(index page)

Data Diversity is Okay

At risk of sounding like a motivational speaker, this is such an exciting time to be involved in science and research.  We are swimming in data and information (yay!), there are exciting software tools available for researchers, librarians, and lay people alike, and the possibilities for discovery seem endless.  Of course, all of this change can be a bit daunting.  How do you handle the data deluge? What software is likely to be around for a while? How do you manage your time effectively in the face of so much technology?

Like many other groups, academic libraries are undergoing some growing pains in the face of the information age. This may be attributed drastic budget cuts, rising costs for journal subscriptions, and the less important role that physical collections play in due to increasing digitization of information.  Researchers are quite content to sit at their laptops and download PDFs from their favorite journals rather than wander the stacks of their local library; they would rather use Google searches to scour the internet for obscure references rather than ask their friendly subject librarian for help in the hunt.

Despite the challenges above, I firmly believe that this is such an exciting time to be working at the interface of libraries, science, and technology.  Many librarians agree with me, including those at UCLA.  Lisa Federer and Jen Weintraub recently put on a great panel at the UCLA library focused on data curation.  I was invited to participate and agreed, which turned out to be an excellent decision.

The panel was called “Data Curation in Action”, and featured four panelists: Chris Johanson, UCLA professor of classics and digital humanities; Tamar Kremer-Sadlik, director of research at the UCLA Center for Everyday Lives of Families (CELF); Paul Conner, the digital laboratory director of CELF; and myself, intended to represent some mix of researchers in science and librarians.

Without droning on about how great the panel was, and how interesting the questions from the audience were, and how wonderful my discussions were with attendees after the panel, I wanted to mention the major thing that I took away: there is so much diverse data being generated by so many different kinds of projects and researchers.  Did I mention that this is an exciting time in the world of information?

Take Tamar and Paul: their project involves following families every day for hours on end, recording video, documenting interactions and locations of family members, taking digital photographs, conducting interviews, and measuring cortisol levels (an indicator for stress).  You should read that sentence again, because that is an enormous diversity of data types, not to mention the volume. Interviews and video are transcribed, quantitative observations are recorded in databases, and there is an intense coding system for labeling images, videos, and audio files.

Now for Chris, who has the ability to say “I am a professor of classics” at dinner parties (I’m jealous).  Chris doesn’t sit about reading old texts and talking about marble statues. Instead he is trying to reconstruct “ephemeral activities in the ancient world”, such as attending a funeral, going to the market, etcetera. He does this using a complex combination of Google Earth, digitized ancient maps, pictures, historical records, and data from excavations of ancient civilizations.  He stole the show at the panel when he demonstrated how researchers are beginning to create virtual worlds in which a visitor can wander around the landscape, just like in a modern day 3D video game.

This is really just a blog post about how much I love my job. I can’t imagine anything more interesting than trying to solve problems and provide assistance for researchers such as Tamar, Paul and Chris.

In case you are not one of the 35 million who have watched it, OK Go has a wonderful video about getting through the tough times associated with the dawning information age (at least that’s my rather nerdy interpretation of this song):

QSE3, IGERT, OA and DCXL

A few months back I received an invite to visit the University of Florida in sunny Gainesville.  The invite was from organizers of an annual symposium for the Quantitative Spatial Ecology, Evolution and Environment (QSE3) Integrative Graduate Education and Research Traineeship (IGERT) program.  Phew! That was a lot of typing for the first two acronyms in my blog post’s title.  The third acronym  (OA) stands for Open Access, and the fourth acronym should be familiar.

I presented a session on data management and sharing for scientists, and afterward we had a round table discussion focused on OA.  There were about 25 graduate students affiliated the QSE3 IGERT program, a few of their faculty advisors, and some guests (including myself) involved in the discussion.  In 90 minutes we covered the gamut of current publishing models, incentive structures for scientists, LaTeX advantages and disadvantages, and data sharing.  The discussion was both interesting and energetic in a way that I don’t encounter from scientists that are “more established”.  Some of the themes that emerged from our discussion warrant a blog post.

First, we discussed that data sharing is an obvious scientific obligation in theory, but when it comes to your data, most scientists get a bit more cagey.  This might be with good reason – many of the students in the discussion were still writing up their results in thesis form, never mind in journal-ready form.  Throwing your data out into the ether without restrictions might result in some speedy scientist scooping you while you are dotting i’s and crossing t’s in your thesis draft.  In the case of grad students and scientists in general, embargo periods seem to be a good response to most of this apprehension. We agreed as a group, however, that such embargos should be temporary and should be phased out over time as cultural norms shift.

The current publishing model needs to change, but there was disagreement about how this change should manifest. For instance, one (very computer-savvy) student who uses R, LaTeX and Sweave asked “Why do we need publishers? Why can’t we just put the formatted text and code online?”  This is an obvious solution for someone well-versed in the world of document preparation in the vein of LaTeX.  You get fully formated, high-quality publications by simply compiling documents. But this was argued against by many in attendance because LaTeX use is not widespread, and most articles need heavy amounts of formatting before publication.  Of course, this is work that would need to be done by the overburdened scientist if they published their own work, which is not likely to become the norm any time soon.

Let’s pretend that we have overhauled both scientists and the publishing system as it is.  In this scenario, scientists use free open-source tools like LaTeX and Sweave to generate beautiful documents.  They document their workflows and create python scripts that run in the command line for reproducible results.  Given this scenario, one of the students in the discussion asked “How do you decide what to read?” His argument was that the current journal system provides some structure for scientists to hone in on interesting publications and determine their quality based (at least partly) on the journal in which the article appears.

One of the other grad students had an interesting response to this: use tags and keywords, create better search engines for academia, and provide capabilities for real-time peer review of articles, data, and publication quality.  In essence, he used the argument that there’s no such thing as too much information. You just need a better filter.

One of the final questions of the discussion came from the notable scientist Craig Osenberg. It was in reference to the shift in science towards “big data”, including remote sensing, text mining, and observatory datasets. To paraphrase: Is anyone worrying about the small datasets? They are the most unique, the hardest to document, and arguably the most important.

My answer was a resounding YES! Enter the DCXL project.  We are focusing on providing support for the scientists that don’t have data managers, IT staff, and existing data repository accounts that facilitate data management and sharing.  One of the main goals of the DCXL project is to help “the little guy”.  These are often scientists working on relatively small datasets that can be contained in Excel files.

In summary, the very smart group of students at UF came to the same conclusions that many of us in the data world have: there needs to be a fundamental shift in the way that science is incentivized, and this is likely to take a while.  Of course, given that these students are early in their careers, and their high levels of interest and intelligence, they are likely to be a part of that change.

Special thanks goes to Emilio Bruna (@brunalab) who not only scored me the invite to UF, but also hosted me for a lovely dinner during my visit (albeit NOT the Tasty Budda…)

Communication Breakdown: Nerds, Geeks, and Dweebs

Last week the DCXL crew worked on finishing up the metadata schema that we will implement in the DCXL project.  WAIT! Keep reading!  I know the phrase “metadata schema” doesn’t necessarily excite folks – especially science folks.  I have a theory for why this might be, and it can be boiled down to a systemic problem I’ve encountered ever since becoming deeply entrenched in all things related to data stewardship: communication breakdown.

I began working with the DataONE group in 2010, and I was quickly overwhelmed by the rather steep learning curve I encountered related to data topics.  There was a whole vocabulary set I had to learn, an entire ecosphere of software and hardware, and a hugely complex web of computer science-y, database-y, programming-y concepts to unpack.  I persevered because the topics were interesting to me, but I often found myself spending time on websites that were indecipherable to the average intelligent person, or reading 50 page “quick start guides”, or getting entangled in a rabbit hole of wikipedia entries for new concepts related to data.

I love learning, so I am not one to complain about spending time exploring new concepts. However I would argue that my difficulties represent a much bigger issue plaguing advances in data stewardship: communication issues.  It’s actually quite obvious why these communication problems exist.  There are a lot of smart people involved in data, all of whom have very divergent backgrounds.  I suggest that the smart people can be broken down into three camps: the nerds, the geeks, and the dweebs.  These stereotypes should not be considered insults; rather they are an easy way to refer to scientists, librarians, and computer types. Check out the full venn diagram of nerds here.

The Nerds. This is the group to which I belong.  We are specially trained in a field and have in-depth knowledge of our pet projects, but general education about computers, digital data, and data preservation are not part of our education.  Certainly that might change in the near future, but in general we avoid the command line like the plague, prefer user-friendly GUIs, and resist any learning of new software, tools, etc. that might take away from learning about our pet projects.

The geeks. Also known as computer folks. These folks might be developers, computer scientists, information technology specialists, database managers, etc.  They are uber-smart, but from what I can tell their uber-smart brains do not work like mine.  From what I can tell, geeks can explain things to me in one of two ways:

1. “To turn your computing machine on, you need to first plug it in. Then push the big button.”
2. “First go to bluberdyblabla and enter c>*#&$) at the prompt. Make sure the juberdystuff is installed in the right directory, though. Otherwise you need to enter #($&%@> first and check the shumptybla before proceeding.”

In all fairness, (1) occurs far less than (2).  But often you get (1) after trying to get clarification on (2).  How to remedy this? First, geeks should realize that our brains don’t think in terms of directories and command line prompts. We are more comfortable with folders we can color code and GUIs that allow us to use the mouse for making things happen.  That said, we aren’t completely clueless. Just remember that our vocabularies are often quite different from yours.  Often I’ve found myself writing down terms in a meeting so I can go look them up later.  Things like “elements” and “terminal” are not unfamiliar words in and of themselves.  However the contexts in which they are used are completely new to me.  That doesn’t even count the unfamiliar words and acronyms, like APIs, github, Python, and  XML.

The dweebs.  Also known as librarians.  These folks are more often being called “information professionals”, but the gist is the same – they are all about understanding how to deal with information in all its forms.  There’s certainly a bit of crossover with the computer types, especially when it comes to data.  However librarian types are fundamentelly different in that they are often concerned with information generated by other people: put simply they want to help, or at least interact with, data producers.  There are certainly a host of terms that are used more often by librarian types: “indexing” and “curation” come to mind.  Check out the DCXL post on libraries from January.

Many of the projects in which I am currently involved require all three of these groups: nerds, geeks, and dweebs.  I watch each group struggle to communicate their points to the others, and too often decide that it’s not worth the effort.  How can we solve this communication impasse? I have a few ideas:

• Nerds: open your minds to the possibility that computer types and librarian types might know about better ways of doing what you are doing.  Tap the resources that these groups have to offer. Stop being scared of the unknown. You love learning or you wouldn’t be a scientist; devote some of that love in the direction of improving your computer savvy.
• Geeks: dumb it down, but not too much. Recognize that scientists and librarians are smart, but potentially in very different ways than you.  Also, please recognize that change will be incremental, and we will not universally adopt whatever you think is the best possible set of tools or strategies and how “totally stupid” or current workflow seems.
• Dweebs: spend some time getting to know the disciplines you want to help. Toot your own horn– you know A LOT of stuff that nerds and geeks don’t, and you are all so darn shy! Make sure both geeks and nerds know of your capacity to help, and your ability to lend important information to the discussion.

And now a special message to nerds (please see the comment string below about this message and its potential misinterpretation).  I plead with you to stop reinventing the wheel.  As scientists have begun thinking about their digital data, I’ve seen a scary trend of them taking the initiative to invent standards, start databases, or create software.  It’s frustrating to see since there are a whole set of folks out there who have been working on databases, standards, vocabularies, and software: librarians and computer types.  Consult with them rather than starting from scratch.

In the case of dweebs, nerds, and geeks, working together as a whole is much much better than summing up our parts.

Survey says…

A few weeks ago we reached out to the scientific community for help on the direction of the DCXL project.  The major issue at hand was whether we should develop a web-based application or an add-in for Microsoft Excel.  Last week, I reported that we decided that rather than choose, we will develop both.  This might seem like a risky proposition: the DCXL project has a one-year timeline, meaning this all needs to be developed before August (!).  As someone in a DCXL meeting recently put it, aren’t we settling for “twice the product and half the features”?  We discussed what features might need to be dropped from our list of desirables based on the change in trajectory, however we are confident that both of the DCXL products we develop will be feature-rich and meet the needs of the target scientific community.  Of course, this is made easier by the fact that the features in the two products will be nearly identical.

How did we arrive at developing an add-in and a web app? By talking to scientists. It became obvious that there were aspects of both products that appeal to our user communities based on feedback we collected.  Here’s a summary of what we heard:

Show of hands:  I ran a workshop on Data Management for Scientists at the Ocean Sciences 2012 Meeting in February.  At the close of the workshop, I described the DCXL project and went over the pros and cons of the add-in option and the web app option.  By show of hands, folks in the audience voted about 80% for the web app (n~150)

Conversations: here’s a sampling of some of the things folks told me about the two options:

Survey: I created a very brief survey using the website SurveyMonkey. I then sent the link to the survey out via social media and listservs.  Within about a week, I received over 200 responses.

Education level of respondents:

Survey questions & answers:

So with those results, there was a resounding “both!” emanating from the scientific community.  First we will develop the add-in since it best fits the needs of our target users (those who use Excel heavily and need assistance with good data management skills).  We will then develop the web application, with the hope that the community at large will adopt and improve on the web app over time.  The internet is a great place for building a community with shared needs and goals– we can only hope that DCXL will be adopted as wholeheartedly as other internet sources offering help and information.

The Science of the DeepSea Challenge

Recently the film director and National Geographic explorer-in-residence James Cameron descended to the deepest spot on Earth: the Challenger Deep in the Mariana Trench.  He partnered with lots of sponsors, including National Geographic and Rolex, to make this amazing trip happen.  A lot of folks outside of the scientific community might not realize this, but until this week, there had been only one successful descent to this the trench by a human-occupied vehicle (that’s a submarine for you non-oceanographers).  You can read more about that 1960 exploration here and here.

I could go on about how astounding it is that we know more about the moon than the bottom of the ocean, or discuss the seemingly intolerable physical conditions found at those depths– most prominently the extremely high pressure.  However what I immediately thought when reading the first few articles about this expedition was where are the scientists?

After combing through many news stories, several National Geographic sites including the site for the expedition, and a few press releases, I discovered (to my relief) that there are plenty of scientists involved.  The team that’s working with Cameron includes scientists from Scripps Institution of Oceanography (the primary scientific partner and long-time collaborator with Cameron),  Jet Propulsion Lab, University of Hawaii, and University of Guam.

While I firmly believe that the success of this expedition will be a HUGE accomplishment for science in the United States, I wonder if we are sending the wrong message to aspiring scientists and youngsters in general.  We are celebrating the celebrity film director involved in the project in lieu of the huge team of well-educated, interesting, and devoted scientists who are also responsible for this spectacular feat (I found less than 5 names of scientists in my internet hunt).  Certainly Cameron deserves the bulk of the credit for enabling this descent, but I would like there to be a bit more emphasis on the scientists as well.

Better yet, how about emphasis on the science in general?  It’s a too early for them to release any footage from the journey down, however I’m interested in how the samples will be/were collected, how they will be stored, what analyses will be done, whether there are experiments planned, and how the resulting scientific advances will be made just as public as Cameron’s trip was.  The expedition site has plenty of information about the biology and geology of the trench, but it’s just background: there appears to be nothing about scientific methods or plans to ensure that this project will yield the maximum scientific advancement.

How does all of this relate to data and DCXL? I suppose this post falls in the category of data is important.  The general public and many scientists hear the word “data” and glaze over.  Data isn’t inherently interesting as a concept (except to a sick few of us).  It needs just as much bolstering from big names and fancy websites as the deep sea does.  After all, isn’t data exactly what this entire trip is about?  Collecting data on the most remote corners of our planet? Making sure we document what we find so others can learn from it?

Here’s a roundup of some great reads about the Challenger expedition:

• National Geographic: James Cameron Begins Descent to Ocean’s Deepest Point
• National Geographic: Cameron’s dive cut short
• National Geographic press release about Cameron’s trip to the bottom
• National Geographic website for the project: Deepsea Challenge
• The Guardian: James Cameron may kill the Kraken but not our journey of discovery
• Spectacular post on Deep Sea News by Craig McCain about the value of this expedition for science and humanity
• Scripps Institution of Oceanography information page about the Deep Sea Challenge
• Stars and Stripes: Deep Sea Dive is Nothing New for the Navy
• US Navy’s Press release for 1960 Trieste trip to the trench

Tweeting for Science

At risk of veering off course of this blog’s typical topics, I am going to post about tweeting.  This topic is timely given my previous post about the lack of social media use in Ocean Sciences, the blog post that it spawned at Words in mOcean,  and the Twitter hash tag #NewMarineTweep. A grad school friend asked me recently what I like about tweeting (ironically, this was asked using Facebook).  So instead of touting my thoughts on Twitter to my limited Facebook friends, I thought I would post here and face the consequences of avoiding DCXL almost completely this week on the blog.

First, there’s no need to reinvent the wheel.  Check out these resources about tweeting in science:

• Wired did a great piece on Twitter + Science, including a list of tweets collected by the piece’s author about why scientists choose to tweet. Don’t take my word for it- read up on what the masses said about Twitter.
• The social media expert + scientist Christie Wilcox (aka @NerdyChristie) created a super duper set of slides about “Why every lab should tweet”; it’s a visual, easy-to-follow way to understand how Twitter could shape your science for the better.
• Of course, the amazing Marine Science blog Deep Sea News posted about Twitter’s power way back in 2010.  Read up on what they say about it.
• The blog Biodiversity in Focus (written by a grad student) recently posted about science and Twitter use, and sums up why it’s valuable in a single word: Networking.
• If you are more geoscience-inclined, check out AGU’s piece on Twitter in science.

That being said, I will now pontificate on the value of Twitter for science, in handy numbered list form.

1. It saves me time.  This might seem counter-intuitive, but it’s absolutely true.  If you are a head-in-the-sand kind of person, this point might not be for you. But I like to know what’s going on in science, science news, the world of science publishing, science funding, etc. etc.  That doesn’t even include regular news or local events.  The point here is that instead of checking websites, digging through RSS feeds, or having an overfull email inbox, I have filtered all of these things through HootSuite.  HootSuite is one of several free services for organizing your Twitter feeds; mine looks like a bunch of columns arranged by topic.  That way I can quickly and easily check on the latest info, in a single location. Here’s a screenshot of my HootSuite page, to give you an idea of the possibilities: click to open the PDF: HootSuite_Screenshot
2. It is great for networking.  I’ve met quite a few folks via Twitter that I probably never would have encountered otherwise.  Some have become important colleagues, others have become friends, and all of them have helped me find resources, information, and insight.  I’ve been given academic opportunities based on these relationships and connections.  How does this happen? The Twittersphere is intimate and small enough that you can have meaningful interactions with folks.  Plus, there’s tweetups, where Twitter folks meet up at a designated physical location for in-person interaction and networking.
3. It’s the best way to experience a conference, whether or not you are physically there. This is what spawned that previous post about Oceanography and the lack of social media use.  I was excited to experience my first Ocean Sciences meeting with all of the benefits of Twitter, only to be disappointed at the lack of participation.  In a few words, here’s how conference (or any event) tweeting works:
   1. A hash tag is declared. It’s something short and pithy, like #Oceans2012. How do you find out about the tag? Usually the organizing committee tells you, or in lieu of that you rely on your Twitter network to let you know.
   2. Everyone who tweets about a conference, interaction, talk, etc. uses the hash tag in their tweet. Examples:    
   3. Hash tags are ephemeral, but they allow you to see exactly who’s talking about something, whether you follow them or not.  They are a great way to find people on Twitter that you might want to network with… I’m looking at you, @rejectedbanana @miriamGoldste.
   4. If you are not able to attend a conference, you can “follow along” on your computer and get real-time feeds of what’s happening.  I’ve followed several conferences like this- over the course of the day, I will check in on the feed a few times and see what’s happening. It’s the next best thing to being there.

I could continue expounding the greatness of Twitter, but as I said before, others have done a better job than I could (see links above).  No, it’s not for everyone. But keep in mind that you can follow people, hash tags, etc. without actually ever tweeting. You can reap the benefits of everything I mentioned above, except for the networking.  Food for thought.

My friend from WHOI, who also attended the Ocean Sciences meeting, emailed me this comment later:

…I must say those “#tweetstars” were pretty smug about their tweeting, like they were the sitting at the cool kids table during lunch or something…

I countered that it was more like those tweeting at OS were incredulous at the lack of tweets, but yes, we are definitely the cool kids.

Oceanographers: Why So Shy?

Last week I attended the TOS/ASLO/AGU Ocean Sciences 2012 Meeting in Salt Lake City. (If you are a DCXL blog regular, you know I was also at the Personal Digital Archiving 2012 Conference last week: my ears were bleeding by Friday night!).  These two conferences were starkly different in many ways.  Ocean Sciences had about 4,000 attendees, while PDA was closer to 100.  Ocean Sciences had concurrent sessions, plenaries, and workshops, while PDA had only one room where all of the speakers presented.  Although both provided provisions during breaks, PDA’s coffee and treats far surpassed those provided at the Salt Palace.  But the most interesting difference? The incorporation of social media into the conference.

There are some amazing blogs out there for ocean scientists: Deep Sea News and SeaMonster come to mind immediately.  There are also a plethora of active tweeters and bloggers in the ocean sciences community, including @labroides @jebyrnes (and his blog) @MiriamGoldste @RockyRohde @JohnFBruno @kzelnio @SFriedScientist @rejectedbanana @DrCraigMc @rmacpherson @Dr_Bik .  I’m sure I’ve left some great ones out- feel free to tweet me and let me know! @carlystrasser).

That being said, ocean scientists stink at social media if OS 2012 was any indication.

First, the Ocean Sciences Meeting did not declare a hash tag – this is the first major conference I’ve been to in a while that didn’t do so.  What does this mean?  Those of us who were trying to communicate about OS 2012 via Twitter were not able to converge under a single hash tag until Tuesday (#oceans2012). Perhaps that isn’t such a big deal since there were only a dozen Tweeters at the conference.  This is unusual for a conference of this size: at AGU 2011 in December, I would hazard to guess that there were more like 200 Tweeters. Food for thought.

Second, I heard from @MiriamGoldste that there was actual, audible clapping when disparaging comments were made about social media in one of the presentations. For shame, oceanographers!  You should take advantage of tools offered to you; short of using social media yourself, you should recognize its growing importance in science (read some of the linked articles below).

Now for PDA 2012. A hash tag was declared (#pda12) and about 2 dozen active tweeters were off and running.  We had dialogues during the conference, helped answer each others’ questions, commented on speakers’ major conclusions, and generally kept those that couldn’t attend the conference in person abreast of the goings-on.  Combine that with real-time blogging of the meeting, and you had a recipe for being connected whether you were sitting in a pew at the Internet Archive or not.  Links were tweeted to newly-posted slides, and generally there was a buzz about the conference.

So listen up, OS 2012 attendees: You are being left in the dust by other scientists who have embraced social media.  I know what you are thinking: “I don’t have time to do all of that stuff!”  One of the conference tweets says it best:

More information…

Read this great post from Scientific American on Social Media for Scientists

COMPASS: Communication partnership for science and the sea. I attended a COMPASS workshop two years ago at NCEAS and was swayed by the lovely Liz Neeley that social media was not only worth my time, but it could advance my career (read “Highly tweeted articles were 11x more likely to be cited” from The Atlantic).

Generally all of the resources on the Social Media For Scientists wikispace

Social Media for Scientists Recap from American Fisheries Society blog

As for how social media relates to the DCXL project, isn’t it obvious? I’ve been collecting feedback straight from potential DCXL users using social media.  Because I have tapped into these networks, the DCXL project’s outcomes are likely to be useful for a large contingent of our target audience.

Academic Libraries: Under-Used & Under-Appreciated

I’m guilty. I often admit this when I meet librarians at conferences and workshops – I’m guilty of never using my librarians as a resource in my 13 years of higher ed, spread across seven academic institutions.  At the very impressive MBL-WHOI Library in Woods Hole MA, there are quite a few friendly librarians that make their presence known to visitors.  They certainly offered to help me, but it never occurred to me that they might be useful beyond telling me on what floor I can find the journal Limnology and Oceanography.

In hindsight, I didn’t know any better.  Yes, we took the requisite library tour in grad school, and yes, I certainly used the libraries for research and access to books and journals, but no, I never talked to the librarians.  Why is this? I have a few theories:

Librarians are terrible at self promotion.  Every time I meet librarian, I’m awed and amazed by the vast quantities of knowledge they hold about all kinds of information.  But most of the librarians I’ve encountered are unwilling to own up to their vast skill set.  These humble folks assume scientists will come to them, completely underestimating the average academic’s stubbornness and propensity for self-sufficiency.  In my opinion, librarians should stake out the popular coffee spot on campus and wear sandwich boards saying things like “You have no idea how to do research” or “Five minutes with me can change your <research> life“.  Come on, librarians – toot your own horns!

Academics are trained to be self-sufficient.  Every grad student has probably gotten the talk from their advisor at some point in their grad education.  In my case the talk had phrases like these:

• “You don’t have to ask me EVERY time you want to run down to the supply room”
• “Which method do YOU think would work best?”
• “How should I know how to dilute that acid? Go figure it out!”

It only takes a couple of brush-offs from your advisor before you realize that part of learning to be scientist involves solving problems all by yourself.  This bodes well for future academic success, but does not allow us to entertain the idea that librarians might be helpful and save us oodles of time.

Google gives academics a false sense of security. Yes, I spend a lot of time Googling things.  Many of this Googling occurs while having a drink with friends – some hotly debated item of trivia comes up, which requires that we pull out our smart phones to find out who’s right (it’s usually me).  But Google can’t answer everything.  Yes, it’s wonderful for figuring out who that actor in that movie was, or for showing a latecomer the amazing honey badger video.  But Google is not necessarily the most efficient way to go about scholarly research.  Librarians know this – they have entire schools dedicated to figuring out how to deal with information.  The field of information science, which encompasses librarians, gives out graduate degrees in information.  Do you really think that you know more about research than someone with a grad degree in information??  Extremely unlikely.  Learn more about Information Science here.

This post does, in fact, relate to the DCXL project.  If you weren’t aware, the DCXL project is based out of California Digital Library.  It turns out that librarians are quite good at being stewards of scholarly communication; who better to help us navigate the tricky world of digital data curation than librarians?

This post was inspired by a great blog posted yesterday from CogSci Librarian: How Librarians Can Help in Real Life, at #Sci013, and more