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13:45:33 <LarryHunter> Lesson 1: User Centered Design is important to this community, and I need to read Steve Sawyer
13:45:55 <Pat> Pat has joined #diw2012
13:45:55 <LarryHunter> Lesson 2:New tools, IRC for collaborative work and Mendeley for bib management
13:46:14 <Susan> Didn't know that the distinction between science and discovery was so important.
13:46:45 <LarryHunter> Lesson 3: NSF might actually fund my work, since while the applications are biomedical, the computer science is generalizable and may be of interest to them
13:47:01 <Susan> I found the challenges in social science "discovery" extremely interesting... and would like to follow up on them!
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13:47:47 <Kerstin> Strong emphasize on biology,in oposition to other meetings where physical  sciences dominate
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13:48:46 <Karsten> +1 on bio emphasis
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13:50:25 <PaoloCiccarese> 1) Importance of usability and user centered design
13:50:38 <helenadeus> Lesson 1: formal knowledge representation is really important, but as a starting point, not an endpoint
13:50:46 <helenadeus> Lesson 2: Don't ask scientists what tools they need because they don't know
13:50:54 <Yolanda> 3 points from NIgham
13:51:12 <helenadeus> Lesson 3: Workflow can be used not only for integrating computational tasks, but human tasks as well
13:51:26 <Yolanda> 1) tradeoff between formal representations and the effort they take, can we quantify/formalize that
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13:51:43 <Susan> Demonstrating utilty is tricky
13:51:56 <Yolanda> 2) determining impact of tools is hard -- how do we measure impact
13:52:34 <Yolanda> 3) Need to work with scientist to design appropriate tools (Turbotax example)
13:52:47 <Yolanda> Kerstin points
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13:53:03 <Yolanda> 1) diversity of backgrounds in meeting (I think that is what he said)
13:53:34 <HuanLiu_> @Huan 1 - Discovery has its many definitions and types 
13:53:35 <Yolanda> 2) Scientists often perceive CS as not research
13:53:50 <Kerstin> where DOE program managers invited
13:53:57 <Yolanda> 3) had not realized the importance of knowledge representation (in climate)
13:54:03 <Karsten> 1) I did not realize there had been so many "success stories" in other science domains -- helps misperception that CS is 'just a service'
13:54:06 <Karsten> 2) Wasn't aware of the many different working definitions of "discovery" -- first hour felt like trying to define and scope
13:54:10 <Karsten> 3) Realized how little attention I have paid to knowledge representation -- we often just "make it work" for the task at hand
13:54:12 <Yolanda> Loren's 3 points:
13:54:43 <Yolanda> 1) many people did not know of success stories
13:54:50 <Yolanda> 2) -- I missed that
13:54:58 <Pat_> People were far less focused on induction from large data sets, and more balanced in their concerns, than I expected. 
13:55:22 <Yolanda> 3) hard to work together when people have diverse backgrounds
13:55:29 <Loren> We all have unique knowledge.
13:55:34 <Yolanda> 3 pints from Evelyn
13:55:44 <Yolanda> 1) importance of discovery for so many people
13:55:50 <Yolanda> 2) importance of social computing
13:55:52 <LarryHunter> +1 to three pints
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13:56:01 <HuanLiu_> @Huan 2 Social computing may be able to help discover global knowledge from local findings in a tangible way
13:56:10 <ChrisS_> +1 to pints (of beer)
13:56:27 <Loren> Learning: We all have unique knowledge.
13:56:41 <Loren> Learning: Social computing as a first class computing method.
13:56:46 <Yolanda> 3) contrast between industry and academia: a) in industry anyone can be an innovator
13:56:52 <Pat_> Analyses of the entire scientific process can serve as organizing frameworks for research on computational discovery. 
13:57:00 <Loren> Learning: Renewed emphasis on working on meaningful issues.
13:57:30 <Loren> Learning: Difficulty of getting a group of people to work effectively and constructively together in a meeting.
13:57:31 <Kerstin> would have liked to define the boundaries of social computing applicability more clearly
13:59:26 <PhilBourne> Breadth of expertise
13:59:29 <Susan> breadth of expertise... some essentially addressing same problem
13:59:52 <Susan> know should be sharing data, have good data formats... but either quick and dirty or formalize
14:00:11 <HuanLiu_> @Huan 3 So much interest in social computing and so much expection of its power.
14:00:12 <Susan> are there enough commonalities to formalize and build shareable tools across communities
14:00:15 <PhilBourne> sharing data either quick and dirty or fully formulated - makes sense to formalize sharing tools and then adapted to communities
14:00:29 <PhilBourne> above is from Alex
14:00:41 <Susan> dilemma, fancy CS tools not being used.  User-centric design approach might be the right thing to do.
14:00:47 <PhilBourne> Vipin
14:01:11 <PhilBourne> so many aspects of informatics that I did not know about
14:01:14 <Susan> so many other aspects than DM/ML in informatics and how CS can help
14:01:31 <Susan> got to know people in other areas coming from different aspects
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14:01:51 <Susan> commonlatiy of experiences in room
14:01:57 <PhilBourne> commonalityin experience s in different doamins
14:02:06 <Karsten> Alex Schliep: 1. breadth of expertise, but have the same type of problems 2. know we *should* use formalism, but usually do it quick and dirty 3. bioinformatics has lots of nice tools, but many of them dont' get used very much -- user-centered design could help this.
14:02:13 <PhilBourne> astronomy a good model for users vs experts many users per expert
14:02:26 <Susan> need success stories to sell to colleagues in other disciplines... CS is not a service discipline
14:02:27 <DavidJensen> Incentives for participation really seems like an important issue.  Researchers may not *want* to share workflows, data, and tools.  They may see these as their competitive advantage.
14:02:47 <PhilBourne> Yan
14:02:49 <PhilBourne> computer science viewed as a service discipline must work to change that
14:03:19 <Susan> started working closely with one biologist on single problem, not that many people cared about those results
14:03:51 <Karsten> Vipin Kumar: 1. breadth of interests, not just data mining and machine learning 2. so much in common among people working with very different scientific disciplines 3. we need more success stories to change perception that computer science is a service
14:03:54 <DavidJensen> Journals are a carefully worked-out balance between cooperation and individual reward.  Social computing for DI is a whole new realm where we'll need to work out the incentive structures.
14:04:00 <PhilBourne> unsatisified with helping biologist of a very narrow problem - happier working across disciplines
14:04:10 <PhilBourne> challenge is to find teh right people to work with while thinking of teh problem at the same time
14:05:05 <PhilBourne> the social phenomenon is changing our lives 
14:05:48 <PhilBourne> Need still better methods to connect people - how can we utilize teh social perspective
14:06:18 <Karsten> Yan Liu: 1. not satisfied solving focused problem with narrow focus -- trying to help the broader community 2. difficult to find scientist willing to work with 3. take advantage of the social revolution to connect the right people, define problems
14:06:24 <DavidJensen> Very interesting point from Yan Liu: Social computing could help crowdsource data gathering for sociology of science.
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14:06:51 <PhilBourne> Carla: impressed with preparation of workshop
14:07:03 <LizBradley_> Carla: learned about new systems
14:07:08 <PhilBourne> learnt alot about new systems
14:07:14 <susan> QED:  quasi experimental design
14:07:15 <PhilBourne> qed
14:07:30 <susan> socia computing interesting
14:07:35 <PhilBourne> emphasize social computing as learning a lot
14:07:53 <Yolanda> Carla 1) lots of new information even before workshop
14:07:58 <PhilBourne> a few people mentioned inference and reasoning - not enough
14:07:59 <Yolanda> 2) importnace of social computing
14:08:13 <susan>  formal reasoning not mentioned as much as expected
14:08:22 <Loren> I forgot to say: I think there's still a tension between problem-centered and technology-centered research.
14:08:40 <PhilBourne> bringing human computation as a way to do reasoning
14:08:43 <susan> brinign human computation as a way of doing reasoning, coupling machines with human 
14:08:44 <Yolanda> 3) human computation as a way to do reasoning (social computing)
14:09:01 <susan> Phil
14:09:08 <Karsten> Carla Gomes: 1. Great workshop organization, gather input ahead of time 2. learned a lot about other systems, new definitions -- especially social computing 3. surprised not too many people mentioned reasoning and inference 4. human computation as a way of doing reasoning is an interesting concept, coupling with machines using the strenghts of each
14:09:12 <susan> (cynic)
14:09:24 <DavidJensen> One of the outcomes of studying the foundations of discovery informatics may be a far better understanding of scientific reasoning in general.
14:09:35 <LizBradley_> Phil 1: absurd that we need discovery tools to discover discovery tools
14:09:40 <susan> absurd that need discovery tools to create discovery tools
14:10:13 <LizBradley_> tools are not advancing very quickly
14:10:29 <LizBradley_> integration of data is the key first step
14:10:52 <LizBradley_> Emphasis on understanding failure is good
14:11:14 <Kerstin> + integration of data, processes and people
14:11:14 <susan> Raul: I learned that way-more scientists use discovery tools than there are computational scientists who can hand-hold them.
14:11:21 <LizBradley_> Emphasis of success of cyberinfrastr overstated
14:11:30 <LizBradley_> Take home point: we are at a tipping point
14:11:38 <susan> Raul: Science needs "TurboTax-style" discovery tools that (1) understand scientists's workflows; (2) are expert, automated guides, and (3) are really, really simple to use.
14:11:40 <Yolanda> Phil: success of CI may have been overstated
14:11:44 <LizBradley_> Catalyst often comes from something that we don't expect
14:11:59 <Yolanda> Phil: we are at a tipping point, after this meeting even more convinced
14:12:25 <susan> cognition may be changing more than I realized
14:12:26 <Yolanda> Phil: role of cognition research
14:12:39 <susan> simplicity, usability, reward will always rule what scientists decide to adopt
14:12:40 <DavidJensen> I was happy to see abductive inference in some of the introductory slides — it's an under-appreciated and fundamental description of much scientific reasoning.
14:12:50 <LizBradley_> Simplicity usability reward will always rule whether scientists adopt something
14:12:55 <susan> Turbotax will not quite do it for scientists.
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14:13:25 <susan> Pirates of Silicon Valley is a great movie
14:13:37 <susan> Pat
14:13:55 <LizBradley_> Pleased that induction from large data sets didn't dominate
14:13:59 <susan> Pleasantly surprised that abduction from large data sets was not so dominant
14:14:02 <PhilBourne> pat: not obsessed about big datasets induction
14:14:17 <PhilBourne> People not aware of success stories
14:14:23 <susan> don't know about long history of work in discoveryin science... wrote survey paper on 8 success stories back in 2000
14:14:34 <susan> missing:  search metaphor
14:14:56 <PhilBourne> Teh search metaphor was missing - what is teh space and how do you constrain it
14:15:10 <LizBradley_> Study discovery in the context of the whole scientific enterprise
14:15:12 <susan> You don't want to study discovery by itself but in context of entire scientific enterprise
14:15:19 <susan> Chris
14:15:21 <PhilBourne> Study discovery in the context of teh full scientific enterprise
14:16:00 <susan> nice to see so many people interested in this
14:16:04 <susan> fragmentation in comp sci is worse than thought
14:16:14 <PhilBourne> Chris: fragmentation in teh computational sciences worse than I thought
14:16:34 <PhilBourne> Chris: NSF truly thinking about the problem
14:16:37 <susan> teams should be interdisciplinary
14:17:20 <susan> 53
14:17:29 <PhilBourne> 5-7 years from a good solution
14:17:30 <DavidJensen> Hod's point about "garage science" raises an interesting thought for me: Can social computing and DI create a *better* incentive structure for research, so that we can massively broaden who can meaningfully contribute to important science.
14:18:09 <susan> domain scientists should be on team to create useful tools
14:19:08 <susan> Steve
14:19:13 <PhilBourne> Work with a scientist not the science in the broader sense
14:19:18 <susan> Feels like kid invited to Santa's workshop
14:19:31 <PhilBourne> Steve: Santa's workshop
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14:19:54 <ChrisS> 1.	Fragmentation in the computational sciences even worse than I thought
14:20:02 <susan> impressed with thoughtful automatization attempt, thoughtful thinking of how to use CS thinking in science
14:20:05 <PhilBourne> Thoughtful automation and how to use computational ability in science
14:20:14 <susan> delighted that didn't turn into BIG science, data
14:20:24 <ChrisS> 2.	I thought all of NSF already knew that domain people need to be on the teams that build things meant to be used.
14:20:32 <susan> ddin't know the fascination with models
14:20:46 <PhilBourne> Fascination of models was not expected
14:21:04 <susan> study of science:  people who best benefit don't get to see it as much
14:21:05 <ChrisS> 3.	Badges (public acknowledgement in labelled form) and computer-supported competitions for data analysis and reviewing is going to be the wave for the future.
14:21:15 <PhilBourne> What is teh takeaway as I move into my craft
14:21:15 <susan> discussion between science and discovery was interesting
14:21:26 <susan> TurboScience
14:21:42 <susan> could be in a high school in 10 years
14:21:48 <PhilBourne> TurboScience - likes that idea- good be in high school in 10 years
14:21:58 <susan> collaborations, human/non-human, how can we collaborate with tools better
14:22:05 <susan> broadern participation in science through social computing
14:22:24 <susan> more people = more energy in science, should be more visible
14:22:28 <DavidJensen> Having a scientist on your project and trying to solve his or her problem is only *one* model of how to do good work in DI.  For example, you could work on a problem that has been previously (and well) defined by another community.  There may be other successful approaches.  Let's not become a monoculture.
14:22:34 <PhilBourne> Very excited by broadening participation in science - more energy comes from more people - science shoudl be a more visibkle enterprise
14:22:41 <susan> alex
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14:22:44 <susan> metrics for discovery
14:22:50 <susan> discovery is a diverse concept
14:23:05 <susan> many issues are more sociological than technological
14:23:26 <Kerstin> + 1 social aspect much bigger hurdle than technical in many cases
14:23:26 <PhilBourne> Alex: Metrics for discovery is hard discovery is a diverse subject teh human factor is still too large pleasantly surprised by amount of understanding and belief in citizen science
14:23:28 <susan> lot to do on the front of models
14:23:38 <susan> simplicity is good, e.g. user interfaces
14:23:44 <susan> didn't hear a killer app
14:23:47 <PhilBourne> takehome - simplicity of user interfaces - no killer app
14:23:59 <susan> education will drammatically change in next 10 years
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14:24:04 <susan> Cecelia
14:24:11 <PhilBourne> Education will dramatically change in teh next 10 years and we can not be isolated from that
14:24:15 <PatLangley> Many people are unaware of the long history of work on computational discovery in scientific domains, and of the many success stories, so we need to do a better job of advertising. 
14:24:19 <susan> people approaching people from different angles
14:24:43 <susan> importance of human, user-centric design
14:24:51 <Loren> Just came across my twitter feed: An online social network for "pre-submission" peer review of scientific articles: http://news.sciencemag.org/scienceinsider/2012/01/online-social-network-seeks-to.html
14:24:57 <PhilBourne> Cecilia - pleasantly surprised about teh recognition of teh importance of teh human design
14:24:57 <PatLangley> I was surprised that the metaphor of heuristic search was generally absent from the discussions. \
14:25:24 <susan> don't know about models, but have built 2 dozen tools for scientists
14:25:25 <PhilBourne> Learnt about the importance of models
14:25:48 <susan> domain scientist should be on a team of builders?  usually the other way around
14:26:04 <susan> Liz
14:26:09 <susan> 17, 37, 46
14:26:20 <susan> command line is terrible interface
14:26:26 <PhilBourne> Command line is a worse interface design than I ever thought
14:26:43 <susan> Huan
14:26:56 <susan> so many definitions of discovery
14:26:58 <PhilBourne> Huan: so many definitions of discovery
14:27:00 <LarryHunter> Cecelia says in her world, scientists say, Hire a computer scientist instead a graduate student?  Why would anyone ever do that?  Biology will eventually become that way, but it isn't yet.
14:27:04 <PhilBourne> and ways to discover
14:27:13 <susan> so much interesting social computing, expectation of social computing
14:27:17 <PhilBourne> so much expectation of social computing
14:27:31 <PhilBourne> finding global findings from local knowledge
14:27:38 <susan> social computing may be able to help discover global knowledge from local knowledge in a tangible way (learned from neighbor)
14:27:49 <susan> More people are interested in this area than he thought
14:27:52 <Yolanda> social computing: a way to find global knowledge
14:28:01 <LizBradley_> What I'll do differently: rather than just go work with scientists, I'll think hard about it first: read some papers, do some planning.
14:28:07 <susan> socialogy of science, ML, social computing
14:28:20 <PhilBourne> David: concrete ways to work together was evident
14:28:29 <susan> lack of whole scientific areas using primitive tools for discovery informatics
14:28:49 <LizBradley_> Did not know that so many people cared so much about the different definitions of "discovery," "models," and so on.
14:29:03 <PhilBourne> Surprised that there are tools for specific examples but not more generic
14:29:11 <susan> equivalence classes that may not line up with scientific fields that could be useful for generalization
14:29:18 <CeciliaAragon> I was pleased by the general understanding of the importance of user-centered design in this community.
14:29:21 <susan> Andrey
14:29:41 <susan> surprise on focus on success stories, could learn from the failures
14:29:48 <susan> surprised at how different we are
14:30:01 <PhilBourne> Andrey: surprised how different we are and how we think about research differently
14:30:03 <susan> how one discipline thinks about another one
14:30:32 <susan> Hod
14:30:33 <PhilBourne> How group dynamics effects what we come up with - would it be different if done remotely?
14:30:55 <PhilBourne> Whole topic is a meta scientific problem
14:31:08 <susan> this is a meta scientific problem, surprised that there haven't been more of these meeting.  Lots of basic things still aren't nailed down.
14:31:31 <susan> Leverage to be gained if automate, make this process more efficient.  Should be high priority
14:31:32 <PhilBourne> Hod: so much leverage if we can automate and make processes more efficient
14:31:48 <susan> How non-linear the scientific process is.
14:31:57 <PhilBourne> Hod - teh scientif cprocess is soooo non-linear
14:32:00 <CeciliaAragon> Having built a couple dozen systems for scientific collaborations without knowing much about the importance of models, I was excited to learn there's so much work in this area, and am looking forward to using models in my own research.
14:32:21 <PhilBourne> The scientific process is stocastic
14:32:40 <susan> process of discovery is not rational
14:32:50 <PhilBourne> The discovery process is open ended and exploratory
14:33:34 <susan> garage science, democratization of discovery:  NSF can get in the near future by letting people in garages do research with much smaller budget
14:33:48 <PhilBourne> The democratization of discovery - people on an island arguing that the tsunami is coming
14:33:55 <helenadeus> Hod: we are like people on an island discussing whether a tsunami is coming
14:34:41 <PhilBourne> The research enterprise is going to change
14:34:45 <susan> problem 42
14:35:04 <CeciliaAragon> It was interesting to see that people kept saying, "a domain scientist should be on a CS project team." Where I come from, scientists generally need to be persuaded that a computer scientist should be on the team to build the code instead of a grad student.
14:35:11 <PhilBourne> Finding the interesting non-trivial thing in teh data - 42
14:35:20 <susan> finding the interesting non-trivial thing in the data
14:35:32 <susan> Google search is an extension of how people think
14:35:39 <PhilBourne> Google search is an extension of how people think - 
14:36:16 <susan> tools are not used because they do not add value by producing something new
14:36:24 <PhilBourne> The tools dont add value - they do not allow us to discover something new
14:36:32 <susan> distinction between discovery and informatics is really important, should focus on discovery
14:36:53 <susan> shouldn't make a TurboTax for science, companies can do this much better
14:37:03 <LarryHunter> +1 for the a successful tool will be one that when a scientists enters her data into the tool and it comes back with something interesting to the scientist in the top one or two results.  It's a waste of time to make TurboTax for scientist.
14:37:06 <PhilBourne> trying to compete with industry is a mistake - companiew will do a better TurboScience tool
14:37:27 <LarryHunter> Actually +10 for Hud's point about returning something interesting from the data
14:37:35 <CeciliaAragon> I really disagree that a company can make tools for scientists better - there's not a big enough market for them to want to do this.
14:37:37 <susan> Kerstin
14:38:05 <susan> discovery of new knowledge in what we have, producing somethign as easy to use as Google but more powerful
14:38:18 <PhilBourne> Kerstin: As useful as Google but across data and other scientific matter
14:38:35 <susan> surprised to see so much focused on the long-tail of science
14:38:59 <PhilBourne> So much focus on long tail surprising - used to teh other way around focus on big data
14:39:01 <susan> maybe due to composition of workshop
14:39:13 <CeciliaAragon> It's a mistake to assume that the private sector does everything better and more efficiently than anyone else - that's only true if there's a reasonable chance of profit. For non-profit areas, or ones that have only long-term returns, the public sector does better.
14:39:25 <susan> problems getting access to real scientists
14:39:29 <PhilBourne> Problems in getting access to real scientists a problem
14:39:52 <susan> Cecelia has to fend them off, is there a way of her sharing users and us sharing technologies?
14:39:53 <PhilBourne> How can views and technologies be better shared?
14:40:01 <susan> Miriah
14:40:01 <helenadeus> +1 to cecilia's comment 
14:40:05 <PhilBourne> A marketplace for informatics
14:40:24 <susan> visualization is young but growing, nice to hear its importance in discovery informatics
14:40:34 <PhilBourne> Pleased taht viz research bought to teh front
14:40:40 <helenadeus> it's out job to figure out the prototypes that are useful,only then will industry take it on (and we should let them)
14:40:45 <susan> usability, user-centered design
14:41:01 <LarryHunter> -1 to Kerstein for saying that biologists are focused on small science.  Biology has been increasingly focused on larger and more expensive and "grander" challenges.  The Democratization ("garage") biology is a complement to big biology (not a replacement) as extremely powerful tools become cheap.  Note that decades after the introduction of the cheap computer, expensive computer science research has not gone away.
14:41:09 <PhilBourne> knowledge representation resonates
14:41:10 <susan> lots of people, lots of different aspects:  knowledge representation
14:41:16 <susan> Haym
14:41:24 <susan> 53: galaxy zoom, fold-it
14:41:36 <PhilBourne> Haym - GalaxyZoo and Foldit - we got it straight away
14:41:58 <PhilBourne> excited about teh thinking of data and models
14:42:04 <susan> interplay between data and models, process: routine thing in DM, pleased to see this here
14:42:21 <PhilBourne> Pleased about the long tail
14:42:24 <susan> long-tail is important... complex data is also interesting
14:42:40 <susan> dark science
14:42:54 <PhilBourne> Pleased about the focus on people
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14:42:59 <susan> people: user-centric design, citixzen science, human bottleneck 
14:43:27 <susan> will
14:43:34 <PhilBourne> Will: oh
14:43:56 <CeciliaAragon> The problem with saying "human bottleneck" is that it goes back to the old model of thinking that humans are the limiting factor or at fault... I prefer the term "impedance mismatch" between human cognition and computation.
14:44:05 <susan> multiple iterations of cartoonmodel of science
14:44:22 <susan> prefer the quantum version
14:44:24 <PhilBourne> Will: multiple iteration of the scientific process - prefers quantum version
14:44:56 <PhilBourne> Will: many aspects of science do not work with this model
14:45:08 <susan> lot of people are interested in incentivizing use of science/tools
14:45:26 <susan> have to build in from the front
14:45:39 <PhilBourne> Will: incentivize use of science and tools - have to build in from teh front - user must be in at the beginning
14:45:46 <LarryHunter> +1 to Cecilia's comment about disagreeing with the "human bottleneck" idea.  *All* interesting science comes out of people, and probably will for some time.
14:45:54 <PhilBourne> Think about what the incentives might be
14:46:10 <PhilBourne> What is important to teh scientist must come first
14:46:41 <susan> didn't see a lot of talk about social/political forces in science
14:46:45 <PhilBourne> Not much talk about the social and political aspects of science
14:47:03 <susan> break down political and social barriers?
14:47:03 <PhilBourne> breakdown teh political barriers not discussed much
14:47:27 <PhilBourne> eg problems of peer review - use technology to change teh structure
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14:49:10 <Karsten> yolanda & haym -- saw the whole irc stream but had trouble posting, will e-mail notes.
14:55:34 <DavidJensen> Charge to the breakout groups is to identify exciting research that would be enabled by DI.  However, we should focus on things that DI would uniquely enable.  What will *uniquely* happen with DI?
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15:06:23 <helenadeus> Daviv - categories to be discussed in the breakouts: efficiency, cover, participation, education and basic knowledge about the scientific process
15:06:32 <helenadeus> s/Daviv/David/
15:08:03 <DavidJensen> To expand...
15:08:14 <DavidJensen> Efficiency — More efficient discovery of what is currently discovered
15:08:20 <DavidJensen> Coverage — Extend what is discoverable beyond what can be discovered currently.
15:08:30 <DavidJensen> Participation — Increase the number of people who can participate in science because of broad availability of tools, data, and collaboration opportunities
15:08:38 <DavidJensen> Education — Increase the availability of actual scientific processes and information to students
15:08:44 <DavidJensen> Knowledge — Increase understanding about the process of scientific discovery because of data collection and basic research on the process itself
15:09:12 <helenadeus> if we want to make an investment in science, we have to understand how we can change it
15:10:29 <Karsten> .com
15:12:41 <Karsten> Karsten has left #diw2012
15:14:47 <PaoloCiccarese> rrsagent, [please] create [the] minutes
15:14:47 <RRSAgent> I'm logging. I don't understand '[please] create [the] minutes', PaoloCiccarese.  Try /msg RRSAgent help
15:14:57 <PaoloCiccarese> rrsagent, create minutes
15:14:57 <RRSAgent> I have made the request to generate http://www.w3.org/2012/02/03-diw2012-minutes.html PaoloCiccarese
15:15:48 <PaoloCiccarese> rrsagent, set logs world-visible