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Lisa Johnston

Open Science Data Initiative (OSDI) - 0 views

ornlogdi.cloudapp.net/Default.aspx

shared by Lisa Johnston on 29 Apr 10 - Cached
  • Lisa Johnston on 29 Apr 10
     
    he Open Science Data Initiative is an initiative led by Oak Ridge National Laboratory in partnership with Microsoft's Public Sector Developer Evangelism team. OSDI is based on OGDI which in turn uses the Azure Services Platform to make it easier to publish and use a wide variety of scientific data from government agencies. OSDI is an sample of OGDI's open source 'starter kit' (coming soon) with code that can be used to publish data on the Internet in a Web-friendly format with easy-to-use, open API's. OSDI-based web API's can be accessed from a variety of client technologies such as Silverlight, Flash, JavaScript, PHP, Python, Ruby, mapping web sites, etc. Whether you are a researcher wishing to use scientific data, a hobyist developer, or a "budding scientist", these open API's will enable you to build innovative applications, visualizations and mash-ups that empower people through access to scientific information. This site is built using the OGDI starter kit software assets and provides interactive access to some publicly-available data sets along with sample code and resources for writing applications using the OSDI APIs.
Amy West

Developing the Capability and Skills to Support eResearch - 0 views

www.ariadne.ac.uk/henty

eresearch escience ariadne

shared by Amy West on 07 Nov 08 - Cached
  • Of particular concern to this article is the need for improved levels of data stewardship to enable good data management for long-term sustainability, both at national and institutional levels.
  • researchers, particularly those engaged in data-intensive research; systems developers, data scientists and other technical staff; data managers of institutional repositories, data archives and discipline-based data centres and their support staff, with those who liaise between depositors and the repository as being of particular interest; and those who are engaged in high-level policy formulation, either in government or research institutions.
  • Interviews were conducted with twelve key established researchers in six Australian institutions, with a focus on academics engaged in data-intensive research. Interviews were conducted also with the manager of a large data centre, and a repository administrator. The institutions concerned were the Australian National University, the University of Melbourne, the University of Tasmania, the University of Queensland, the University of Sydney and one area of the Commonwealth Scientific and Industrial Research Organisation (CSIRO).
  • ...18 more annotations...
  • There was wide agreement that there are three types of skills required for practitioners of eResearch, their support staff and repository staff. Not surprisingly, there was a strong need for technical skills. Perhaps not as obvious was the identification of a wide range of non-technical skills. Less obvious again was mention of an assortment of personal qualities, which, while not skills in the formal sense of the term, were singled out as being important.
  • The surveys indicated that not everyone needs the same level of technical skills to conduct or support eResearch.
  • So you need a basic literacy level to look after your computers where you’re storing your data, and then in order to access, like a remote repository, you need to know something about how to connect to that remote repository, what the format of the data should be to go in it, how to convert your data to that required format
  • included skills related to high-performance computing (HPC) and the access grid, data (and database) management, data curation, information engineering, information modelling, software development, remote communications, distributed processing, informatics, portal design, computational fluid dynamics, database integration, visualisation and programming of all kinds.
  • Some of these skills are tightly connected to specific disciplines, especially informatics.
  • The need for technical skills is allied to the ability to understand end-to-end workflows, especially for repository managers and developers who need to be able to think like the researcher and to apply that understanding to developing the repository. By workflows, I mean the many software applications, processing operations and interactions required for research tasks to be carried through to completion.
  • The group of librarians at ‘The Researcher Librarian Nexus’ workshop identified a need for further development of their technical skills, mentioning in particular metadata, something which did not feature among any of the other responses, other than by implication.
  • These vary from skills in data analysis (including the use of statistical packages and other techniques such as data mining) through information seeking to a broader range of general skills. Project management, business analysis, communications, negotiation, intellectual property, team building and train the trainer were mentioned specifically. Another was generic problem solving, because, as one researcher aptly put it, the kinds of problems which arise when undertaking eResearch mean that ‘There’s never going to be someone who has done it before.’
  • The librarians involved with the Researcher/Librarian Nexus workshop also identified it as being of high priority for repository managers, along with marketing, advocacy, copyright, metadata, educational outreach and grant submission writing. They also singled out the intriguing skill of ‘researcher management’ while not specifying precisely what this might entail.
  • A good grasp of copyright and intellectual property issues was seen as essential,
  • These were listed as: open-mindedness, patience and an ‘ability to cooperate and collaborate rather than compete’
  • For example, one researcher, in the field of finance told me of his need for programmers who have a high level of expertise in economics, econometrics, statistics, maths and programming; ‘otherwise all the programming expertise doesn’t really help because then they make strange assumptions in their coding that just result in nonsense output.’
  • One solution to the need to bridge the disciplinary gap is to use graduate students to help with the technical aspects, where those students have an interest and aptitude for this kind of work. In some cases this might be done by providing scholarships, the students then graduating with a PhD on the basis their contribution to the research project has been of sufficient originality to warrant the degree.
  • The barrier to research most often mentioned was the difficulty in assembling all the skills required to conduct a project, particularly in relation to data management and stewardship. In some cases the gap is organisational, as happens for example when the researcher is either unaware of or unable to tap into the skills of a central IT unit. More often the gap was in a lack of understanding of what each group needs, what each has to offer and where responsibilities lie. Examples of this can be seen in comments like the following:
  • For instance if you’ve got data in say NetCDF file formats and the repository wants it in TIFF format, well you need to know something about the technicality of getting your data from NetCDF format into TIFF format.
  • The humanities and social sciences are notable areas where the take-up rate of eResearch has been slower than, for example, in the hard sciences, and where there have been calls for exemplars to be publicised. Many practitioners in the humanities and social sciences find it difficult to envisage where their work might fit into the concept of eResearch.
  • Few researchers are aware that there are such things as repositories, so it is important that the repository is seen as (and indeed is) ‘a good repository – that it’s good in the sense of its high quality but also good in that it adds value for [the researcher].’
  • If research institutions are to minimise the gap between the ideals and realities of eResearch, there is some way to go in providing both institutional capacity and appropriately qualified individuals. While eResearch is dependent on good ICT infrastructure, this is not sufficient in itself. The results of the survey outlined here show that capacity in information technology skills is important but must be accompanied by a range of non-technical skills in such areas as project management. Equally important is the creation of research environments which are covered by well-propagated and understood policies, which are appropriately organised into structures with clearly delineated roles and responsibilities and which minimise the current barriers experienced by many researchers.
Amy West

Open access to research data a lot tougher than you think - 2 views

arstechnica.com/...lot-tougher-than-you-think.ars

open access data publications preservation

shared by Amy West on 29 Aug 11 - No Cached
  • It means that researchers need to deal with the formatting and deposition of data, an annoying step when they would rather be focusing on their next project. Given the time lag, it's also difficult to associate the correct metadata with the material that's being a
  • According to the commentary, scientists view data deposition as a burden due to the extra work it involves. Research data is usually not in the correct format for submission to repositories when the project is completed, and so the scientist must take the time to convert it.
  • The authors here propose a new approach to data management, where each research institution should employ data managers to work with scientists and administer local, structured data storage. Local storage and support is the preference of most scientists, who would rather not hand off control of their data to remote strangers.
Lisa Johnston

Open science at web-scale: Optimising participation and predictive potential : JISC - 0 views

www.jisc.ac.uk/...opensciencerpt.aspx

shared by Lisa Johnston on 12 Jan 10 - Cached
  • Lisa Johnston on 12 Jan 10
     
    This report has attempted to draw together and synthesise evidence and opinion associated with data-intensive open science from a wide range of sources. The potential impact of data-intensive open science on research practice and research outcomes, is both substantive and far-reaching. There are implications for funding organisations, for research and information communities and for higher education institutions.
Amy West

PLoS Computational Biology: Defrosting the Digital Library: Bibliographic Tools for the... - 0 views

www.ploscompbiol.org/...10.1371%2Fjournal.pcbi.1000204

shared by Amy West on 12 Nov 08 - Cached
  • Presently, the number of abstracts considerably exceeds the number of full-text papers,
  • full papers that are available electronically are likely to be much more widely read and cited
  • Since all of these libraries are available on the Web, increasing numbers of tools for managing digital libraries are also Web-based. They rely on Uniform Resource Identifiers (URIs [25] or “links”) to identify, name, and locate resources such as publications and their authors.
  • ...27 more annotations...
  • We often take URIs for granted, but these humble strings are fundamental to the way the Web works [58] and how libraries can exploit it, so they are a crucial part of the cyberinfrastructure [59] required for e-science on the Web.
  • link to data (the full-text of a given article),
  • To begin with, a user selects a paper, which will have come proximately from one of four sources: 1) searching some digital library, “SEARCH” in Figure 4; 2) browsing some digital library (“BROWSE”); 3) a personal recommendation, word-of-mouth from colleague, etc., (“RECOMMEND”); 4) referred to by reading another paper, and thus cited in its reference list (“READ”)
  • There is no universal method to retrieve a given paper, because there is no single way of identifying publications across all digital libraries on the Web
  • Publication metadata often gets “divorced” from the data it is about, and this forces users to manage each independently, a cumbersome and error-prone process.
  • There is no single way of representing metadata, and without adherence to common standards (which largely already exist, but in a plurality) there never will be.
  • Where DOIs exist, they are supposed to be the definitive URI. This kind of automated disambiguation, of publications and authors, is a common requirement for building better digital libraries
  • Publication metadata are essential for machines and humans in many tasks, not just the disambiguation described above. Despite their importance, metadata can be frustratingly difficult to obtain.
  • So, given an arbitrary URI, there are only two guaranteed options for getting any metadata associated with it. Using http [135], it is possible to for a human (or machine) to do the following.
  • This technique works, but is not particularly robust or scalable because every time the style of a particular Web site changes, the screen-scraper will probably break as well
  • This returns metadata only, not the whole resource. These metadata will not include the author, journal, title, date, etc., of
  • As it stands, it is not possible to perform mundane and seemingly simple tasks such as, “get me all publications that fulfill some criteria and for which I have licensed access as PDF” to save locally, or “get me a specific publication and all those it immediately references”.
  • Having all these different metadata standards would not be a problem if they could easily be converted to and from each other, a process known as “round-tripping”.
  • many of these mappings are non-trivial, e.g., XML to RDF and back again
  • more complex metadata such as the inbound and outbound citations, related articles, and “supplementary” information.
  • Personalization allows users to say this is my library, the sources I am interested in, my collection of references, as well as literature I have authored or co-authored. Socialization allows users to share their personal collections and see who else is reading the same publications, including added information such as related papers with the same keyword (or “tag”) and what notes other people have written about a given publication.
  • CiteULike normalizes bookmarks before adding them to its database, which means it calculates whether each URI bookmarked identifies an identical publication added by another user, with an equivalent URI. This is important for social tagging applications, because part of their value is the ability to see how many people (and who) have bookmarked a given publication. CiteULike also captures another important bibliometric, viz how many users have potentially read a publication, not just cited it.
  • Connotea uses MD5 hashes [157] to store URIs that users bookmark, and normalizes them after adding them to its database, rather than before.
  • he source code for Connotea [159] is available, and there is an API that allows software engineers to build extra functionality around Connnotea, for example the Entity Describer [160].
  • Personalization and socialization of information will increasingly blur the distinction between databases and journals [175], and this is especially true in computational biology where contributions are particularly of a digital nature.
  • This is usually because they are either too “small” or too “big” to fit into journals.
  • As we move in biology from a focus on hypothesis-driven to data-driven science [1],[181],[182], it is increasingly recognized that databases, software models, and instrumentation are the scientific output, rather than the conventional and more discursive descriptions of experiments and their results.
  • In the digital library, these size differences are becoming increasingly meaningless as data, information, and knowledge become more integrated, socialized, personalized, and accessible. Take Postgenomic [183], for example, which aggregates scientific blog posts from a wide variety of sources. These posts can contain commentary on peer-reviewed literature and links into primary database sources. Ultimately, this means that the boundaries between the different types of information and knowledge are continually blurring, and future tools seem likely to continue this trend.
  • he identity of people is a twofold problem because applications need to identify people as users in a system and as authors of publications.
  • Passing valuable data and metadata onto a third party requires that users trust the organization providing the service. For large publishers such as Nature Publishing Group, responsible for Connotea, this is not necessarily a problem.
  • business models may unilaterally change their data model, making the tools for accessing their data backwards incompatible, a common occurrence in bioinformatics.
  • Although the practice of sharing raw data immediately, as with Open Notebook Science [190], is gaining ground, many users are understandably cautious about sharing information online before peer-reviewed publication.
  • Amy West on 12 Nov 08
     
    Yes, but Alexandria was also a lot smaller; not totally persuaded by analogy here...
Lisa Johnston

Tim Berners-Lee: The year open data went worldwide | Video on TED.com - 0 views

www.ted.com/..._open_data_went_worldwide.html

shared by Lisa Johnston on 05 Apr 10 - Cached
  • Lisa Johnston on 05 Apr 10
     
    6-min Video of open data examples at TED conference
Lisa Johnston

Open Online Research Data Management Course for Ph.D Students - 2 views

digital-scholarship.org/...ement-course-for-ph-d-students

shared by Lisa Johnston on 20 Oct 11 - No Cached
  • Lisa Johnston on 20 Oct 11
     
    If anyone plans on running through the online course, please do update the data management website with any new information. The content closely follows the structure of our pages. thanks
Lisa Johnston

Nature 461 (2009): Data sharing: Empty archives - 0 views

www.nature.com/...461160a.html

sharing sata

shared by Lisa Johnston on 10 Sep 09 - No Cached
  • Lisa Johnston on 10 Sep 09
     
    new feature on open access to data and difficulties with sharing
David Govoni

Open Science Grid Home page - 0 views

www.opensciencegrid.org

science research e-science e-research grid grid-computing application modeling opensource simulation OSG cyberinfrastructure

shared by David Govoni on 10 Jan 09 - Cached
  • David Govoni on 10 Jan 09
     
    "A national, distributed computing grid for data-intensive research."
Amy West

Liveblog: BRDI: Author Deposit Mandates for Federal Research Grantees : Gavin Baker - 0 views

www.gavinbaker.com/...-for-federal-research-grantees

shared by Amy West on 02 Feb 09 - Cached
  • DC Principles Coalition: We believe in free access to science, within the constraints of our business models.
  • The public doesn’t need access to the full articles
  • The problem is that consumers want everything for free.
  • ...6 more annotations...
  • Repositories can do all the functions of journals except quality control, and we don’t want government doing that.
  • Social sciences often left out of discussions about data curation, open access, etc.
  • We could argue that taxpayers paid for the research in general, not necessarily each publication.
  • But the Public Access Policy requires the peer-reviewed manuscript, not the one after which the publishers add value. The America COMPETES model, for un-peer-reviewed grant proposals, is almost useless to the public. In health, you want the refereed results, not the grantee’s report to the agency.
  • If journals can’t survive, from an economic perspective, that’s not harm — it’s just a failure to adapt.
  • Journal growth trends with funding for researchers. As universities want to be more prestigious, they aim to publish more. Trying to have access to everything requires too much money — you have to prioritize.
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