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Going on the Defensive

June 20, 2014

Had an interesting conversation today. The content of the conversation and the person I had the conversation with is unimportant for this post. It turned out to be a good discussion, and I imagine in the near future I’m going to learn a lot about the topic discussed. But first, I had to open up.

You see, the discussion started with me going on the defensive because my position was being attacked and ridiculed. So much so that I felt that I was personally under attack. My experience is that when someone’s position is attacked, they will more then likely go on the defensive, close up, and not be able to listen to the other person’s point of view, let alone change their mind. All the person ends up doing is defending their own position because it’s under attack. The harsher the attack, the more personal it is, the more you end up defending your position instead of opening up to the possibilities and having a nice lively discussion.

We see it all the time in politics to be sure. Instead of having constructive, lively discussions on the issues, the one side goes on the attack causing the other side to go on the defensive. Nothing gets accomplished. And when it turns personal, it becomes even worse. Not only is the position attacked, but the person as well. You’re busy defending your position and yourself.

And it took me a while to realize that I had closed down. Instead of listening to what the other person had to say, I was searching for the right weapon to fend off the attack, even coming up with “facts” to support my position. But once I realized what I was doing I was able to open up, listen to what the other person had to say, request research articles that I might be able to learn more, potentially grow as a human being, learn something new, and potentially change my position.

There is not one single issue out there that I am 100% on. I have a position, and I can discuss my position. But I’m never 100% positive that I am right. I mean, how could I be. For example, if I have my numbers right, 90+% of scientists are 95+% sure that global warming and global climate change is a critical issue and is human caused. But hey, there’s still that ~5% probability that we could be wrong. Highly unlikely, but possible. The world is a big place with many complex systems interacting together to create our world. And we understand such a small part of that interconnectedness. So how could we possibly be 100%. How could I be so absolute in my positions. I try to approach discussions, debates, conversations in such a way that I will speak to and promote my position, but there is a distinct possibility that I could be wrong. And I’ll be the first to admit it.

So let’s have a lively, constructive conversation on the issues, grow in our understanding and the various perspectives, and be open to the possibilities.

DCO Data Science Day 2014

June 14, 2014

Knowledge Representation

One key point that I heard Mark Ghiorso bring up is related to the fact that there were 20 parameters to a data model that he was using, that he needed to make changes to one or more parameters, but did not know/understand what those 20 parameters were, even with all his years of experience. There wasn’t any information that labeled or described the parameters. The knowledge of these parameters was missing, or had to be searched for.

The point that I took from this is that there needs to be a knowledge representation of the models, the parameters that go into the models. And more then that, the information about the people who put together the model, where the data came from that they use in the model, the software and theories used to create the model, and so on. But semantic representation of the 20 parameters would be ideal. Labels, descriptions, citations, and related documents would be examples of concepts that we could be keeping track of. And in this knowledge store could include information about what would happen if one were to modify the parameters.

When I first started working with the Semantic Web here at the Tetherless World Constellation at RPI we used terms like Knowledge Systems, Knowledge Provenance, Knowledge Store, and even Knowledge Information. Using that word Knowledge meant a lot to me. It was more then just creating directed graphs using concepts and properties, linking things together, creating models, and other engineering tasks. To me it’s more about representing knowledge. This knowledge is in people’s minds, text files on their personal computers, blog posts, scripts that they wrote (probably not well documented.) for the most part the knowledge is in their minds.

As I’ve said for some time now, concepts and properties, the relationships between concepts, are all first-class citizens. I want to be able to ask questions about the individuals to be sure, say a particular dataset, a measured parameter in the dataset, the sensor that measured the parameter, the instrument that that sensor is on, the deployment of that instrument, and so on … but I also want to be able to ask questions about the concepts themselves, the properties, the relationships. Tell me about the concept Cruise. What is an instrument? What is a sensor? Tell me about the idea of measuring parameters by a particular sensor. Describe them to me. Share the knowledge.

Unfortunately this requires someone to enter information. That’s right … data entry. Not a lot of people seem to want to do data entry. Far too busy to do that, not as interesting as the cool science and engineering tasks. Or, in some cases for those with large egos, too important to do data entry. Well, nobody is too important for data entry. Should lead by example anyway. Or, in the least, get someone to do your data entry for you. And we all know what is meant by “I’ll do it later.” It probably won’t get done. And sometimes you just gotta enter the information in by hand, no need to try and create some cool application to do it for you. Just start slapping those keys.

Don’t just declare the concepts and relationships, define and describe them. Don’t just create an individual, label it and describe it. Don’t just do the cool science and engineering, let others understand and follow your cool science and engineering. There are curious minds out there thirsty for not just words, but for knowledge.

Data Science … What?

Some of the presentation given at the DCO data science day contained a great deal of information about the specific science that the presenter specialized in. Deep carbon, extreme physics, deep life, deep energy, volcanology, chemistry, biology, whatever. My take from this … they don’t know, or don’t know how to talk about data science. As scientists they understand very well the special area of research in which they participate. But data? That’s okay … I’m a data scientist and I know very little about solar terrestrial science, oceanography, deep carbon, and other sciences. Well, that’s why we organized this event. And of course I could certainly say, being a data scientist, that everyone should know about data science. Well, that’s because all the scientists collect data, need to do something with the data, store the data somewhere, want their data to be used (eventually), cited. They want recognition for their work, and rightfully so. All of that, and more, is data science. Not that a scientist working in the field of Deep Carbon needs to know how to do any of this, necessarily, but a general understanding of what is data science would be beneficial, specifically data management and the development of data management plans.

21st Century Workshop

One of these days we’ll have a conference/workshop that includes live documentation, not printed on paper, in a folder, in a booklet, whatever. But a live document that has links, is clickable, is searchable.

Could find out information about a person, look at their bio, find out where they do their research, information about the organizations, search for content, search for sessions, search for posters, be able to contact researchers of interest, learn more about research, and more and more. The linked data world.

Day 2 … WOW!

On day 2 of the workshop we presented the DCO Data Science platform. This includes the community portal (Drupal), semantically represented information creation and management (VIVO), data storage and management (CKAN). And I learned a lot. Namely, there’s still a lot to do. Our team has done a tremendous amount of work and have done a tremendous job.

Having a specific number of people working with the tools (Drupal about a dozen and VIVO just a handful, and CKAN even less), is one thing. But getting all the scientists at the workshop to start using the tools? That’s a different story.

Lots of exciting research and work to do here. Lots of great features and community tools are on their way!

The Art of Communication

February 26, 2014

The Art of Communication

Yeah, I know. There’s been plenty written on this topic over the years. I personally wish the art of communication were taught in at least high school. I wish nonviolent communication were taught in high school.

Here’s my philosophy … everyone wants to be heard. Everyone. Not just you. Not just the team lead. Not just the meeting organizer. Not just the professor. And everyone deserves the chance to be heard completely.

The key, in my opinion, to successful communication is … listening. It’s not good enough to just hear someone talking, but to hear what they have to say. It’s not fair to the talker or the listener. That’s what I mean by being heard completely. Listen, don’t interrupt. Sometimes it’s even good to say back to someone what it is you heard them say, just to verify its accuracy. That’s always good in relationship conversations. Don’t think about how you’re going to answer, or what you’re going to say in reply. Don’t even need to react to what they’re saying. Just listen to them, hear them, and try to understand them.

There are some who love to hear themselves talk. They just talk and talk and talk. They interrupt. They don’t let people finish what they have to say. And they make it difficult for people to get a word in edgewise. They make it really difficult for others to be heard, let alone be heard completely. This is unfortunate, whether in a classroom or a meeting room. Hey dude, it’s not all about you.

In an academic environment, yes, there is a teacher, or professor, or TA, who’s job it is to teach. But there are students who want to learn, they want to ask and answer questions, they want to express opinions, they want to convey ideas and thoughts. Just think how rich the conversation would be if everyone had a chance to be heard.

Meetings are my big frustration. Meetings are between 2 or more people. Not just one person. More then likely, everyone in that meeting is very busy, has a lot to do, a lot on their plate. I know I do. And I know that I want to get that meeting over with in as efficient a way as possible so that I can get back to work.

I will be the first to admit that I am not the best at meetings. I am easily distracted and sometimes end up pulling others into my distraction … SQUIRREL. And I promise that I will work really hard at that.

For project meetings, I am very interested in finishing the meetings as quickly as possible, not to take up everyone else’s time, and even try to finish up early. I promise that I will not discuss things in meetings that not everyone needs to be a part of. Not everyone needs to discuss every aspect of every part of the project. So if just 2 or 3 people out of 10 need to discuss something, they can take it offline and discuss it where they aren’t wasting everyone else’s time.

In a recent meeting there were quite a few questions asked during the meeting. I personally felt that just asking those questions was good enough for the meeting. Jot down the question, make sure you know who needs to participate in the discussion and answering of the question, and move on. No need to discuss the actual questions in the meeting. That’s definitely not the case in every meeting or for every question. It was just that in this meeting, which usually lasts a little over an hour on a good day, the questions were being asked, and then discussed and answered by just the 2 people who needed to participate in the discussion. The others in the meeting, including me, just did something else, like actually getting word done for the project.

So here’s my check list.

  1. Talk only when it’s your turn to talk. Don’t hog the entire conversation.
  2. Let other’s talk, be heard, and be heard completely. Don’t interrupt them. Don’t even do the “Mmhmm” thing, while they’re talking. Let them finish. Focus on what they’re saying, not on how you’re going to respond. Even if you disagree with them 100%.
  3. Only talk about what is relevant. Don’t talk about what you’re doing for another project that is unrelated to this project or class.
  4. Only talk about what the entire group needs to talk about. Take smaller conversations offline and include only those people that need to be included.
  5. Just because a question is asked, doesn’t mean it needs to be discussed or answered in the meeting. Again, take it offline and include only those people that need to be included.
  6. And the very most important point … listen. Hear completely what someone has to say.

Oh, one other thing, and something else that I’m going to work really hard at. Pay attention. Even if you have to pretend that you are paying attention. Don’t work on something else, don’t check email, don’t have your nose buried in your iPad, and take off those Google Glasses. I’ve heard people say that they are hearing what is being said while they’re working on something else … but I don’t believe it. Maybe it’s because I can’t do that. Look up, pay attention, and participate fully.

Tell Me a Story

February 10, 2014

A great many times, when we start a project related to the semantic web, people go directly to creating the directed graphs, drawing the data model on the white board. These are classes, those are properties. So the discussion focuses on the creation of the data model, the schema, the implementation, as most of the time people are talking about RDFs and OWL.

But wait … slow down folks. Before we get to implementing anything, tell me a Story. The story is the narrative. It draws out the main ideas that you’re wanting to express in your work. Write about what someone is wanting to accomplish, why they want to accomplish it, what they are researching, measurements they might be using to generate a plot or graph or image or whatever. Describe where they are getting their data, how they are getting it, what they are looking for when they get their data, how they wish they could get their data. Tell me a story using a natural language. Even draw me a picture.

Senator Smith, from the great state of Meh, has just received a PDF of the Ecosystem Status Report from his science team. He reads that the document is generated every two years by various organizations and scientists regarding the health of the environment, tracking changes in key indicators of climate, physical forcing, ecosystem dynamics, and the role of humans in this system. One of the chapters in the document contains information that the Senator wants to learn more about, to discover what information was used to generate a statement in the document.

The Senator clicks on the plot related to the statement. By clicking on the plot he is taken to a splash page for the image, generated from the semantic expression of the plot. Included in the page is information about how the plot was created, from an IPytjon Notebook that loaded a couple of datasets, who created the plot, what role that person plays and for what organization. If he wanted to he could learn that an IPython Notebook is a collection of cells, each cell containing code that is run that does certain things, like loading a data file containing measurements or derived measurements of a particular indicator, and plotting it. The Senator finds out who ran the notebook to generate the plot, who wrote the code that generates the plot, where the dataset came from, the definition of the indicator and other measurements, and continues to click on datasets that were used to derive the current dataset until he gets to the original dataset of measurements taken on a cruise hosted by the Woods Hole Oceanographic Institution with PI Tony Sullivan.

There’s the story. The story is what we need to start with.

From this story we can then pull out the information and relationships that we need to model. But still we’re not talking about a data model. No! What we want to do now is develop the information model. List the concepts. List the relationships between the concepts. What do we want to keep track of and what do we want to link together?

From the story above we see that there is a ecosystem status report, which is a document. That document has chapters. And in the chapters there are images, graphs, plots, citations, references to other documents  and referenced datasets. The plots, images, graphs, etc… are clickable. The plot was generated from the IPython notebook that has cells, authors, and someone who ran the notebook. The plot was created in a cell in the notebook, the cell has an author and the cell loads a data file in from a dataset derived from datasets collected on a cruise run by WHOI for a project with a PI. The data can be traced back to an organization, and funding information (should have citation information and licensing information as well). On the cruise was an instrument with a sensor attached that collected the data that is in the dataset. The cruise is a deployment of a ship that is owned by WHOI and was, for this deployment, captained by Captain Phillips.

Wow, this use case contains a LOT of information.

The creation of this story is one of the first pieces of the semantic web iterative methodology that was developed at the Tetherless World Constellation of Rensselaer Polytechnic Institute by Peter Fox and Deborah McGuinness.

Semantic Web Methodology and Technology Development Process

Semantic Web Methodology and Technology Development Process

Another first step in the process is bringing together a small set of diverse people from various fields who will participate in the telling of the story and the implementation of the story. In many of these projects I play the role of the information modeler. Sometimes the project manager. In some I play the role of the system architect, and in many the role of a software engineer. Those last two are my favorite, by the way.

As a software engineer I see a whole set of software use cases related to this story. So I use the term use case different from the way the methodology describes it. I would consider the methodology use case as the story. And from the story you develop a whole set of implementation use cases, project management use cases, modeling use cases, etc…

Each of these use cases are much, much smaller in scope and fit the classic definition of a use case. The use cases are formalized, link to requirements documents and specific implementation information. Pieces of that are turned into tickets that are assigned to an individual and are clear in their expectations. These tickets can be organized and prioritized, fit into a schedule, assigned to an agile sprint, and can eventually be marked as completed.

Once the tasks for a use case are completed the use case can be marked as completed. Once the use cases for the story are completed the story can then have a happy ending. At each step a sense of accomplishment at the completion of something.

All that information that I talked about in the beginning is more then just a technical accomplishment.  The information represents knowledge. There’s information about all that went into the completion of that Ecosystem Status Report so now it can be recognized  and referenced. The importance of each step is revealed, how the information is being collected, who collected it, and the value of all of that information and the value of each of the steps. An appreciation for the hard work and the resources that went into the report are more apparent.

ESIP Winter Meeting 2014

February 3, 2014

Another great meeting. Had a lot of great conversations, a lot of good meetings, met with collaborators, good sessions to attend.

And starting right out of the gate: First speaker. The speaker asks a lot of great questions related to software in the sciences. He talked about software being publicly available, in code repositories like subversion and git, having unit tests, etc. The exam that he had us take was very interesting, asking us questions like “how many of you use code repositories?” and “how many of you write unit tests?” But there’s additional questions that could be asked.

  1. How many of you use formal documentation practices such as javadoc, doxygen, pydoc, etc…
  2. How many of you can write a formal use case document
  3. How many of you use a content management system to document installation, configuration, architecture documentation, technical infrastructure and decision making documentation.
  4. How many of you have an expressive representation of your software so that it can be referenced or cited elsewhere. (It’s one of my own goals)
  5. How many of you have gone through a formal code review process.

There’s representing the software in such a way that it can be referenced and cited. By represented I’m talking about an expressive representation using RDF. OPeNDAP Hyrax is a software package that includes various software components, dynamically loadable modules. Each of these components and modules can be represented, as can the versions of the software components, and even the installation and configuration of the software. And the URI that represents the installed and running piece of software can then be used in provenance capture.

Let’s not forget licensing and citation information either. A representation of the license and information on how to cite the software that was used to generate data products.

Expressing the data vs. sharing the data

Now there’s an interesting concept. The speaker (Kevin Ahsley) stated that there’s nothing that says you can’t say that data exists without making the data available. In other words, there’s lots of scientists out there who don’t make their data available, nor do they even state that the data exists. Why not? You can say that the data exists, even have representation of the data with at least some basic metadata and/or semantic representation, without making the data itself available. Nicely put.

Speaking with Kevin later in the conference, he wondered how many times a certain question had been raised and researched, failed, but never documented or shared. Because the research and data aren’t expressed in any way, even stating that it existed, can cause things to be re-researched, re-examined, etc… Why waste the time, or spend the time. Or, by making that information available, perhaps someone can say “I was going to do that, but what if we tried this instead.” So instead of repeating the error, trying a different approach.

And that sends me back to a conversation I was having with Peter Fox a while ago, talking about research. The idea of research is that you have a question that you want to explore. You might not know how to approach the problem or the question, how to proceed, or what the results will be. But you have that question. The goal of the research isn’t to succeed, in that you are trying to prove something, but to succeed in that you’re performing the research. The experiments might fail. The research might fail. You might learn that what you had originally thought isn’t really accurate, or the “truth”. That’s all part of the research. Share your data, share your results, and express the data and research semantically so we get a nice rich expression of information.

Information model vs. Data model

I think I’ve talked about this before, but maybe bears to be talked about again. There is no need to come to an agreement on terms anymore. Believing that we have to have one model to rule them all would be considered closed world. The Semantic Web is Open World. If we have terms that we determine to mean the same thing, and have the same relationships with other terms, then we can equate the terms.

One thing that I like to do when first getting into a project with a new group is that we first generate the use case story. And from the story we come up with the information model, the concepts that we want represented, and the relationships between the concepts. Not data modeling, not writing a schema, not an ontology or anything, but an information model. In the back of my mind I might know of an ontology that we could use, but I don’t bring it up just yet. I jot down some notes. The goal at this point is to come up with the information model, the things that we want to formally represent, and their relationships.

Later, when we begin to formalize the information model, and decide on the representation (OWL, RDFs, relational schema, whatever, though tending more towards more semantic representations these days, so RDFs at least), in other words, developing the data model, even then I don’t necessarily use someone else’s terms or another ontology. We stay in the context of the user story, the way that they talk, the terms that they use, the relationships, etc… Stay with what they know. Again, we can keep in mind various ontologies that we might want to utilize, but we make sure we stay with the terminology that the user is used to.

At some future time, then we start utilizing other ontologies and their capabilities, their terminology, their meaning of terms. But we do this with the users, with their knowledge, with their buy-in.

I think, it’s not the term, necessarily, though that is very important because words themselves carry meaning for people, carry emotion, and very specific perspectives. But it’s more about the meaning behind the words, the terms. Encoding that perspective, encoding the meaning.

And when you decide to use another ontology, you are deciding to use the meanings of the concepts and relationships, not just the terms. So understanding the meaning of the terms is very important.

And here’s the reason I’m talking about this. We’re talking about PROV-ES. It seems that the usage of some of the terms is inconsistent with the meaning of the terms as decided by W3C. For example, it seems that the PROV-ES team equates a prov:Agent with a foaf:Agent. And that is not the case. A foaf:Agent is really a prov:Entity. A prov:Agent is a foaf:Agent that is performing an action. For example, a piece of software would be considered a prov:Entity. Let’s say OPeNDAPHyrax is-a prov:Entity. The running of that piece of software would be considered a prov:Agent.

Here’s what Tim Lebo said: “An agent is something that bears some form of responsibility for an activity taking place, for the existence of an entity, or for another agent’s activity. So yes, Agents “steer” Activities and are “responsible” for the Entites that they influence. I go by the rule of thumb that a single code file (Entity) invoked many times is many Agents, and the code file is distinct from each of the agents. Whether you say the agents are the same is up to your needs.”

A prov:SoftwareAgent has inputs and outputs and uses a particular configuration, the environment in which it is running. It’s the software plus something else.

So PROV-ES saying that a NASA project is a prov:Agent is inaccurate in that they were really saying that a NASA project is a prov:Entity instead.

Discovery and Search

Perhaps I’m missing something here as well. It seems to me that the discussion still revolved around data granule browsing and search. Within a given portal, now that the user knows of the portal, find the data that the user is interested in. Here’s some search terms, keywords, etc, etc…

But how did the user get to that portal? How did they discover that this portal, that portal, and that other portal, contained data that they might be interested in? This is what I would consider discovery. And I go back to the graphic that Peter Fox has used in the past. There’s three levels. There’s discovery, there’s browse and search, and then there’s access. All are really forms of discovery, even access. I’ve found the data, now look inside. Discover what’s there, the representation, and what tools can be used to access, manipulate, transform, and visualize the data. Even that’s a form of discovery. But to me, discovery is finding the various data portals, catalogs … virtual observatories, that have information that I might be interested in.

The question, I’m looking for a particular piece of information, but don’t know where to start looking. So I need to discover where to look. Google, Wikipedia, dictionary, thesaurus would be the results of that inquiry. Now that I know where to look I can begin my search for the information. Once I’ve found some virtual observatories, then I search through and browse their holdings. And once I find the data that I’m interested in, then I access it.

Ahh … just thinking out loud!


Imagine if all of our projects are expressed in the knowledge store. All the working groups in the project. All the meetings for the project. All the collaborators working on the project and what they are doing specifically for the project. All the publications and presentations from the project. All the events attended that reference the project. All the announcements for e project too. And so on.

Imagine if all the people in the lab are represented; a bio for them and a current bio picture; announcements about accomplishments, awards, appointments, etc…; all their publications and presentations; all the projects they are working on; all the classes they are attending, teaching, or supporting; all the events they attend. So here’s the visualization:

I go to Peter Fox’ page, and see his picture, contact information, additional pages to visit for more information, a bio, and his current roles and affiliations, and his interests and skills. There’s a link for announcements, and that list is dynamically generated from the knowledge store. Same with his publications, presentations, and events attended, currently attending, and future events. A page for the projects he’s working on, and his role in the project. So we have events, announcements, publications, presentations, projects, classes being taught, uhhh … what else?

To see a list of events that members of the lab are currently participating in or will be participating in. Awesome. The list of papers, publications, presentations that anyone and everyone in the lab has authored or co-authored. The list of projects we’re currently participating in. And so on.

And it’s all linked together with other representations, other sites, other data, other semantic representations (linked open data). Ahh … just imagine.

Imagine how impressive it would be if all of that were semantically represented in the Tetherless World web site.

Wait a minute … it is. Well, a lot of it is. Hmm, so what’s missing? Oh yeah … the one thing that people just hate to do … data entry!

Comment: The Semantic Web, to me, is all about expressing knowledge, sharing knowledge, which is why I like to call it Knowledge Store instead of triple store, Knowledge Provenance, instead of just provenance, etc… We can automate, we can write scripts, make it all machine readable, but in the end, it’s all about providing knowledge to humans, sharing knowledge, even sharing experiences.

It seems that people try to automate things in order to keep from having to do data entry, when what we really need is for people to do more data entry. Just do it. Take the time and do the data entry. It’s worth it in the short term and in the long term. Enter a good description, enter as much relationship information as you can, enter as much as you can. Once you say “Oh, I’ll get to that later.”, later becomes ‘probably won’t do it.’

Just Words

December 25, 2013

What if people recognized that words are just that, words. They are bits displayed on a computer screen in the shape of letters, or scribbles and scratches on a piece of paper, or vibrations emanating from a person’s vocal chords that are recognized as speech by the recipient, and so on. They are an agreed upon manifestation shared by a group of people to express thought.

And what if I could say the words “Happy Holidays” and it means that from the bottom of my heart I express my desire that you have a joyous and loving celebration of whatever your belief is of this season? My intention is to express my heartfelt hope that you enjoy this time of the year in whatever fashion you chose to celebrate it. The words are a simple, rudimentary manifestation of that intention, of that desire.

What if you could say to a Jewish friend “Merry Christmas” and, instead of that being interpreted to mean that I look to enslave the world by forcing someone to follow a particular religious belief system and that all other belief systems are wrong and anyone who follows said belief system should be taken out back behind the chemical shed and shot, that they interpret it instead to be a heartfelt desire from me to them to have a joyous celebration of their belief system, that I’m expressing my love for them and their family, that my intention is purely one of love.

What if a Jewish friend could say Happy Chanukah to you, and instead of you taking offense to that and thinking that they are trying to convert you to their way of thinking and belief system, that instead you interpret it for what it really is, that they are wishing you the love and joy that they share during their celebration onto you, that they love you and hope you too share in the love.

I am neither Jewish nor Christian, Muslim nor Buddhist, or anything. Yet I take absolutely no offense when someone says to me Happy Chanukah, or Merry Christmas, or wishes me a joyous Al-Hijira, or simply says Happy Holidays. Thank you for your well wishes!

So it’s not a War on Christmas, as some would say. It’s not an attack on any one person’s beliefs or intended to be negative in any way.

The words are simply a manifestation of the intention of wishing me the best. And for that, I am eternally grateful. I look at the meaning of the words and the meaning of their relationship with the person saying the words.

With that said, I hope you all had a wonderful, loving, joyous Winter Solstice, the re-birth of the physical sun as the days become longer, and the re-birth of the spiritual sun within you.

AGU Fall Meeting 2013

December 20, 2013

AGU Fall Meeting 2013 is over. THANK GOD! Well, to be clear, before I left for AGU I was really excited to be going. I was looking forward to the sessions, the posters, the talks, the collaborations, learning more, making new connections, and more. It’s sooooo much fun. And I loved being in San Francisco, at AGU, with sooo much to do and sooo much to learn. But, by Friday morning, I’m ready to go home. It is mentally and, for me at least, emotionally and physically exhausting. A great conference, but glad to be back home.

Presentations, Project and Prepare!

Wether an oral presentation or a poster presentation there are some very important things to remember. One of them, when you are speaking … PROJECT! Don’t talk to the microphone, talk to the person in the furthest corner of the room. Talk to the audience, not the screen, not your slides, not the session chairs, not the person in the front row. Talk to the audience. Repeat questions so the rest of the audience can hear, then answer the question.

Of course, it helps to have your slides in front of you. Unfortunately at AGU, the screen is to your right or left. Hmm … next time, I’m taking up my iPad with the presentation on it. Notes and all. Set up the iPad on the podium and only look when I have to. I like that idea … I’ll add that to my meeting prep notes.

So this means PRACTICE. Just writing your presentation isn’t good enough. You need to practice it many, many times. Make sure you’re within the time frame. Make sure your slides flow. Present to your family or friends. Or give a practice talk in the lab. There’s no reason why you can’t talk to the audience instead of presenting to your presentation (looking at your notes or the screen.)

Even if presenting a poster, practice. Practice give information about your work. Pointing out the main points of your poster, the key things you want people to take away after visiting your poster. Practice that.

This year at AGU we had what was called a flash mob, though it’s not what you think. Informatics folks were invited to gather at an informatics poster session. We started at one poster, moved on to the next poster, etc… Each presenter got to give a 5 minute presentation about their poster and their work. This would be a great practice presentation. The key is to make sure your audience understands the key points about your work.

Be ready!

I remember at a conference a while ago someone who was presenting during a session saying, “I use Linux, I don’t understand Windows”, regarding the fact that their presentation was being run on a Windows machine. Something similar happened at AGU this year.


If you know computers, then they are fundamentally the same. Mouse, keyboard, screen, CPU, memory, disk, etc… There’s menus for applications whether you’re on a Mac, a PC, or Linux. When you run a presentation, it’s basically the same. You run the slideshow, and you either use the arrow keys to move between the slides, or click through. There … DONE.

But this person was convinced that there was a HUGE issue because they knew Linux, not Windows. And they convinced others that that the presentation wasn’t going to be that great, I think.

With that in mind, read the information about presenting at a conference. Whether a poster presentation or oral presentation, follow their guidelines, follow their suggestions, be ready. Make sure you’re slides will run on their system, that you’re using the right software, and so on.


Your area of study is not the focus of everyone else’s study. I heard a gentleman, after a good talk, say that the focus of the presenter’s study should be “The Cloud”. Great … you go off and study the cloud, big data, whatever. I’m happy there’s people who are wanting to do that. Very exciting. Sounds like you are very passionate about that. I’m not. And neither was the speaker. The speaker had a very good response to the gentleman. “Yes, it is a very important topic, but that’s not my focus.”

We’d all love to have everyone interested in our work, and we might have opinions about what is important. I think documentation is really, really, really important. I think everyone should participate in data entry, even if it isn’t the most exciting aspect of research or science, I believe it’s vitally important. Without documentation and data entry then you might find yourself isolated. In other words, you and maybe a few others will understand what you’re doing and why. I believe we need to engage other scientists and researchers, but also educators, students, policy makers, and so on. So making sure your research, your data, your processes, your software, your reasoning, your findings, are documented, that you’ve entered your information about your work, I believe that that is important and everyone should do it. Should be part of everyone’s work. See my blog post

But I’m not going to assume that this should be everyone’s focus. And I know for a fact that it’s not popular.

And there are plenty of areas of research. Not everyone can work on the same area. There are people who need to research global climate change, there are people who need to research the impact of global climate change, and there are people who need to research what we can do to eliminate the human impacts on global climate change. Ocean sciences, solar sciences, terrestrial sciences, geophysical sciences, biological systems, ecological systems, and so on. Too many to list here. Lots to do, and plenty of people to do it. Unfortunately, the funding is getting a little tighter.

Practicing what we preach

Okay, maybe preaching is a bit strong of a term, but you know what I mean. The Tetherless World Constellation is all about semantics. It’s all about very expressive representations of many different concepts and relationships. We have projects related to ecology, earth and environmental sciences, health, first responders, linked open government data, web science, information technology, social sciences and social media, and much, much more.

So do we think we should have a rich representation of the lab and all that we do there? Presentations, publications, talks, courses, people, projects, organizations, software frameworks and components, semantic tools, the lab infrastructure and inventory, people’s skills … Everything, right?

The answer is yes, we should have that. We do have quite a bit of that going on already, and there’s more that we can do.

So imagine next AGU when we have 6-7 people attending again with a dozen or so presentations. Imagine that each of those presentations has a URI, and that each presentation has a QR code in it somewhere, resources slide for a oral presentation, on each of the posters. Someone scans that QR code and browses to the URI, and gets a nice display of the presentation, including citation information, author list, event information, related information such as projects, research areas, and concepts. And, of course, a way to download that document. Or if they use a tool to pull back RDF from that URI and get the document description, then our work becomes machine readable as well.

Oh wait, we already have that. Unfortunately there were only a few presentations with QR codes this AGU. And I messed mine up by using the Drupal page URL instead of the document URI. Oops! So maybe next year we could do this? Again, guess I’ll add that to my meeting prep notes. Make sure everyone has a QR code for their document URI.

So we do have some representation of our work, but there is so much more to do. Software tools and packages that we’ve developed, our lab infrastructure, better descriptions and bios, more concepts that we can tag things with, smarter ways to represent this information visually, cool graphical visualizations of our lab. Ahhh … so much exciting work yet to be done. This field of study is quite fascinating, and quite fun.

Well represented

The Tetherless World Constellation was well represented at AGU Fall meeting 2013. The event page,, lists all of our presentations, talks, and posters. Everyone who gave oral presentations … great job. Thanks to Evan Patton for stepping in at the last moment and presenting at two oral sessions and one poster session. Thanks to everyone for helping to staff the Academic Booth that we had set up in the exhibitors hall. And thanks to Professors Peter Fox, Deborah L. McGuinness, and James Hendler for giving us the tools and know-how to have such a great presence there.

And a lot of great sessions to attend this year on a wide range of informatics topics. Semantics, provenance, data stewardship, and more. I learned a lot this year to be sure, all the great projects going on out there surrounding earth science informatics.


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