Types of resource

basic resources => data types => PNG images, Excel tables,
create resources => web based collaborative tools => google docs, google tables,
share resources => sharing platforms => dropbox, others
discover resources => good search tools =>
publish resources => curation, linking, discovery => dryad, dataverse

“Actions of a researcher”:

1. Plan and Discover

2. Generate ideas

3. Collect Data (observe and generate data)

4. Analyze

5. Disseminate and viz

6. Impact

Fig 1: The top 50 word wordle.com cloud of terms used from this document. Note that “google”, “data”, “tools”, “discovery” and “search” are key features

Fig 2: A detailed wordle.com word cloud of terms used from the tools section of this document.

1. Planning and Discovery

●Meet funder requirements for data management

○California Digital Library/ UC Curation Center (CDL/UC3) Data Management

Planning Tool (based on DCC tool) https://dmp.cdlib.org/

○DCC: https://dmponline.dcc.ac.uk/

●Analyze state of the art research

○Literature search, coupled with notification services.  General scholarly search





○Discipline-specific engines:

■Physics: http://arxiv.org

■Astronomy: http://ads.harvard.edu

■Medicine: http://www.ncbi.nlm.nih.gov/pubmed/

■Biomed experts: http://www.biomedexperts.com/Portal.aspx

■Google Alerts: http://www.google.com/alerts

■PubCrawler: http://pubcrawler.gen.tcd.ie/

○Explore: Wolfram alpha - search over curated data: http://wolframalpha.com

○Data discovery: find related datasets/studies [GAP: no good ways to search for

data across disciplines, hard even within a particular domain]; some

discipline-specific examples:

●Library repository http://www.bids.ac.uk/

○General data repositories, eg, http://dataone.org/

○Domain specific databases, eg, http://www.pdb.org/

●Obtain persistent identifiers

○services, eg, http://n2t.net/ezid, http://handle.net/

○identifiers, eg, DOIs, HTTP URIs

●After data is generated, archive it and generate citations and expose them to appropriate

abstracting and indexing services (eg, Web of Knowledge http://wokinfo.com/)

○DataCite http://datacite.org/

○Dryad http://datadryad.org

○Dataverse: http://thedata.org, http://dvn.iq.harvard.edu (social science data),

http://dvn.theastrodata.org (astronomy data)


○MyExperiment: http://www.myexperiment.org

2. Generate Ideas

●Google Docs, Word, excel, latex,

●Wikis (http://wikispaces.com)

●mind map and concept map software

○mindmeister: http://www.mindmeister.com/

○CMapTools http://cmap.ihmc.us/

○Personal Brain: http://www.thebrain.com/

●Evernote, data sharing - “cloud storage surfaces” : http://www.evernote.com/

●Blogs (http://wordpress.com), Twitter (http://twitter.com), Disqus (http://disqus.com)

●WorldMap: http://worldmap.harvard.edu/

●Skype, WebEx, Adobe Connect

3. Collect Data

●Google spreadsheets: http://docs.google.com

●Microsoft Excel

●Relational and non relational databases

○mysql, oracle, postgresql BDB, CouchDB, NoSQL


●Future: Excel DataScope: http://research.microsoft.com/en-us/projects/exceldatascope/

●Google Forms

●GIS, geo tagging http://en.wikipedia.org/wiki/Geotagging

●Sensor Streaming Software


●Storing data and meta data:



4. Analyze

●Reviews of these tools:



●Data Wrangler: http://vis.stanford.edu/wrangler/app/

●Google Fusion Tables: http://www.google.com/fusiontables/Home/

●Google Refine: http://code.google.com/p/google-refine/

●R, Splus http://www.math.montana.edu/Rweb/

●Hadoop, Map/Reduce: http://hadoop.apache.org/

●AWS: http://aws.amazon.com/

●Traditional perl, python, ruby, sed, awk, grep, (unix tools)


●Lucene: http://lucene.apache.org/java/docs/index.html

●Matlab: http://www.mathworks.com/products/matlab/index.html

●Mathematica: http://www.wolfram.com/mathematica/

●Wolfram/Alpha: http://www.wolframalpha.com/






○Tableau: http://www.tableausoftware.com/

○Fusion Tables


○Many Eyes: http://www-958.ibm.com/software/data/cognos/manyeyes/

5. Disseminate and Viz.

See Generate above


○See Analysis above

●Mendeley http://mendeley.com

●Google Docs

●Wikis (http://wikispaces.com), Blogs (http://wordpress.com)


twitter.com / blogger.com / tumblr.com / posterous.com / google.com / wordpress.com

●Google visualization API

●Open Layers


●BioCatalogue (web-services), Dryad (data), Dataverse (data), Google Code /

SourceForge / GitHub / Bitbucket (software)

6. Impact

total-impact.org / klout / ranking / f1000 /

●H and G numbers eigenfactor.org  and readermeter.org


Key Unsolved Problems

●Universal scientific search

○“the email problem” - conversations over email are part of science, how to


○“the file transfer problem” - institute firewalls, “dropbox.com” freemium service

○“the file format problem” (video, documents, binary blobs)

○“the library subscription problem” (open access)

○converting audio to text, multimedia indexing and searching

●Lack of integration and seamlessness. Long list of tools that don’t interconnect.

●Not enough inter-disciplinary tools

●Making sense of thousands of papers, sites, etc. Processing vast amount of information

(without having to read them all). Some text mining tools that are OK, but lots to develop

in this area. Eg, Summarizing tools, aggregate tools, zoom in/zoom out, intelligent

filtering, recommendation engines, http://www.nactem.ac.uk

●How are we going to teach all the tools, resources. The advocacy problem,

Possible answers to key unsolved problems

ifttt.com  (concept, logic model) - we need a scientific version of this to trigger integration

●Searchable Registry for scientific, scholarly tools and resources (across domains)

Rough Transcript of my Opening Remarks

Dear Colleagues:

 It is an honor to be asked to address this group, some of whom I seem to see more than my own family, to set the stage for this 2011 Microsoft Research sponsored eScience workshop on Transforming Scholarly Communication.

 The first fundamental question to ask is, do we need a transformation in the first place? Obviously we all believe we do otherwise we would not be here, but what about the majority of scholars? I use my colleagues up and down the corridor as a benchmark to answer that question. A group currently oblivious to much of what we will show tomorrow. But nevertheless a group increasingly not oblivious to the changes going on around them – data sharing policies, cuts in library budgets, open access, and our students. Let me illustrate this latter point with a recent example of something remarkable that happened to me.

 A couple of months ago I received by email a paper to PLoS Comp Biol. This happens from time to time as authors try and circumvent the standard submission procedure and contact me as Editor in Chief directly. It was a paper in pandemic modeling, which appeared to question conventional approaches to such modeling. Not being an expert here I sent the manuscript to Simon Levin in Princeton who is on our Editorial Board for his opinion. Simon is a Kyoto Prize winner and an expert in large-scale biological modeling. He indicated there was something special about this well written paper. Since the sole author was living in San Diego I agreed to meet with her and discuss the work. Simply by asking she had received a large amount of free computer time from the San Diego Supercomputer Center (SDSC), got free access to Mathamatica and had clearly benefited from the open access literature as well as resources like Wikipedia. I encouraged her to submit the work to Science, which she has done, and it is currently under review.


The sole author’s name is Meredith.  What makes this story remarkable, is that she is 15 years old and a senior at La Jolla High School in San Diego. She subsequently presented her work at my lab meeting, which I must say was much better attended than usual. Sitting there with my eyes closed I thought I was in the presence of a professor deep into their area of expertise. It was only when I opened my eyes and saw the braces did reality sink in.

Clearly this is an extreme case, but lets not be modest, what we are trying to do here is enable anyone with an Internet connection and a will to learn, achieve what Meredith has achieved. I cannot think of a more noble cause. While we have seen this possibility for a long time, what is new is that others are now seeing it too.

We all have our own Meredith stories or at the very least some driver that moves us in the same way. For some it is the glacial pace at which knowledge exchange takes place; for others it is the sense of unease about the lack of reproducibility in our own science; for others it is the inaccessibility of knowledge; and for others still it is the totally qualitative way quantitative scientists measure the value of scholarship.

With Meredith as our motivation, let us take a minute to analyze the path we are on towards transforming scholarship through what has happened this past year and then what might happen as a result of this workshop.

2011 may well be remembered as the year that stakeholders – scientists, publishers, archivists and librarians, developers, funders, and decision makers went from working in isolation to beginning to work together. What started with Beyond the PDF in January had become a “movement” by the time the summer meetings were over. The Dagstuhl meeting captured the spirit in a manifesto that should become a living document for us all to consider. Movements have transformed entrenched systems before and it remains an open and very exciting question as to whether that will happen here. For a small group to cause change to many requires that the many believe that change is needed and gradually get on board. I believe that time has come.

The driver of change is the ground swell towards open science. When I first heard that a group of prominent life scientists got together and agreed to start a new open access journal I was disappointed – such vision coming up with something that we had already. But if the effort by HHMI, Wellcome Trust and Max Plank does indeed compete with Science and Nature it will precipitate change. My sense is that Publishers see the writing on the wall, or more appropriately the screen and the smart ones are gearing up to a future with different business models. The winning publishers will move from serving science through scientific process and dissemination to doing that plus enabling knowledge discovery, more equitable reward systems and improving comprehension by a broader audience. Interestingly, it is not clear to me, based on my interactions with OAPSA, that open access publishers see it that way. Many simply see delivering papers as before, but with a different revenue model. Ironically even if they see the promise of change, they do not have the resources to make it happen. We must help them and that is why meetings like this one are so important. A serious example of what we must fix is the lack of consistent representation of their papers in XML. PubMed Central will come back to haunt us when developers begin to seriously try and use the content. This is history repeating itself – look at the biological databases. We should learn from history.

Open science is more that changing how we interface with the final product it is interfacing with the complete scientific process – motivation, ideas, hypotheses, experiments to test the hypotheses, data generated, analysis of that data, conclusions and  awareness. This is a tall order and I believe we need to proceed in steps. Clearly access and effective use of data is a valuable next step. Funders are demanding it, scientists (to some degree) are providing it and repositories exist to accept it. But right now it is a mess, but we have an opportunity. Ontologies exist, some tools exist and so we have the opportunity NOT to repeat the horrible loss of productivity we see in the publishing world of rehashing the same material for different publishers. Let us define and implement data standards and input mechanisms that capture the generic metadata, provide the hooks for more domain specific deeper content and allow a more universal deposition and search. We need to do this now before systems become entrenched. Otherwise Google, Bing and the like will be our tools for data discovery – we need deep and meaningful search of data.

Let me conclude with a couple of thoughts on what I believe should come from the workshop. 

1.     We will hear about some wonderful tools and innovative software developments to support scholarly communication – we must define a way to aggregate these efforts to facilitate uptake by others around a focused and shared development effort.

2.     We need to define ways to recruit to the movement – it will take more than tools to do so – are there clear wins for all concerned? If so what are they? Platforms to disseminate scholarship, new reward systems, knowledge discovery from open access content, proven improved comprehension.

What can we do so that more 15 year olds are active contributors to scholarship? This is our challenge. Thank you very much.







Bios of the Organizers

Bio sketches of the meeting organizers, who will float from group to group:

  1. Alyssa Goodman, Harvard University
  2. Alberto Pepe, Harvard University
  3. Mary Lee Kennedy, Harvard University
  4. Malgorzata (Gosia) Stergios, Harvard University
  5. Lee Dirks, Microsoft Research
  6. Alex Wade, Microsoft Research
  7.  Joshua M. Greenberg, Alfred P. Sloan Foundation
  8. Chris Mentzel, Gordon & Betty Moore Foundation

Bios of attendees in #resources theme

  1. Alberto Accomazzi, Harvard-Smithsonian Center for Astrophysics (ADS)
  2. Phil Bourne , UCSD
  3. Mercé Crosas, Harvard, IQSS
  4. James Cuff, Research computing at Harvard FAS
  5. Stephen Friend, SAGE Bionetworks
  6. Paul Ginsparg, Cornell University
  7. John Kunze, CDL / UC3
  8. Hilmar Lapp,  Informatics at NESCent
  9. Liz Lyon, UKOLN
  10. Chris Mentzel, Moore Foundation
  11. August Muench, Smithsonian Astrophysical Observatory
  12. Ashfaq Munshi, Terabitz 

Demonstrations on RESOURCES on Meeting Day 1

RESOURCES Seamless technologies for literature and data (literature/data search engines; cloud-based, group sharing, adjustable permissions, integration with search)
Facilitator: Phil Bourne, Professor of Pharmacology, University of California, San Diego

  • Alberto Accomazzi, Project Manager, NASA Astrophysics Data System at Harvard-Smithsonian Center for Astrophysics (Demo of ADS and ADS Labs)
  • Mercé Crosas, Director of Product Development, IQSS at Harvard University (Demo of Dataverse)
  • Ashfaq Munshi, Founder and CEO, Terabitz (Demo of Terabitz)