EASE

Tag: RESEARCH EVALUATION

  • B – Research impact assessment models and methods

    Milat AJ, Bauman AE, Redman S. A narrative review of research impact assessment models and methods. Health Research Policy and Systems 2015;13:18
    (doi: 10.1186/s12961-015-0003-1)

    The purpose of this narrative literature review is to synthesize evidence that describes processes and conceptual models for assessing policy and practice impacts of public health research. The literature is characterised by an over reliance on bibliometric methods to assess research impact. Future impact assessment processes could be strengthened by routinely engaging the end-users of research in interviews and assessment processes.
    http://www.health-policy-systems.com/content/13/1/18

  • B – The Resource Identification Initiative

    Bandrowski A, Brush M, Grethe JS, et al. The Resource Identification Initiative: a cultural shift in publishing. Journal of Comparative Neurology 2016;524(1):8-22
    (doi: 10.1002/cne.23913)

    The Resource Identification Initiative was launched as a pilot project to improve the reporting standards for research resources in the Methods sections of articles and thereby improve identifiability and scientific reproducibility. The pilot engaged over 25 biomedical journal editors from most major publishers, as well as scientists and funding officials. Authors were asked to include Research Resource Identifiers (RRIDs) in their articles prior to publication for three resource types: antibodies, model organisms, and tools (i.e., software and databases). RRIDs are assigned by an authoritative database.
    http://www.ncbi.nlm.nih.gov/pubmed/26599696

  • B – Storing and accessing biomedical big data

    Bourne PE, Lorsch JR. Green ED. Sustaining the big-data ecosystem. Nature Nov 5 2015;527
    (doi: 10.1038/527S16a)

    Biomedical big data offer tremendous potential for making discoveries, but the cost of sustaining these digital assets and the resources needed to make them useful have received relatively little attention. Funders should encourage the development of new metrics to ascertain the usage and value of data and when we have a better understanding of data usage, we can develop business models for storing, organizing and accessing them. Tools and rewards that incentivize researchers to submit their data to data resources in ways that maximize both quality and ease of access, are also needed.
    http://www.nature.com/nature/journal/v527/n7576_supp/full/527S16a.html

  • B – Scientometric analysis on big data

    Vivek KS, Sumit KB, Khushboo S, et al. Scientometric mapping of research on “Big Data”. Scientometrics e-pub 9 Sept. 2015
    (doi: 10.1007/s11192-015-1729-9)

    This paper presents a scientometric analysis of research work done on the emerging area of “Big Data” in the years 2010-2014. The analysis maps comprehensively the parameters of total output, growth of output, authorship and country-level collaboration patterns, major contributors (countries, institutions and individuals), top publication sources, thematic trends and emerging themes in the field.
    http://rd.springer.com/article/10.1007%2Fs11192-015-1729-9

  • B – Reasons for abandoning clinical trials

    Couzin-Frankel J. Researchers seek clear reasons when clinical trials end early. Science July 2015;349(6245):222
    (doi: 10.1126/science.349.6245.222)

    About 12% of clinical trials are reported to shut down prematurely. Knowing why could help minimize the number of terminated trials going forward. A team of three computational biologists began exploring why clinical trials end prematurely. They looked at all 3122 terminated trials on the ClinicalTrials.gov registry at the time their study began, and divided the reasons for ending early into “buckets,” such as funding, ethical reasons, or business decisions, so they could see the breakdown by category.  but those explanations were often hazy.
    https://www.sciencemag.org/content/349/6245/222.full

  • B – Beyond the impact factor?

    Fazel S, Lamsma J. Beyond the impact factor? Evidence Based Mental Health 2015;18:33-35
    (doi: 10.1136/eb-2015-102087)

    To investigate the possible differences between the Journal Impact Factor (JIF) and new journal metrics, the authors ranked the top 30 journals in the clinical neurosciences (ie, psychology, psychiatry, neuroscience and general medicine) based on their JIF and compared their JIF ranking with one that was a composite score of their JIF, h5-index, Impact per Publication (IPP), Source Normalised Impact per Paper (SNIP)  and SCImago Journal Rank (SJR) rankings. They recommend researchers and funders should support those journals that aim to increase value and reduce waste and consider a range of impacts, including different journal impact factors, when deciding on journal choice.
    http://ebmh.bmj.com/content/18/2/33.full

  • B – High-impact-factor syndrome

    Caves C.M. High-impact-factor Syndrome. APS News 2014;23(10):8,6

    The author discusses the use of the bibliometric high impact factor used as a proxy for assessing a scientist’s work, the malign influence this is having. He suggests a number of ways to try to prevent this and to conform to best practices for conducting and evaluating research.
    http://www.aps.org/publications/apsnews/201411/backpage.cfm

  • B – The Kardashian index

    Hall N. The Kardashian index: a measure of discrepant social media profile for scientists. Genome Biology 2014;15:424
    (doi: 10.1186/s13059-014-0424-0)

    The author proposes the “Kardashian index” (from the name of one of the most followed people on twitter), a measure of discrepancy between a scientist’s social media profile and publication record based on the direct comparison of numbers of citations and twitter followers. He has compared the numbers of followers that research scientists have on twitter with the number of citations they have for their peer-reviewed work.
    http://rd.springer.com/article/10.1186%2Fs13059-014-0424-0

  • B – Impact factor mania

    Casadevall A, Fang FC. Causes for the persistence of impact factor mania. mBio 2014;5(2):e00064-14 
    (doi: 10.1128/mBio.00064-14)

    Science and scientists are currently afflicted by an epidemic of mania manifested by associating the value of research with the journal where the work is published rather than the content of the work itself. The authors consider the reasons for the persistence of impact factor mania and its pernicious effects on science. They conclude that impact factor mania persists because it confers significant benefits to individual scientists and journals. Various measures to reduce the influence of the impact factor are considered.      
    http://mbio.asm.org/content/5/2/e00064-14.full
                  

     

     

  • B – Citation increments between collaborating countries


    (doi: 10.1007/s11192-012-0797-3)

    International collaboration enhances citation impact. Collaborating with a country increments the citations received from it. The authors observed a certain tendency for these increments to be lower in countries with greater impacts, and differences in the behaviour of the countries between the various scientific disciplines, with the effects being greatest in Social Sciences, followed by Engineering.
    http://rd.springer.com/article/10.1007%2Fs11192-012-0797-3

  • B – Online-to-print delays and impact factor

    Tort ABL, Targino ZH, Amaral OB. Rising publication delays inflate journal impact factors. PLoS ONE 2012;7(2):e53374
    (doi: 10.1371/journale.pone.0053374)

    In this study the authors used publication records of neuroscience journals to analyze the evolution of publication delay over the last decade, and to study whether this phenomenon can alter journal impact factors. They showed that online-to-print lags (that is, the delay between online availability of an article and its print publication) have risen steeply in recent years, and that they led to earlier citations, and thus to an increase in impact factors. According to the authors, a simple means to avoid distortions such as the one described is the indexing of articles by scientific databases on the date of their online appearance, rather than on that of their publication in print.
    http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0053374

  • B – Japanese randomized controlled trials

    Yoneoka D, Hisashige A, Ota E, et al. Are Japanese randomized controlled trials up to the task? A systematic review. PLoS ONE 2014;9(3):e90127
    (doi: 10.1371/journal.pone.0090127)

    The number of published randomized controlled trials (RCTs) is rapidly increasing worldwide. This study identified the number of all Japanese RCTs published in Japan in 2010, it assessed their general characteristics and quality and analyzed factors related to their quality. Despite a considerable number of RCTs conducted in Japan, their quality is not satisfactory in some domains. On the other hand, there are high-quality, non-indexed RCTs. The full disclosure of trial information and quality control of clinical trials are urgently needed in Japan.
    http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0090127