CiteULike: hpiwowar's library [652 articles]

Mapping Open - Open Everything

2008-07-01T14:17:06-00:00

On the persistence of supplementary resources in biomedical publications.

NR Anderson — 2008-06-23T20:21:51-00:00

BMC bioinformatics, Vol. 7 (2006)

BACKGROUND: Providing for long-term and consistent public access to scientific data is a growing concern in biomedical research. One aspect of this problem can be demonstrated by evaluating the persistence of supplementary data associated with published biomedical papers. METHODS: We manually evaluated 655 supplementary data links extracted from PubMed abstracts published 1998-2005 (Method 1) as well as a further focused subset of 162 full-text manuscripts published within three representative high-impact biomedical journals between September and December 2004 (Method 2). RESULTS: For Method 1 we found that since 2001, only 71 - 92% of supplementary data were still accessible via the links provided, with 93% of these inaccessible links occurring where supplementary data was not stored with the publishing journal. Of the manuscripts evaluated in Method 2, we found that only 83% of these links were available approximately a year after publication, with 55% of these inaccessible links were at locations outside the journal of publication. CONCLUSION: We conclude that if supplemental data is required to support the publication, journals policies must take-on the responsibility to accept and store such data or require that it be maintained with a credible independent institution or under the terms of a strategic data storage plan specified by the authors. We further recommend that publishers provide automated systems to ensure that supplementary links remain persistent, and that granting bodies such as the NIH develop policies and funding mechanisms to maintain long-term persistent access to these data.

Collaboration uncovered: Exploring the adequacy of measuring university-industry collaboration through co-authorship and funding

Jonas Lundberg — 2008-06-01T21:09:26-00:00

Scientometrics, Vol. 69, No. 3. (23 December 2006), pp. 575-589.

Summary Analysing co-authored publications has become the standard way to measure research collaborations. At the same time bibliometric researchers have advised that co-authorship based indicators should be handled with care as a source of evidence on actual scientific collaboration. The aim of this study is to assess how well university-industry collaborations can be identified and described using co-authorship data. This is done through a comparison of co-authorship data with industrial funding to a medical university. In total 436 companies were identified through the two methods. Our results show that one third of the companies that have provided funding to the university had not co-authored any publications with the university. Further, the funding indicator identified only 16% of the companies that had co-authored publications. Thus, both co-authorship and funding indicators provide incomplete results. We also observe a case of conflicting trends between funding and co-authorship indicators. We conclude that uncritical use of the two indicators may lead to misinterpretation of the development of collaborations and thus provide incorrect data for decision-making.

Bibliometrics as a research assessment tool : impact beyond the impact factor

Jonas Lundberg — 2008-06-01T21:08:42-00:00

(2006)

Introduction: While bibliometrics is frequently used as a tool for research assessment and is thought of as objective, quantitative and unobtrusive, the usability of bibliometric indicators has seldom been properly assessed and their value therefore been questioned. The aim of this thesis is to explore and develop the utility of bibliometrics as a research assessment tool. This is done through four studies that address the validity of bibliometrics as a research assessment tool. The issues that are further investigated are related with field delineation (Study I), collaboration (II) and research performance (I/III/IV). Materials and methods: The thesis is primarily based on data from the citation indices (Cl) produced by Thomson Scientific. In the different studies bibliometric indicators are calculated based on 24 223 (I), 62 104 (II) 6 142 055 (III) and 596 (IV) publications. To assess the validity of bibliometric indicators calculated using the Cl, these are combined with data from PubMed (I) and compared with data from manual assessments (I), financial data (II) and a Swedish system for identification and early assessment of new methods in health care (IV). Three new indicators are developed based on theoretical reasoning (III). Results: Frequently used bibliometric methods do not allow valid assessment of the development of research areas (I) or collaboration between academia and industry (II). There are also flaws associated with the current state-of-the-art performance indicator (III). At the same time, there are bibliometric methods available that can be used for identifying research areas (I) and there are certain types of collaboration between academia and industry that accurately can be described using bibliometric indicators (II). New performance indicators, which assign equal weight to all publications and control for the skewed and differing distribution of citations over publications, have been developed (III). Publications related to health care technologies that are deemed as very promising by clinical experts receive high scores using these indicators (IV). Discussion: The results of this thesis show that uncritical assessments of research areas based on rudimentary article identification strategies or collaboration analyses based solely on coauthorship data may be misleading and thus provide incorrect information for decision-making. At the same time, a correct use of refined bibliometric indicators may provide valuable background information for decision makers.

Data Sharing and Secondary Use of Scientific Data: Experiences of Ecologists

Ann Zimmerman — 2008-06-01T21:06:20-00:00

Not by metadata alone: the use of diverse forms of knowledge to locate data for reuse

Ann Zimmerman — 2008-06-01T21:05:32-00:00

International Journal on Digital Libraries, Vol. 7, No. 1. (2 October 2007), pp. 5-16.

Abstract An important set of challenges for eScience initiatives and digital libraries concern the need to provide scientists with the ability to access data from multiple sources. This paper argues that an analysis of scientists‘ reuse of data prior to the advent of eScience can illuminate the requirements and design of digital libraries and cyberinfrastructure. As part of a larger study on data sharing and reuse, I investigated the processes by which ecologists locate data that were initially collected by others. Ecological data are unusually complex and present daunting problems of interpretation and analysis that must be considered in the design of cyberinfrastructure. The ecologists that I interviewed found ways to overcome many of these difficulties. One part of my results shows that ecologists use formal and informal knowledge that they have gained through disciplinary training and through their own data-gathering experiences to help them overcome hurdles related to finding, acquiring, and validating data collected by others. A second part of my findings reveals that ecologists rely on formal notions of scientific practice that emphasize objectivity to justify the methods they use to collect data for reuse. I discuss the implications of these findings for digital libraries and eScience initiatives.

Scientific Collaboration on the Internet

Gary Olson — 2008-06-01T21:04:33-00:00

Modern science is increasingly collaborative, as signaled by rising numbers of coauthored papers, papers with international coauthors, and multi-investigator grants. Historically, scientific collaborations were carried out by scientists in the same physical location--the Manhattan Project of the 1940s, for example, involved thousands of scientists gathered on a remote plateau in Los Alamos, New Mexico. Today, information and communication technologies allow cooperation among scientists from far-flung institutions and different disciplines. Scientific Collaboration on the Internet provides both broad and in-depth views of how new technology is enabling novel kinds of science and engineering collaboration. The book offers commentary from notable experts in the field along with case studies of large-scale collaborative projects, past and ongoing. The projects described range from the development of a national virtual observatory for astronomical research to a National Institutes of Health funding program for major multi-laboratory medical research; from the deployment of a cyberinfrastructure to connect experts in earthquake engineering to partnerships between developed and developing countries in AIDS research. The chapter authors speak frankly about the problems these projects encountered as well as the successes they achieved. The book strikes a useful balance between presenting the real stories of collaborations and developing a scientific approach to conceiving, designing, implementing, and evaluating such projects. It points to a future of scientific collaborations that build successfully on aspects from multiple disciplines.

New Knowledge from Old Data: The Role of Standards in the Sharing and Reuse of Ecological Data

Ann Zimmerman — 2008-06-01T21:02:17-00:00

Science Technology Human Values (5 February 2008), 0162243907306704.

This article analyzes the experiences of ecologists who used data they did not collect themselves. Specifically, The author examines the processes by which ecologists understand and assess the quality of the data they reuse, and investigates the role that standard methods of data collection play in these processes. Standardization is one means by which scientific knowledge is transported from local to public spheres. While standards can be helpful, the results show that knowledge of the local context is critical to ecologists' reuse of data. Yet, this information is often left behind as data move from the private to the public world. The knowledge that ecologists acquire through fieldwork enables them to recover the local details that are so critical to their comprehension of data collected by others. Social processes also play a role in ecologists' efforts to judge the quality of data they reuse. 10.1177/0162243907306704

Sharing digitized research-related information on the World Wide Web

Katherine Mccain — 2008-06-01T21:01:08-00:00

Journal of the American Society for Information Science, Vol. 51, No. 14. (26 October 2000), pp. 1321-1327.

Five-hundred twenty-seven full bibliographic records containing URLs were downloaded from SCISEARCH as part of an exploration of the extent of Web publication of electronic research-related information (E-RRI) in the sciences and classified as to resource type, subject area, and degree of intellectual property protection. Four hundred eighty-five records represented nonduplicate descriptions of data compilations (194), software (153), Websites (73), electronic documents (49), and digitized images (17). The greatest concentration of E-RRI was found in molecular biology (QP=123), general natural history and biology (QH=84), and medicine (R=74). Roughly two-thirds of the 410 accessible Webpages (67%) permitted totally free and unrestricted public access and use of the information; 11% requested citation of a related journal article as acknowledgment of use; the remainder stated conditions for use or relied on a statement of copyright as an indication of ownership. The World Wide Web appears to have become a significant channel for scientists to distribute databases, software, and other information related to their published research.

Science 2.0 -- Is Open Access Science the Future?: Scientific American

2008-06-01T20:22:39-00:00

Science 2.0: Great New Tool, or Great Risk?: Scientific American

2008-04-11T12:57:25-00:00

The New Science of Sharing

2008-06-01T20:21:05-00:00

Cancer Data? Sorry, Can’t Have It - New York Times

2008-06-01T20:20:30-00:00

Freeing the Dark Data of Failed Scientific Experiments

2008-06-01T20:19:43-00:00

URL decay in MEDLINE - a 4-year follow-up study

Jonathan Wren — 2008-04-16T08:15:26-00:00

Bioinformatics (15 April 2008), btn127.

Motivation: Internet-based electronic resources, as given by Uniform Resource Locators (URLs), are being increasingly used in scientific publications but are also becoming inaccessible in a time-dependant manner, a phenomenon documented across disciplines. Initial reports brought attention to the problem, spawning methods of effectively preserving URL content while some journals adopted policies regarding URL publication and begun storing supplementary information on journal websites. Thus, a re-examination of URL growth and decay in the literature is merited to see if the problem has grown or been mitigated by any of these changes Results: After the 2003 study, three follow-up studies were conducted in 2004, 2005 and 2007. Unfortunately, no significant change was found in the rate of URL decay among any of the studies. However, only 5% of URLs cited more than twice have decayed versus 20% of URLs cited once or twice. The most common types of lost content were computer programs (43%), followed by scholarly content (38%) and databases (19%). Compared to URLs still available, no lost content type was significantly over or under-represented. Searching for 30 of these websites using Google, 11 (37%) were found relocated to different URLs. Conclusions: URL decay continues unabated, but URLs published by organizations tend to be more stable. Repeated citation of URLs suggests calculation of an electronic impact factor (eIF) would be an objective, quantitative way to measure the impact of Internet-based resources on scientific research. Contact: Jonathan-Wren@OMRF.org 10.1093/bioinformatics/btn127

Open access and openly accessible: a study of scientific publications shared via the internet.

Jonathan D Wren — 2005-04-15T04:18:05-00:00

BMJ (12 April 2005)

OBJECTIVES: To determine how often reprints of scientific publications are shared online, whether journal readership level is a predictor, how the amount of file sharing changes with the age of the article, and to what degree open access publications are shared on non-journal websites. DESIGN: The internet was searched using an application programming interface to Google, a popular and freely available search engine. MAIN OUTCOME MEASURES: The proportion of reprints of journal articles published between 1994 and 2004 from within 13 subscription based and four open access journals that could be located online at non-journal websites. RESULTS: The probability that an article could be found online at a non-journal website correlated with the journal impact factor and the time since initial publication. Papers from higher impact journals and more recent articles were more likely to be located. On average, for the high impact journal articles published in 2003, over a third could be located at non-journal websites. Similar trends were observed for the delayed or full open access publications. CONCLUSIONS: Decentralised sharing of scientific reprints through the internet creates a degree of de facto open access that, though highly incomplete in its coverage, is none the less biased towards publications of higher popular demand.

MIAME, we have a problem.

Robert Shields — 2006-01-25T14:08:54-00:00

Trends Genet (24 December 2005)

Scientific Data Collections and Distributed Collective Practice

Cragin — 2006-09-15T17:36:42-00:00

Computer Supported Cooperative Work (CSCW), Vol. 15, No. 2-3. (June 2006), pp. 185-204.

Patents and Data-Sharing in Public Science

Rebecca Eisenberg — 2008-05-28T17:34:56-00:00

Social Science Research Network Working Paper Series

Accepted Paper Series

Sharing neuroimaging studies of human cognition.

JD Van Horn — 2005-09-05T13:52:59-00:00

Nat Neurosci, Vol. 7, No. 5. (May 2004), pp. 473-481.

After more than a decade of collecting large neuroimaging datasets, neuroscientists are now working to archive these studies in publicly accessible databases. In particular, the fMRI Data Center (fMRIDC), a high-performance computing center managed by computer and brain scientists, seeks to catalogue and openly disseminate the data from published fMRI studies to the community. This repository enables experimental validation and allows researchers to combine and examine patterns of brain activity beyond that of any single study. As with some biological databases, early scientific, technical and sociological concerns hindered initial acceptance of the fMRIDC. However, with the continued growth of this and other neuroscience archives, researchers are recognizing the potential of such resources for identifying new knowledge about cognitive and neural activity. Thus, the field of neuroimaging is following the lead of biology and chemistry, mining its accumulating body of knowledge and moving toward a 'discovery science' of brain function.

Successes and rewards in sharing digital reconstructions of neuronal morphology.

GA Ascoli — 2008-05-24T17:16:30-00:00

Neuroinformatics, Vol. 5, No. 3. (2007), pp. 154-160.

The computer-assisted three-dimensional reconstruction of neuronal morphology is becoming an increasingly popular technique to quantify the arborization patterns of dendrites and axons. The resulting digital files are suitable for comprehensive morphometric analyses as well as for building anatomically realistic compartmental models of membrane biophysics and neuronal electrophysiology. The digital tracings acquired in a lab for a specific purpose can be often re-used by a different research group to address a completely unrelated scientific question, if the original investigators are willing to share the data. Since reconstructing neuronal morphology is a labor-intensive process, data sharing and re-analysis is particularly advantageous for the neuroscience and biomedical communities. Here we present numerous cases of "success stories" in which digital reconstructions of neuronal morphology were shared and re-used, leading to additional, independent discoveries and publications, and thus amplifying the impact of the "source" study for which the data set was first collected. In particular, we overview four main applications of this kind of data: comparative morphometric analyses, statistical estimation of potential synaptic connectivity, morphologically accurate electrophysiological simulations, and computational models of neuronal shape and development.

Data Sharing for Computational Neuroscience.

Jeffrey Teeters — 2008-02-11T17:49:42-00:00

Neuroinformatics (8 February 2008)

Computational neuroscience is a subfield of neuroscience that develops models to integrate complex experimental data in order to understand brain function. To constrain and test computational models, researchers need access to a wide variety of experimental data. Much of those data are not readily accessible because neuroscientists fall into separate communities that study the brain at different levels and have not been motivated to provide data to researchers outside their community. To foster sharing of neuroscience data, a workshop was held in 2007, bringing together experimental and theoretical neuroscientists, computer scientists, legal experts and governmental observers. Computational neuroscience was recommended as an ideal field for focusing data sharing, and specific methods, strategies and policies were suggested for achieving it. A new funding area in the NSF/NIH Collaborative Research in Computational Neuroscience (CRCNS) program has been established to support data sharing, guided in part by the workshop recommendations. The new funding area is dedicated to the dissemination of high quality data sets with maximum scientific value for computational neuroscience. The first round of the CRCNS data sharing program supports the preparation of data sets which will be publicly available in 2008. These include electrophysiology and behavioral (eye movement) data described towards the end of this article.

Criteria for Publication in The Plant Cell

2008-05-22T04:41:31-00:00

Modification of the Data Release Policy for Gene Expression Profiling Experiments

2008-05-22T04:40:18-00:00

Gene expression omnibus: microarray data storage, submission, retrieval, and analysis.

T Barrett — 2008-05-21T20:52:44-00:00

Methods in enzymology, Vol. 411 (2006), pp. 352-369.

The Gene Expression Omnibus (GEO) repository at the National Center for Biotechnology Information archives and freely distributes high-throughput molecular abundance data, predominantly gene expression data generated by DNA microarray technology. The database has a flexible design that can handle diverse styles of both unprocessed and processed data in a Minimum Information About a Microarray Experiment-supportive infrastructure that promotes fully annotated submissions. GEO currently stores about a billion individual gene expression measurements, derived from over 100 organisms, submitted by over 1500 laboratories, addressing a wide range of biological phenomena. To maximize the utility of these data, several user-friendly web-based interfaces and applications have been implemented that enable effective exploration, query, and visualization of these data at the level of individual genes or entire studies. This chapter describes how data are stored, submission procedures, and mechanisms for data retrieval and query. GEO is publicly accessible at http://www.ncbi.nlm.nih.gov/projects/geo/.

Mining microarray data at NCBI's Gene Expression Omnibus (GEO)*.

T Barrett — 2006-10-31T10:42:17-00:00

Methods Mol Biol, Vol. 338 (2006), pp. 175-190.

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) has emerged as the leading fully public repository for gene expression data. This chapter describes how to use Web-based interfaces, applications, and graphics to effectively explore, visualize, and interpret the hundreds of microarray studies and millions of gene expression patterns stored in GEO. Data can be examined from both experiment-centric and gene-centric perspectives using user-friendly tools that do not require specialized expertise in microarray analysis or time-consuming download of massive data sets. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

Public access for teaching genomics, proteomics, and bioinformatics.

AM Campbell — 2006-07-25T18:29:14-00:00

Cell Biol Educ, Vol. 2, No. 2. (2003), pp. 98-111.

When the human genome project was conceived, its leaders wanted all researchers to have equal access to the data and associated research tools. Their vision of equal access provides an unprecedented teaching opportunity. Teachers and students have free access to the same databases that researchers are using. Furthermore, the recent movement to deliver scientific publications freely has presented a second source of current information for teaching. I have developed a genomics course that incorporates many of the public-domain databases, research tools, and peer-reviewed journals. These online resources provide students with exciting entree into the new fields of genomics, proteomics, and bioinformatics. In this essay, I outline how these fields are especially well suited for inclusion in the undergraduate curriculum. Assessment data indicate that my students were able to utilize online information to achieve the educational goals of the course and that the experience positively influenced their perceptions of how they might contribute to biology.

NCBI GEO standards and services for microarray data

Ron Edgar — 2006-12-12T03:05:11-00:00

Nature Biotechnology, Vol. 24, No. 12., pp. 1471-1472.

Enabling integrative genomic analysis of high-impact human diseases through text mining.

J Dudley — 2008-05-14T20:17:55-00:00

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing (2008), pp. 580-591.

Our limited ability to perform large-scale translational discovery and analysis of disease characterizations from public genomic data repositories remains a major bottleneck in efforts to translate genomics experiments to medicine. Through comprehensive, integrative genomic analysis of all available human disease characterizations we gain crucial insight into the molecular phenomena underlying pathogenesis as well as intra- and inter-disease differentiation. Such knowledge is crucial in the development of improved clinical diagnostics and the identification of molecular targets for novel therapeutics. In this study we build on our previous work to realize the next important step in large-scale translational discovery and analysis, which is to automatically identify those genomic experiments in which a disease state is compared to a normal control state. We present an automated text mining method that employs Natural Language Processing (NLP) techniques to automatically identify disease-related experiments in the NCBI Gene Expression Omnibus (GEO) that include measurements for both disease and normal control states. In this manner, we find that 62% of disease-related experiments contain sample subsets that can be automatically identified as normal controls. Furthermore, we calculate that the identified experiments characterize diseases that contribute to 30% of all human disease-related mortality in the United States. This work demonstrates that we now have the necessary tools and methods to initiate large-scale translational bioinformatics inquiry across the broad spectrum of high-impact human disease.

Standards for microarray data.

CA Ball — 2008-05-11T11:46:56-00:00

Science (New York, N.Y.), Vol. 298, No. 5593. (18 October 2002)

Envisioning a Biomedical Data Reuse Registry

Heather Piwowar — 2008-05-11T10:41:35-00:00

Blog post on March 24, 2008: http://researchremix.wordpress.com/2008/03/24/envisioning-a-biomedical-data-reuse-registry/

Human variome microattribution reviews.

2007-12-30T06:53:19-00:00

Nat Genet, Vol. 40, No. 1. (January 2008)

Does the CONSORT checklist improve the quality of reports of randomised controlled trials? A systematic review.

AC Plint — 2008-05-10T07:29:49-00:00

The Medical journal of Australia, Vol. 185, No. 5. (4 September 2006), pp. 263-267.

OBJECTIVE: To determine whether the adoption of the CONSORT checklist is associated with improvement in the quality of reporting of randomised controlled trials (RCTs). DATA SOURCES: MEDLINE, EMBASE, Cochrane CENTRAL, and reference lists of included studies and of experts were searched to identify eligible studies published between 1996 and 2005. STUDY SELECTION: Studies were eligible if they (a) compared CONSORT-adopting and non-adopting journals after the publication of CONSORT, (b) compared CONSORT adopters before and after publication of CONSORT, or (c) a combination of (a) and (b). Outcomes examined included reports for any of the 22 items on the CONSORT checklist or overall trial quality. DATA SYNTHESIS: 1128 studies were retrieved, of which 248 were considered possibly relevant. Eight studies were included in the review. CONSORT adopters had significantly better reporting of the method of sequence generation (risk ratio [RR], 1.67; 95% CI, 1.19-2.33), allocation concealment (RR, 1.66; 95% CI, 1.37-2.00) and overall number of CONSORT items than non-adopters (standardised mean difference, 0.83; 95% CI, 0.46-1.19). CONSORT adoption had less effect on reporting of participant flow (RR, 1.14; 95% CI, 0.89-1.46) and blinding of participants (RR, 1.09; 95% CI, 0.84-1.43) or data analysts (RR, 5.44; 95% CI, 0.73-36.87). In studies examining CONSORT-adopting journals before and after the publication of CONSORT, description of the method of sequence generation (RR, 2.78; 95% CI, 1.78-4.33), participant flow (RR, 8.06; 95% CI, 4.10-15.83), and total CONSORT items (standardised mean difference, 3.67 items; 95% CI, 2.09-5.25) were improved after adoption of CONSORT by the journal. CONCLUSIONS: Journal adoption of CONSORT is associated with improved reporting of RCTs.

Microarray standards at last.

2008-05-10T06:32:39-00:00

Nature, Vol. 419, No. 6905. (26 September 2002)

Real Science: What it Is and What it Means

John Ziman — 2008-05-10T04:45:41-00:00

(15 May 2000)

Unlike most other similar books, this systematic, carefully reasoned, non-technical analysis of the nature and significance of scientific knowledge opens the way to reconciliation in the 'science wars'. By describing how academic scientists actually undertake research and communicate their findings, it shows that the philosophy, psychology and sociology of science are inextricably entwined, and that 'realism' and 'relativism' are just two sides of the same coin. The writing is well-informed, down-to-earth and lucid.

The sociology of science: Theoretical and empirical investigations

Robert Merton — 2008-05-10T04:42:14-00:00

(28 December 1973)

Lead editorial: trials - using the opportunities of electronic publishing to improve the reporting of randomised trials.

DG Altman — 2008-05-10T04:23:54-00:00

Trials, Vol. 7 (2006)

This editorial introduces the new online, open access, peer-reviewed journal Trials. The journal considers manuscripts on any aspect of the design, performance, and findings of randomised controlled trials in any discipline related to health care, and also encourages the publication of protocols. Trialists will be able to provide the necessary detail for a true and complete scientific record. They will be able to communicate not only all outcome measures, as well as varying analyses and interpretations, but also in-depth descriptions of what they did and honest reflections about what they learnt.Trials also encourages articles covering generic issues related to trials, for example focussing on the design, conduct, analysis, interpretation, or reporting.

Code of conduct is needed for publishing raw data.

G Eysenbach — 2008-05-10T04:23:20-00:00

BMJ (Clinical research ed.), Vol. 323, No. 7305. (21 July 2001)

Sharing publication-related data and materials: responsibilities of authorship in the life sciences.

TR Cech — 2008-05-10T02:36:11-00:00

Plant physiology, Vol. 132, No. 1. (May 2003), pp. 19-24.

Responsible Science, Volume II: Background Papers and Resource Documents (Responsible Science)

Panel — 2008-05-09T16:55:35-00:00

(01 January 1993)

Responsible Science, Volume I: Ensuring the Integrity of the Research Process

Panel — 2008-05-09T16:54:35-00:00

(01 January 1992)

Collaborative information synthesis I: A model of information behaviors of scientists in medicine and public health

Catherine Blake — 2008-04-30T14:59:19-00:00

Journal of the American Society for Information Science and Technology, Vol. 57, No. 13. (November 2006)

Scientists engage in the discovery process more than any other user population, yet their day-to-day activities are often elusive. One activity that consumes much of a scientist's time is developing models that balance contradictory and redundant evidence. Driven by our desire to understand the information behaviors of this important user group, and the behaviors of scientific discovery in general, we conducted an observational study of academic research scientists as they resolved different experimental results reported in the biomedical literature. This article is the first of two that reports our findings. In this article, we introduce the Collaborative Information Synthesis (CIS) model that reflects the salient information behaviors that we observed. The CIS model emerges from a rich collection of qualitative data including interviews, electronic recordings of meetings, meeting minutes, e-mail communications, and extraction worksheets. Our findings suggest that scientists provide two information constructs: a hypothesis projection and context information. They also engage in four critical tasks: retrieval, extraction, verification, and analysis. The findings also suggest that science is not an individual but rather a collaborative activity and that scientists use the results of one analysis to inform new analyses. In Part 2, we compare and contrast existing information and cognitive models that have inadvertently reported synthesis, and then provide five recommendations that will enable designers to build information systems that support the important synthesis activity.

Citation analysis: The counting house

David Adam — 2008-04-30T14:40:39-00:00

Nature, Vol. 415, No. 6873. (14 February 2002), pp. 726-729.

The Scientist: Secretive, Selfish or Reticent? A Social Network Analysis

Richard Giordano — 2008-04-29T17:48:59-00:00

(2007)

This paper was presented at the "Third International Conference on e-Social Science 2007." The abstract is: The central question of this paper is what influences collaborators in electronic work environments to decide what knowledge to exchange, how much to exchange, and under what circumstances. Are scientists motivated by purely selfish economic considerations, or is there a role for social relations to influence behavior? This paper develops an empirically-based theoretical framework based on Mark Granovetter's (1985) theory of social embeddedness that helps to explain the conditions for knowledge exchange in Grid environments. This is an early analysis based on interviews with a small population of researchers in molecular microbiology, molecular biology, and cell signaling in the United Kingdom. We conducted a small, preliminary social network analysis of this group to determine what they exchange, with whom, and under what conditions. By identifying the role that social embeddedness plays in motivating scientists, we not only begin to understand the contours of knowledge and information exchange in e-science, but we can also develop reasonable expectations of knowledge-sharing patterns across individual scientists and laboratories.

Issues in biomedical research data management and analysis: needs and barriers.

NR Anderson — 2008-02-22T03:29:38-00:00

J Am Med Inform Assoc, Vol. 14, No. 4. (g 2007), pp. 478-488.

OBJECTIVES: A. Identify the current state of data management needs of academic biomedical researchers. B. Explore their anticipated data management and analysis needs. C. Identify barriers to addressing those needs. DESIGN: A multimodal needs analysis was conducted using a combination of an online survey and in-depth one-on-one semi-structured interviews. Subjects were recruited via an e-mail list representing a wide range of academic biomedical researchers in the Pacific Northwest. MEASUREMENTS: The results from 286 survey respondents were used to provide triangulation of the qualitative analysis of data gathered from 15 semi-structured in-depth interviews. RESULTS: Three major themes were identified: 1) there continues to be widespread use of basic general-purpose applications for core data management; 2) there is broad perceived need for additional support in managing and analyzing large datasets; and 3) the barriers to acquiring currently available tools are most commonly related to financial burdens on small labs and unmet expectations of institutional support. CONCLUSION: Themes identified in this study suggest that at least some common data management needs will best be served by improving access to basic level tools such that researchers can solve their own problems. Additionally, institutions and informaticians should focus on three components: 1) facilitate and encourage the use of modern data exchange models and standards, enabling researchers to leverage a common layer of interoperability and analysis; 2) improve the ability of researchers to maintain provenance of data and models as they evolve over time though tools and the leveraging of standards; and 3) develop and support information management service cores that could assist in these previous components while providing researchers with unique data analysis and information design support within a spectrum of informatics capabilities.

A review of standards for data exchange within systems biology.

Lena Strömbäck — 2007-04-27T06:29:21-00:00

Proteomics, Vol. 7, No. 6. (16 March 2007), pp. 857-867.

The rapid increase in experimental data within systems biology has increased the need for exchange of data to allow analysis and comparison of larger datasets. This has resulted in a need for standardized formats for representation of such results and currently many formats for representation of data have been developed or are under development. In this paper, we give an overview of the current state of available standards and ontologies within systems biology. We focus on XML-based standards for exchange of data and give a thorough description of similarities and differences of currently available formats. For each of these, we discuss how the important concepts such as substances, interactions, and experimental data can be represented. In particular, we note that the purpose of a standard is often visible in the structures it provides for the representation of data. A clear purpose is also crucial for the success of a standard. Moreover, we note that the development of representation formats is parallel to the development of ontologies and the recent trend is that representation formats make more and more use of available ontologies.

Borrowed theory: Applying exchange theories in information science research

Hazel Hall — 2008-04-29T15:20:27-00:00

Library & Information Science Research, Vol. 25, No. 3. (2003), pp. 287-306.

Social Network Analysis of Information Exchange Among Environmental Organizations in Alabama

Laura Robinson — 2008-04-29T15:11:52-00:00

(August 2006)

Introduction to Social Network Methods: Table of Contents

2008-04-29T15:10:15-00:00

Academic topographies : A network analysis of disciplinarity among communication faculty

WJ White — 2008-04-29T15:09:25-00:00

Human communication research, Vol. 25, No. 4. (1999), pp. 604-617.

This study draws on the tenets of structuration theory and the methodological prescriptions of Archer to examine how theoretical and paradigmatic differences among interdisciplinary faculty are reflected in the ongoing work of their school. It uses network analyses of Ph.D. committee participation from 1984 to 1995 among 29 faculty members of a school with departments of communication, journalism, and library and information science to assess how organizational and cognitive structures serve to constrain or pattern the cohesion of faculty members. The analyses presented here are used to develop a snapshot ofa scholarly community undergoing incremental change over time. Analysis reveals the presence of two groups of faculty whose members coparticipate more cohesively within themselves than between groups. The sources of the division include the structure of departments within the school as well as the levels of analysis and research traditions enacted by faculty. The study constructively replicates similar analyses.