Crowd science is scientific research that is conducted with the participation of volunteers who are not professional scientists. Thanks to the Internet and online platforms, project initiators can draw on a potentially large number of volunteers. This crowd can be involved to support data-rich or labour-intensive projects that would otherwise be unfeasible. So far, research on crowd science has mainly focused on analysing individual crowd science projects. In our research, we focus on the perspective of project initiators and explore how crowd science projects are set up. Based on multiple case study research, we discuss the objectives of crowd science projects and the strategies of their initiators for accessing volunteers. We also categorise the tasks allocated to volunteers and reflect on the issue of quality assurance as well as feedback mechanisms. With this article, we contribute to a better understanding of how crowd science projects are set up and how volunteers can contribute to science. We suggest that our findings are of practical relevance for initiators of crowd science projects, for science communication as well as for informed science policy making.
Currently blockchain is being hyped. Many claim that the blockchain revolution will affect not only our online life, but will profoundly change many more aspects of our society. Many foresee these changes as potentially being more far-reaching than those brought by the internet in the last two decades. If this holds true, it is certain that research and knowledge creation will also be affected by this. So, what is blockchain all about? More importantly, could knowledge creation benefit from it? One potential area it could be useful is in addressing the credibility and reproducibility crisis in science.
In this study, we explore the citedness of research data, its distribution over time and its relation to the availability of a digital object identifier (DOI) in the Thomson Reuters database Data Citation Index (DCI). We investigate if cited research data “impacts” the (social) web, reflected by altmetrics scores, and if there is any relationship between the number of citations and the sum of altmetrics scores from various social media platforms. Three tools are used to collect altmetrics scores, namely PlumX, ImpactStory, and Altmetric.com, and the corresponding results are compared. We found that out of the three altmetrics tools, PlumX has the best coverage. Our experiments revealed that research data remain mostly uncited (about 85 %), although there has been an increase in citing data sets published since 2008. The percentage of the number of cited research data with a DOI in DCI has decreased in the last years. Only nine repositories are responsible for research data with DOIs and two or more citations. The number of cited research data with altmetrics “foot-prints” is even lower (4–9 %) but shows a higher coverage of research data from the last decade. In our study, we also found no correlation between the number of citations and the total number of altmetrics scores. Yet, certain data types (i.e. survey, aggregate data, and sequence data) are more often cited and also receive higher altmetrics scores. Additionally, we performed citation and altmetric analyses of all research data published between 2011 and 2013 in four different disciplines covered by the DCI. In general, these results correspond very well with the ones obtained for research data cited at least twice and also show low numbers in citations and in altmetrics. Finally, we observed that there are disciplinary differences in the availability and extent of altmetrics scores.
Longo and Drazen miss the very point of scientific research when they write, that the researchers may «even use the data to try to disprove what the original investigators had posited«. It is at the core of the scientific paradigm that researchers take nothing as final truth. This is what Popper proposed in his critical rationalism and Merton in his conceptualization of skepticism.
We want to clarify, given recent concern about our policy, that the Journal is committed to data sharing in the setting of clinical trials. As stated in the Institute of Medicine report from the committee1 on which I served and the recent editorial by the International Committee of Medical Journal Editors (ICMJE),2 we believe there is a moral obligation to the people who volunteer to participate in these trials to ensure that their data are widely and responsibly used.
According to ResearchGate, the academic social networking site, their RG Score is “a new way to measure your scientific reputation”. With such high aims, Peter Kraker, Katy Jordan and Elisabeth Lex take a closer look at the opaque metric. By reverse engineering the score, they find that a significant weight is linked to ‘impact points’ – a similar metric to the widely discredited journal impact factor. Transparency in metrics is the only way scholarly measures can be put into context and the only way biases – which are inherent in all socially created metrics – can be uncovered.
Letzte Woche trat das Editorial Board der linguistischen Fachzeitschrift Lingua geschlossen zurück. Die Gruppe um den Chefredakteur Johan Rooryck, Sprachwissenschaftler an der Universität Leiden, kündigte an, ein neues Journal unter dem Namen Glossa zu gründen. Rooryck begründet den Rücktritt des Editorial Board damit, dass das Verlagshaus Elsevier, bei dem Lingua erscheint, nicht auf deren Open-Access-Bedingungen eingehen will. Lingua existiert seit 1949 und ist auf Google-Scholar immerhin unter den wichtigsten drei sprachwissenschaftlichen Fachzeitschriftenzu finden.
All six editors and all 31 editorial board members of Lingua, one of the top journals in linguistics, last week resigned to protest Elsevier's policies on pricing and its refusal to convert the journal to an open-access publication that would be free online.This might be the beginning of an interesting trend …
Last year, we recruited 29 teams of researchers and asked them to answer the same research question with the same data set.