Humanity began writing on stone and clay tablets, and then moved to papyrus, paper, and now we write with electrons.  Does it seem that our media for information storage is becoming more flimsy or is it better to search through piles of electrons than card catalogs?  How can we save the wonderful work that we are all paying for (in the form of government funded research)?  Do database records hold the same value as published papers?  If so, how can we maintain them indefinitely?  Should there be a paper version of each database?  How can cloud computing, the linked data/open data initiatives help?  What is the role of libraries in this sort of data landscape?

In my own experience, working on a semantic web project called the Neuroscience Information Framework (NIF) at the University of California San Diego, I noticed something strange that has happened to our society that bears on these questions.  For several months my desk was housed among many others in one of those open workspaces whose explicit goal is to improve communication between the individuals (no cubicles). One day there was an interruption in the wireless service in the building.  This interruption resulted in the inevitable frustration of “I can’t do what I was just doing,” but then a tremendous event occurred: these strange entities who had been toiling near me and whose existence I acknowledged with a nod each morning became real humans.  The amazing awakening resembled an episode of Star Trek where the Borg, a half machine half biological group fully integrated in the hive mind suddenly lost connectivity to the hive and were bumbling around, very confused.  People all around me began waking up from the technology trance and started to act more like … people.  They greeted me, we exchanged opinions of the wireless services, and we met.

With my Borg experience in mind, questions of our deep dependence on technology crystallized.  What if the power went off on wikipedia?  What if google didn’t exist?  How would I find things?  How would I be able to work without google docs?  In this networked world, is it possible that we can’t survive without the collective?

The level of integration of online information and search systems with our lives has become very eerie, to say the least.

As scientists do we have the same issues?  Can’t we do research without PubMed?  A few years ago while at Stanford, a colleague and I were talking to an art historian and the conclusion of the discussion was, “if it (a scientific paper or a piece of data) does not exist on the web, then it does not exist”.  Something quite contrary to the experience of the art historian, who apparently still did research in a physical building that contained actual papers, books, and non-digital versions of art.

So, then, who backs up the data that we are becoming completely dependent on?  When researchers move to a new university or pass onto the great beyond, what happens to the data stores that they maintained?  Do they take their data with them setting up cloud computing operations?

The good news is scientific data in databases, whether or not its published on paper, is backed up and data are regularly checked for integrity at most sites.  Data and software tools are also replicated in so called “mirrors”, which are essentially copies of the same data or software tools that serve a particular community.   Additionally, the National Library of Medicine copies and stores many of the significant databases in their systems, allowing researchers to access them and storing a digital copy for posterity. For example, the Gensat project data exist on Rockefeller servers, but also a mirror of the data is set up at NCBI (the electronic national library of medicine and the home of PubMed).

This seems safe enough. However, the directors of the National Institutes of Health are not always as willing to indefinitely support databases as they are to pay researchers to set them up.   So after five or ten years when the funding runs out, what happens to all that data that researchers painstakingly toiled for many years to gather?  Some data was published on paper, some was likely not published anywhere or pulled together from papers by raw human effort such as the Ki database, which gathered the raw numbers from many publications for affinity between drugs and receptors.  Many databases contain that elusive negative data which is not considered worthy of publishing by the ‘peer reviewing’ crowd, but which may save other researchers tremendous time if they try to replicate an experiment that several others already found did not work.  Some databases migrate to funded projects and then are maintained by other universities while the funding is in flux, but some simply vanish into the ether.  Should someone maintain them?

The experience of the private human genome project “Panther,” started by Craig Venter at Celera Inc, later Applera, later Applied Biosystems, later an unsupported project at the Stanford Research Institute, and now potentially rising from the ashes into a new project, shows that industrial data may have a similar or potentially an even more dire fate.

In recent years, several movements have swept data science. One is the open data movement and another is the linked data movement.  Both bear on this issue of data maintenance.  The linked data movement (one of the buzzwords in the semantic web community) attempts to link all pieces of related information by formal relationships, sort of like playing an enormous game of “Six Degrees of Kevin Bacon” with scientific data.  Obviously, these data sets must be openly accessible for this to work, so the open data movement spurred the creation of huge datasets readable by anyone in the world.  These data sets include some of the most valuable biomedical data, such as OMIM and PubMed, but also include wikipedia and other less than peer-reviewed data.   Lots of the people in the open data world talk about their preferred ways of storing that data, such as “tuples” or graphs, but all this boils down to a couple of main ideas:

1. A piece of data should persist in a reliable way, with a reliable address.
2. A piece of data should be in a format that is readable by others.
3. A piece of data should have a unique identifier, a social security number.
4. A piece of data is not owned by anyone, but should be traceable to its origin.

Therefore, the open data community has a vested interest in making all data available for their systems to consume and compute, including the databases whose authors, or whose authors funding, has expired.

In the model of linked data, as a ‘six degrees of Kevin Bacon’ analogy, the data graph would suffer if the record of a movie were to be wiped off the graph.  Would we still know that Tom Hanks was connected to Kevin Bacon if Apollo 13 was no longer a data link?    Probably, but the link would no longer be direct.

The problem with linked data disappearing is that the relationship between Aquaporin4 and Eric Nestler is less well established than the relationship between Tom Hanks and Kevin Bacon. Actually, a database of supplementary materials contains this connection (see Drug Dependent Gene database). Indeed, if the data are deposited inside of a database but are not central nodes of discourse they may disappear without a sound.  However, their inherent value may not be in their connectivity; it may instead be that they are valuable in a direction that few have pursued as a line of investigation, such as a promising lead for a therapeutic agent in a particular disease, or the piece of negative data that will spare another researcher a year of fruitless endeavor.

The six degrees of online data sources

The stance of the Neuroscience Information Framework (NIF) as a member of the semantic web community is that data should be preserved because it may be useful at a later time.  The larger question is who will pay to preserve the data?  What is the role of libraries in an age where books are no longer made of paper, but stores of knowledge with ‘a front end’ and a ‘back end’?  Will we have thousands of databases taking up room in library basements somewhere, where they can be accessed like so many other ‘collections,’ or will projects such as NIF be the keepers of these data because they can integrate the searching of the data across data structures?  Who will champion data preservation in the digital age?

Perform a search in NIF and you will see various terms highlighted in the search results (the current highlighting color is brick red, but we are open to suggestions).   Hover over each of these highlighted terms and NIF will tell you what the term means to the NIF system.  If you hover over “IS,” NIF tells you it’s an anatomical structure. If you right click on it and ask to see “IS” in the Neurolex, it will tell you that IS is an abbreviation for the inferior salivitory nucleus.  This new feature is an example of what is often called “entity recognition.”

In the formal world of knowledge representation, an entity is that which is perceived, known, or inferred to have its own distinct existence.  For NIF, entities are those things like organisms, cells, molecules, and techniques that define our domain.  These entities are represented in the NIF ontologies.  Each entity has its own numerical identifier, sort of like a social security number, that uniquely identifies the entity.  This identifier is used to point to different ways of saying the same things to the same entity.  For example, NIF doesn’t care whether you call entity birnlex_2645, the IS, inferior salivary nucleus, or Freddy, for that matter.  They are all (and always) the same thing.

Unfortunately, the richness and complexity of our language makes recognizing entities a tricky thing, as everyone who uses a search engine knows.  Not only can we call the same entity many things, but we can call many entities the same thing.  Chances are that the IS highlighted by NIF in the search results actually is not the inferior salivatory nucleus but the third person form of the verb “to be,” or perhaps it is the initial segment of an axon or the Institute for Science.    Right now, NIF doesn’t really know.

In future releases of NIF, we will be working towards improving the accuracy of our entity recognition.  Why?  Because once we know that IS is a brain nucleus, we can find anything that is known about it:  its projections, its genes, the diseases in which it is affected.  A preview of what is coming can be seen in the NIF Cards.

Search for IS with NIF Card

NIF cards for each entity can be viewed by right clicking over the highlighted term and selecting “Show NIF card” from the menu. NIF cards currently are only implemented for anatomical structures and cells.

For now, however, we hope you will explore the new NIF and develop an appreciation for the difficulties of semantic search by seeing what NIF thinks the results mean.  You may be surprised!

Please provide your valuable feedback by sending an email to support@neuinfo.org.  Thank you.

Adulthood, like many terms we use for describing data, is a very poorly defined and a somewhat arbitrary concept. When does an organism become an adult? The answer in general would be “it depends on how you define adult.” In the highly charged world of scientific discourse, people may argue correctly that there is no single definition of adult that would satisfy everyone or that there is a magical time point at which it occurs. The question for the Neuroscience Information Framework or any other group attempting to integrate data from many sources is not whether one group of definitions is correct, but rather whether such a concept is useful for comparing and understanding data.

To illustrate this point, MGI or the mouse genome informatics project, which is the place to go for all things mouse (from mouse strains to ontologies and genes), does not define the term adult, because of the disagreement among scientists as to what constitutes the break between juvenile and adult mice (personal communication). Of course MGI does have the “adult brain ontology”, among other resources labeled with the term adult. So they use the term as it is useful and describes a set of organismal characteristics, but are unwilling to define the term due to the ambiguities in the definitions.

Other large datasets, such as the Allen Brain Atlas do not deal with these sorts of definitions; rather they take data only from postnatal day 55 animals, which they consider safely within the adult range.

In an ideal world, we would provide a standard set of organism attributes for every subject used that is provided in a computable form, e.g., age, weight, sexual maturity. Anyone would therefore request data only from those subsets of animals that were comparable, e.g., between ages 30 days and 90 days and between 100g – 200g. Within a given resource, e.g., database, one can easily set up such a system. However, for a system like NIF that searches across broad swaths of information contained in individual databases, XML files, HTML pages and text, it is currently impossible to provide such a universal computational service on the fly even for something that should be conceptually simple, e.g., representation of age (days, months, years, prenatal, embryonic etc). Nevermind the fact that such information is not consistently available for a source.

A consideration of the literature shows that many times the only label for age is “adult” with no specifics provided.

For databases that take and analyze data from published work, like neuromorpho.org, the word adult is the only age that accurately describes a particular data set. Automated systems recognize this term, but if the definition is not constant across sources, the “adult” is not a useful bucket for aggregating information. One source may have adult as starting at P21 while another at P30. Furthermore automated systems would not be able to translate “P55” as adult, or “week 5” into adulthood unless there was a definition that could be applied.

DEFINITION OF ADULT

The question is whether we can come up with a definition of adulthood that can be consistently applied. Most of the biological definitions of adulthood deal with the readiness of an organism to reproduce, sexual maturity, or the notion that an animal is full-grown. Both definitions have inherent problems. For example, many species including male rats do not stop growing until death, making “full-size” only applicable when animals have reached their death. Similarly, sexual maturity may be defined as the onset of estrus, but can also be defined as the termination of ‘pubescence’ a period of time that is difficult to access in a rat or mouse.

Adding a little complexity to the problem is the relatively simple question of what is the day of birth. Scientists from various entrenched camps define postnatal day zero as the day of birth and others define it as postnatal day one. Neither group is incorrect, but anyone attempting to bring together data from various datasets (or publications) is required to spend a large amount of time attempting to understand whether the particular piece of data comes from an animal that is P5 or P4.

Due to the inherent problems in defining such a thing, the ontology community (a community concerned with establishing standards in discourse in scientific communication) and many researchers that build databases meant to compare data from various sources treat adulthood with caution. Nonetheless, as evidenced by its wide use, the concept of “adult” is useful and often stands alone as an important characteristic for defining data even though it is not well defined for any species.

THE ARBITRARY BUT DEFENSIBLE SOLUTION

The above-mentioned problems with defining adulthood are echoed and magnified in humans, because of a need to access emotional maturity and readiness to take on the tasks of independent existence in a complex society.   The solution to determining what an adult human is has been strangely simple and boils down to a number.  Any parent of a teenager knows that there is no magical event that happens on the 18^th birthday of a child, but for legal systems a hard cut-off is needed, so that treatment of criminal activities and rights bestowed on individuals are clearly defined.  Therefore in almost all advanced societies the legal adult is 18 years of age, whether or not they are emotionally ready to be one or whether or not the pubertal period has passed.

We suggest that a similar arbitrary but defensible cut-off date should be established and implemented for all research animals so that when age of animal is reported as “adult” we can, with some degree of certainty, compare data of one study to the thousands of other similar studies.

According to the work of Finlay and Darlington (Science, 268:1578-84) with the chronometry of species, the final important steps in brain development of mice occur 29.7 days after conception, or postnatal day 12 (birth is P0 in this case), menstruation typically begins between postnatal day 25 and 40 and body growth is completed at about age postnatal day 50.  So we can use the arbitrary date of postnatal day 50 as the definition of adult mouse, as this is a reasonable standard for an adult.  We will define the day of birth as postnatal day 0.  Mice between the age of P0 and P24 will be termed juvenile and mice between P25 and P49 should be termed early adult.

IN CONCLUSION

In the NIFSTD (Neuroscience Information Framework standard ontology) we will define arbitrary but defensible standards for mice and other common research species as this sort of standard is an important part of establishing a common framework in discussion, and not necessarily dealing with the absolute scientific truth.

The reason that we need a standard for age and many other such common terms is that we need to establish a point of reference, which will allow for accurate communication about results.  This is presumably the reason that the standard international system of units (SI) was put in place and we believe in the standardization of certain common variables in experiments for the sake of effective data analysis.

We look forward to seeing you at the next NIF Webinar.  Until then, enjoy all of the NIF Webinar archives.

Happy Holidays.

This INCF Newsletter features:

• information on INCF and INCF Node activities
• photos from the INCF booth at the SfN Neuroscience 2009 meeting in Chicago
• the latest information on the upcoming INCF Neuroinformatics Congress
  (Maryann Martone, PI of NIF, will be the keynote speaker for USA at Neuro Informatics 2010 held in Kobe, Japan in August)
• a short interview with Jeanette Hellgren Kotaleski, Coordinator of the new Erasmus Mundus PhD progam in Neuroinformatics; “EuroSPIN”

View the latest INCF Newsletter!

We created a short presentation of the improvements incorporated in NIF 2.0 (in case you missed them).
Do check it out and tell us what you think!

We look forward to hearing from you.

We’re glad that you’re here. The Neuroscience Information Framework project is a community portal for neuroscience researchers (although everyone is welcome) that provides the means and access to find data, tools, materials and information to drive neuroscience discovery. NIF is supported by the NIH Blueprint consortium and is built for neuroscientists by neuroscientists, working through the neuroinformatics committee of the Society for Neuroscience and the International Neuroinformatics Coordinating Facility. NIF makes use of advances in search, knowledge engineering, text mining and human legwork to access the so-called “hidden web,”,i.e., dynamic databases and other content that is not indexed by search engines. For years, people have been asking “Why can’t we have a Google for neuroscience?” Well, this is it! We maintain a custom web index built around neuroscience, a catalog of curated resources, access to many neuroscience databases, a large literature corpus for neuroscience, all accessible simultaneously through a simple search interface. You may search these resources using the NIF vocabularies, an extensive vocabulary that covers many domains of neuroscience. Why is this important? We use these vocabularies to search for synonyms and related terms so that you can focus or expand your searches, e.g., not “Neuron” but 125 different types of neurons, classified by neurotransmitters and brain regions. As the NIF evolves and the vocabularies grow, we’ll use them more and more for searching and organizing results.

With the release of NIF 1.5, we have significantly upgraded NIF, both its contents and its look and feel, and we need your feedback! Is something missing from NIF? Let us know by recommending a resource. Are you building a database and would like to make use of the framework? Then visit our recommended best practices and use our vocabularies. Do you need help getting started with NIF? Then attend one of our on-line tutorials. Is it easy to use and useful to you? If not, help us make the NIF better by becoming a beta tester.

We want the NIF to be a community place for neuroscientists and those who want access to neuroscience data and tools. We’ll be making use of Wikis and other Web 2.0 tools so that you can not only come to the NIF to take information away but also contribute your expertise and leave some of your experience behind. We have already set up the NeuroLex Wiki to help us build the NIF vocabularies. The NIF Blog is a place where we will discuss neuroinformatics and information retrieval topics based on current trends and our experience building the NIF. If you have an opinion or experience you’d like to share, we’ll be happy to work with guest bloggers.