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The Dawn of Open Access to Phylogenetic Data

Posted on by Hans Dillaerts

« The scientific enterprise depends critically on the preservation of and open access to published data. This basic tenet applies acutely to phylogenies (estimates of evolutionary relationships among species). Increasingly, phylogenies are estimated from increasingly large, genome-scale datasets using increasingly complex statistical methods that require increasing levels of expertise and computational investment. Moreover, the resulting phylogenetic data provide an explicit historical perspective that critically informs research in a vast and growing number of scientific disciplines. One such use is the study of changes in rates of lineage diversification (speciation – extinction) through time. As part of a meta-analysis in this area, we sought to collect phylogenetic data (comprising nucleotide sequence alignment and tree files) from 217 studies published in 46 journals over a 13-year period. We document our attempts to procure those data (from online archives and by direct request to corresponding authors), and report results of analyses (using Bayesian logistic regression) to assess the impact of various factors on the success of our efforts. Overall, complete phylogenetic data for of these studies are effectively lost to science. Our study indicates that phylogenetic data are more likely to be deposited in online archives and/or shared upon request when: (1) the publishing journal has a strong data-sharing policy; (2) the publishing journal has a higher impact factor, and; (3) the data are requested from faculty rather than students. Importantly, our survey spans recent policy initiatives and infrastructural changes; our analyses indicate that the positive impact of these community initiatives has been both dramatic and immediate. Although the results of our study indicate that the situation is dire, our findings also reveal tremendous recent progress in the sharing and preservation of phylogenetic data. »

URL : The Dawn of Open Access to Phylogenetic Data

DOI: 10.1371/journal.pone.0110268

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Co-construire les collections avec les usagers

Posted on by Hans Dillaerts

« La co-construction des collections avec les usagers intéresse de nombreuses bibliothèques de lecture publique, d’une part pour augmenter la proximité de l’institution avec les citoyens et d’autre part dans le but de proposer des collections mieux adaptées aux besoins des lecteurs. Mais elle soulève également des réticences, notamment parce que la collection relève du domaine du professionnel et que l’idée de déléguer ces tâches aux usagers suscite des résistances. Ce mémoire a pour but de délimiter les contours de la co-construction, une notion très discutée mais encore peu définie, d’explorer et d’analyser les enjeux de son application au domaine des collections, et enfin de servir de guide pratique en donnant des lignes directrices et des conseils aux bibliothèques qui souhaitent mettre en place de tels projets. »

URL : Co-construire les collections avec les usagers

Alternative URL : http://www.enssib.fr/bibliotheque-numerique/notices/64143-co-construire-les-collections-avec-les-usagers

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The 1:1 Principle in the Age of Linked Data

Posted on by Hans Dillaerts

« This paper explores the origins of the 1:1 Principle within Dublin Core Metadata Initiative (DCMI). It finds that the need for the 1:1 Principle emerged from prior work among cultural heritage professionals responsible for describing reproductions and surrogate resources using traditional cataloging methods. As the solutions to these problems encountered new ways to model semantic data that emerged outside of libraries, archives, and museums, tensions arose within DCMI community. This paper aims to fill the gaps in our understanding of the 1:1 Principle by outlining the conceptual foundations that led to its inclusion in DCMI documentation, how the Principle has been (mis)understood in practice, how violations of the Principle have been operationalized, and how the fundamental issues raised by the Principle continue to challenge us today. This discussion situates the 1:1 Principle within larger discussions about cataloging practice and emerging Linked Data approaches. »

URL : http://diginole.lib.fsu.edu/slis_faculty_publications/25/

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Estimating Open Access Mandate Effectiveness : I. The MELIBEA Score

Posted on by Hans Dillaerts

« MELIBEA is a Spanish database that uses a composite formula with eight weighted conditions to estimate the effectiveness of Open Access mandates (registered in ROARMAP). We analyzed 68 mandated institutions for publication years 2011-2013 to determine how well the MELIBEA score and its individual conditions predict what percentage of published articles indexed by Web of Knowledge is deposited in each institution’s OA repository, and when. We found a small but significant positive correlation (0.18) between MELIBEA score and deposit percentage. We also found that for three of the eight MELIBEA conditions (deposit timing, internal use, and opt-outs), one value of each was strongly associated with deposit percentage or deposit latency (immediate deposit required, deposit required for performance evaluation, unconditional opt-out allowed for the OA requirement but no opt-out for deposit requirement). When we updated the initial values and weights of the MELIBEA formula for mandate effectiveness to reflect the empirical association we had found, the score’s predictive power doubled (.36). There are not yet enough OA mandates to test further mandate conditions that might contribute to mandate effectiveness, but these findings already suggest that it would be useful for future mandates to adopt these three conditions so as to maximize their effectiveness, and thereby the growth of OA. »

URL : http://arxiv.org/abs/1410.2926op

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Exposing the predators. Methods to stop predatory journals

Posted on by Hans Dillaerts

« The internet is greatly improving the impact of scholarly journals, but also poses new threats to their quality. Publishers have arisen that abuse the Gold Open Access model, in which the author pays a fee to get his article published, to make money with so-called predatory journals. These publishers falsely claim to conduct peer review, which makes them more prone to publish fraudulent and plagiarised research. This thesis looks at three possible methods to stop predatory journals: black- and white-lists, open peer review systems and new metrics. Black- and whitelists have set up rules and regulations that credible publishers and journals should follow. Open peer review systems should make it harder for predatory publishers to make false claims about their peer review process. Metrics should measure more aspects of research impact and become less liable to gaming. The question is, which of these three methods is the best candidate to stop predatory journals. As all three methods have their drawbacks, especially for new but high quality journals, none of them stop predatory journals on its own can. Rather, we need a system in which researchers, publishers and reviewers communicate more openly about the research they create, disseminate and read. But above all, we need to find a way to take away incentives for researchers and publishers to engage in fraudulent practices. »

URL : http://hdl.handle.net/1887/28943

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New data, new possibilities: Exploring the insides of Altmetric.com

Posted on by Hans Dillaerts

« This paper analyzes Altmetric.com, one of the most important altmetric data providers currently used. We have analyzed a set of publications with DOI number indexed in the Web of Science during the period 2011-2013 and collected their data with the Altmetric API. 19% of the original set of papers was retrieved from Altmetric.com including some altmetric data. We identified 16 different social media sources from which Altmetric.com retrieves data. However five of them cover 95.5% of the total set. Twitter (87.1%) and Mendeley (64.8%) have the highest coverage. We conclude that Altmetric.com is a transparent, rich and accurate tool for altmetric data. Nevertheless, there are still potential limitations on its exhaustiveness as well as on the selection of social media sources that need further research. »

URL : http://arxiv.org/abs/1408.0135

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Using Crowdsourcing to Evaluate Published Scientific Literature: Methods and Example

Posted on by Hans Dillaerts

« Systematically evaluating scientific literature is a time consuming endeavor that requires hours of coding and rating. Here, we describe a method to distribute these tasks across a large group through online crowdsourcing. Using Amazon’s Mechanical Turk, crowdsourced workers (microworkers) completed four groups of tasks to evaluate the question, “Do nutrition-obesity studies with conclusions concordant with popular opinion receive more attention in the scientific community than do those that are discordant?” 1) Microworkers who passed a qualification test (19% passed) evaluated abstracts to determine if they were about human studies investigating nutrition and obesity. Agreement between the first two raters’ conclusions was moderate (κ = 0.586), with consensus being reached in 96% of abstracts. 2) Microworkers iteratively synthesized free-text answers describing the studied foods into one coherent term. Approximately 84% of foods were agreed upon, with only 4 and 8% of ratings failing manual review in different steps. 3) Microworkers were asked to rate the perceived obesogenicity of the synthesized food terms. Over 99% of responses were complete and usable, and opinions of the microworkers qualitatively matched the authors’ expert expectations (e.g., sugar-sweetened beverages were thought to cause obesity and fruits and vegetables were thought to prevent obesity). 4) Microworkers extracted citation counts for each paper through Google Scholar. Microworkers reached consensus or unanimous agreement for all successful searches. To answer the example question, data were aggregated and analyzed, and showed no significant association between popular opinion and attention the paper received as measured by Scimago Journal Rank and citation counts. Direct microworker costs totaled $221.75, (estimated cost at minimum wage: $312.61). We discuss important points to consider to ensure good quality control and appropriate pay for microworkers. With good reliability and low cost, crowdsourcing has potential to evaluate published literature in a cost-effective, quick, and reliable manner using existing, easily accessible resources. »

URL : Using Crowdsourcing to Evaluate Published Scientific Literature: Methods and Example

DOI: 10.1371/journal.pone.0100647