRUE)){
  dir.create('Cores')
}

write_agefile(download = threepines[[1]], chronology = 1, path = '.', corename = 'ThreePines', cal.prog = 'Clam')

#  Now go look in the 'Cores' directory and you'll see a nicely formatted file.  You can run clam now:
clam('ThreePines', type = 1)

The code for the ‘clam’ function works exactly the same way it works in the manual, except I’ve added a type 6 for Stineman smoothing. In the code above you’ve just generated a fairly straightforward linear model for the core. Congratualtions. I hope you can also see how powerful this workflow can be.

A future step is to do some more code cleaning (you’re welcome to fork or collaborate with me on GitHub), and, hopefully at some point in the future, add the funcitonality of Bacon to this as part of a broader project.

References

Blaauw, M., 2010. Methods and code for ‘classical’ age-modelling of radiocarbon sequences. Quaternary Geochronology 5: 512-518

EarthCube webinars and the challenges of cross-disciplinary Big Data.

EarthCube is a moon shot.  It’s an effort to bring communities broadly supported through the NSF Geosciences Directorate and the Division of Advanced Cyberinfrastructure together to create a framework that will allow us to understand our planet (and solar system) in space and in time using data and models generated across a spectrum of disciplines, and spanning scales of space and time.  A lofty goal, and a particularly complex one given the fragmentation of many disciplines, and the breadth of researchers who might be interested in participating in the overall project.

To help support and foster a sense of community around EarthCube the directorate has been sponsoring a series of webinars as part of a Research Coordination Network called “Collaboration and Cyberinfrastructure for Paleogeosciences“, or, more simply C4P.  These webinars have been held every other Tuesday from 4 – 5pm Eastern, but are archived on the webinar website (here).

The Neotoma Paleoecological Database was featured as part of the first webinar.  Anders Noren talked about the cyber infrastructure required to support LacCore‘s operations, and Shanan Peters talks about an incredible text mining initiative (GeoDeepDive) in one of the later webinars.

Image
Fig 1. The flagship for the Society for American Pedologists has run aground and it is now a sitting duck for the battle machine controlled by the Canadian Association for Palynologists in the third war of Data Semantics.

It’s been interesting to watch these talks and think about both how unique each of these paleo-cyberinfrastructure projects is, but also how much overlap there is in data structure, use, and tool development.  Much of the struggle for EarthCube is going to be developing a data interoperability structure and acceptable standards across disciplines.  In continuing to develop the neotoma package for R I’ve been struggling to understand how to make the data objects we pull from the Neotoma API interact well with standard R functions, and existing R packages for paleoecological data.  One of the key questions is how far do we go in developing our own tools before that tool development creates a closed ecosystem that cuts off outside development?  If I’m struggling with this question in one tiny disciplinary nook, imagine the struggle that is going to occur when geophysicists and paleobotanists get together with geochonologists and pedologists!

Interoperability of these databases needs to be a key goal.  Imagine the possibilities if we could link modern biodiversity databases with Pleistocene databases such as Neotoma, and then to deep time databases like the Paleobiology Database in a seamless manner.  Big data has clearly arrived in some disciplines, but the challenges of creating big data across disciplines is just starting.