The conclusions of research articles generally depend on bodies of data that cannot be included in the articles themselves. The sharing of this data is important for reasons of both transparency and possible reuse. Science, Technology and Medicine journals have an obvious role in facilitating sharing, but how they might do that is not yet clear.
Would more researchers share data if they got more for it? Possibly. The currency does not even have to change. What is missing in the academic system is the recognition for intermediaries, also for data. Those who publish well get cited. The H-index increases and thereby the chances for professional advancement. Good articles are good for the career. Good data however are still not as important than they should be.
Writer Seth Godin explains why it’s absolutely fine if you steal his ideas … you have to promise to make them better.
Ideas. TED.
Looking at Open Science and Open Data from a broad perspective. This is the idea behind “Scientific data sharing: an interdisciplinary workshop”, an initiative designed to foster dialogue between scholars from different scientific domains which was organized by the Istituto Italiano di Antropologia in Anagni, Italy, 2-4 September 2013.We here report summaries of the presentations and discussions at the meeting. They deal with four sets of issues: (i) setting a common framework, a general discussion of open data principles, values and opportunities; (ii) insights into scientific practices, a view of the way in which the open data movement is developing in a variety of scientific domains (biology, psychology, epidemiology and archaeology); (iii) a case study of human genomics, which was a trail-blazer in data sharing, and which encapsulates the tension that can occur between large-scale data sharing and one of the boundaries of openness, the protection of individual data; (iv) open science and the public, based on a round table discussion about the public communication of science and the societal implications of open science.
Despite widespread support from policy makers, funding agencies, and scientific journals, academic researchers rarely make their research data available to others. At the same time, data sharing in research is attributed a vast potential for scientific progress. It allows the reproducibility of study results and the reuse of old data for new research questions. Based on a systematic review of 98 scholarly papers and an empirical survey among 603 secondary data users, we develop a conceptual framework that explains the process of data sharing from the primary researcher’s point of view. We show that this process can be divided into six descriptive categories: Data donor, research organization, research community, norms, data infrastructure, and data recipients. Drawing from our findings, we discuss theoretical implications regarding knowledge creation and dissemination as well as research policy measures to foster academic collaboration. We conclude that research data cannot be regarded a knowledge commons, but research policies that better incentivise data sharing are needed to improve the quality of research results and foster scientific progress.
Aside from the ethics and etiquette of fully open data-sharing, there are practical issues that journals still need to address. One is the cost of sharing data. Both the Public Library of Science and the UK Royal Society recommend the storage repository Dryad, which currently charges US$15 for the first gigabyte of data over its 10-gigabyte limit, and $10 per gigabyte thereafter. However, studies in areas such as neuroscience can generate terabytes of raw data (1 terabyte is 1,000 gigabytes) — a quantity that few labs could afford to upload.
Open science is a practice in which the scientific process is shared completely and in real time. It offers the potential to support information flow, collaboration and dialogue among professional and non-professional participants. Using semi-structured interviews and case studies, this research investigated the relationship between open science and public engagement. This article concentrates on three particular areas of concern that emerged: first, how to effectively contextualise and narrate information to render it accessible, as opposed to simply available; second, concerns about data quantity and quality; and third, concerns about the skills required for effective contextualisation, mapping and interpretation of information
Long before the term "citizen science" was coined, the field of astronomy has benefited from countless men and women who study the sky in their spare time.
Today, most research projects are considered complete when a journal article based on the analysis has been written and published. The trouble is, unlike Galileo's report in Sidereus Nuncius, the amount of real data and data description in modern publications is almost never sufficient to repeat or even statistically verify a study being presented. Worse, researchers wishing to build upon and extend work presented in the literature often have trouble recovering data associated with an article after it has been published. More often than scientists would like to admit, they cannot even recover the data associated with their own published works.
Have you, or your organization, created or repackaged/reused quality content (from briefing papers, to presentations, videos, etc) on open access, open data and open science in any language, which can be useful and used on the FOSTER training context?
I co-founded the Public Library of Science (PLOS) in 2002 because I believed deeply that the open access publishing model PLOS espoused and has come to dominate was good for science, scientists and the public. Over the past decade open access has become a personal crusade – my own religion – one I have fervently promoted here on this blog, on social media, and to thousands of colleagues at meetings and social engagements. To back up my commitment to open access, since 2000, I have exclusively published papers from my lab in open access journals, and have urged – some might say hectored and harassed – my colleagues to do the same.But in the last few weeks I have had a major change of heart.
The Web of Science and its corresponding Journal Impact Factor are inadequate for an understanding of the impact of scholarly work from developing regions, argues Juan Pablo Alperin. Alternative metrics offer the opportunity to redirect incentive structures towards problems that contribute to development, or at least to local priorities. But the altmetrics community needs to actively engage with scholars from developing regions to ensure the new metrics do not continue to cater to well-known and well-established networks.
LSE Impact Blog.
Was diese Prognosen antreibt, sind drei Vorstellungen. (1) „Das Internet“ ist eine Technologie, die aus sich heraus alle Eigenschaften des Wissens verändern wird. (2) Wissenschaft und Universität waren zuvor gefesselt und kompromittiert, so dass die Zeit reif ist, um sie neu zu fassen. (3) Mehr „Offenheit“ ist die Lösung für alle Probleme und nicht zuletzt politisch unbedingt und ausnahmslos wünschbar. Alle drei Ansichten sind weit verbreitet. Keine ist so, wie sie hier formuliert wurde, wahr. Über die erste Behauptung will ich mich hier nicht weiter äußern, das haben andere getan, die den naiven technologischen Determinismus kritisiert haben, dem anheimfällt, wer das Internet wie ein Ding behandelt, das uns ein bestimmtes Verhalten auferlegt. Was mich mehr beschäftigt ist die Politische Ökonomie der „Wissenschaft 2.0“ und die Art, wie sie jene politische Entscheidungen unsichtbar macht, die wir in Bezug auf all die genannten Fragen treffen müssen. Vielleicht fasse ich meine Frage am besten so: Wenn „Offenheit“ die Lösung sein soll, was ist dann das Problem?
Beide Seiten aber fänden es gut, wenn alle Zusatzkosten der Publikation auf die Universitäten verlagert würden, von denen sie ohnehin nicht glauben, dass sie die Zukunft der Forschung repräsentieren. So ist die einzige plausible Prognose die, dass wenn die Rolle von Universitäten und Bibliotheken weiter untergraben wird, das gesamte System der „Peer Review“ ersetzt werden wird durch eine Art marktbasierte Evaluation von Artikeln, die dann im Stile von „Gefällt mir“-Buttons der Weisheit der Menge überlassen bleibt. Insofern hat „Open Science 2.0“ nichts mit einer Demokratisierung oder anderweitigen Verbesserung von Forschung zu tun. Was damit bezweckt wird ist vielmehr, einige große Firmen an den Eingängen zur modernen Kommerzialisierung des Wissens gut zu positionieren.
METRIC, The Economist reports, is led by Stanford professor John Ioannidis, an epidemiologist who in 2005 famously wrote "Why most published research findings are false," a paper that sought to prove that research can and is often marred by sloppy statistical findings and shoddy ideas.