Authors : Nadine Levin, Sabina Leonelli, Dagmara Weckowska, David Castle, John Dupré
This article documents how biomedical researchers in the United Kingdom understand and enact the idea of “openness.”
This is of particular interest to researchers and science policy worldwide in view of the recent adoption of pioneering policies on Open Science and Open Access by the U.K. government—policies whose impact on and implications for research practice are in need of urgent evaluation, so as to decide on their eventual implementation elsewhere.
This study is based on 22 in-depth interviews with U.K. researchers in systems biology, synthetic biology, and bioinformatics, which were conducted between September 2013 and February 2014.
Through an analysis of the interview transcripts, we identify seven core themes that characterize researchers’ understanding of openness in science and nine factors that shape the practice of openness in research.
Our findings highlight the implications that Open Science policies can have for research processes and outcomes and provide recommendations for enhancing their content, effectiveness, and implementation.
The ability to measure the use and impact of published data sets is key to the success of the open data/open science paradigm. A direct measure of impact would require tracking data (re)use in the wild, which is difficult to achieve.
This is therefore commonly replaced by simpler metrics based on data download and citation counts. In this paper we describe a scenario where it is possible to track the trajectory of a dataset after its publication, and show how this enables the design of accurate models for ascribing credit to data originators.
A Data Trajectory (DT) is a graph that encodes knowledge of how, by whom, and in which context data has been re-used, possibly after several generations. We provide a theoretical model of DTs that is grounded in the W3C PROV data model for provenance, and we show how DTs can be used to automatically propagate a fraction of the credit associated with transitively derived datasets, back to original data contributors.
We also show this model of transitive credit in action by means of a Data Reuse Simulator. In the longer term, our ultimate hope is that credit models based on direct measures of data reuse will provide further incentives to data publication.
We conclude by outlining a research agenda to address the hard questions of creating, collecting, and using DTs systematically across a large number of data reuse instances in the wild.
As scientific data volumes, format types, and sources increase rapidly with the invention and improvement of scientific capabilities, the resulting datasets are becoming more complex to manage as well.
One of the significant management challenges is pulling apart the individual contributions of specific people and organizations within large, complex projects.
This is important for two aspects:1) assigning responsibility and accountability for scientific work, and 2) giving professional credit to individuals (e.g. hiring, promotion, and tenure) who work within such large projects.
This paper aims to review the extant practice of data attribution and how it may be improved. Through a case study of creating a detailed attribution record for a climate model dataset, the paper evaluates the strengths and weaknesses of the current data attribution method and proposes an alternative attribution framework accordingly.
The paper concludes by demonstrating that, analogous to acknowledging the different roles and responsibilities shown in movie credits, the methodology developed in the study could be used in general to identify and map out the relationships among the organizations and individuals who had contributed to a dataset.
As a result, the framework could be applied to create data attribution for other dataset types beyond climate model datasets.
The development of e-Research infrastructure has enabled data to be shared and accessed more openly. Policy mandates for data sharing have contributed to the increasing availability of research data through data repositories, which create favourable conditions for the re-use of data for purposes not always anticipated by original collectors.
Despite the current efforts to promote transparency and reproducibility in science, datare-use cannot be assumed, nor merely considered a ‘thrifting’ activity where scientists shop around in datarepositories considering only the ease of access to data.
The lack of an integrated view of individual, socialand technological influential factors to intentional and actual data re-use behaviour was the key motivatorfor this study. Interviews with 13 social scientists produced 25 factors that were found to influence theirperceptions and experiences, including both their unsuccessful and successful attempts to re-use data.
These factors were grouped into six theoretical variables: perceived benefits, perceived risks, perceived effort,social influence, facilitating conditions, and perceived re-usability.
These research findings provide an in-depth understanding about the re-use of research data in the context of open science, which can be valuablein terms of theory and practice to help leverage data re-use and make publicly available data moreactionable.
La question émergente en France des données de la recherche se situe dans un cadre institutionnel foisonnant mais rigide, délicat à cerner. La recherche est aussi financée et évaluée au niveau européen.
Cette organisation nationale et européenne se double d’un aspect international inhérent à la recherche et aux échanges d’informations rapides et répétés, accélérés par le développement d’Internet.
Le labyrinthe institutionnel franco-européen se superpose ainsi avec le millefeuille international et disciplinaire du monde de la recherche. Enfin, la proximité de deux mouvements qui ne sont pourtant pas synonyme, l’Open Access et l’Open Data, vient encore troubler la compréhension de ce panorama.
Il n’est donc pas aisé de comprendre les rôles de chacun des acteurs quant aux données de la recherche. C’est à une clarification de ce paysage que nous nous proposons de participer, en initiant une cartographie des initiatives et acteurs visibles en France concernant les données des sciences humaines et sociales.
Authors : Mallory C. Kidwell, Ljiljana B. Lazarević, Erica Baranski, Tom E. Hardwicke, Sarah Piechowski, Lina-Sophia Falkenberg, Curtis Kennett, Agnieszka Slowik, Carina Sonnleitner, Chelsey Hess-Holden, Timothy M. Errington, Susann Fiedler, Brian A. Nosek
Beginning January 2014, Psychological Science gave authors the opportunity to signal open data and materials if they qualified for badges that accompanied published articles. Before badges, less than 3% of Psychological Science articles reported open data.
After badges, 23% reported open data, with an accelerating trend; 39% reported open data in the first half of 2015, an increase of more than an order of magnitude from baseline. There was no change over time in the low rates of data sharing among comparison journals.
Moreover, reporting openness does not guarantee openness. When badges were earned, reportedly available data were more likely to be actually available, correct, usable, and complete than when badges were not earned.
Open materials also increased to a weaker degree, and there was more variability among comparison journals. Badges are simple, effective signals to promote open practices and improve preservation of data and materials by using independent repositories.
Auteurs/Authors : Stefan Buddenbohm, Nathanael Cretin, Elly Dijk, Bertrand Gaie, Maaike De Jong, Jean-Luc Minel, Blandine Nouvel
Publishing research data as open data is not yet common practice for researchers in the arts and humanities, and lags behind other scientific fields, such as the natural sciences. Moreover, even when humanities researchers publish their data in repositories and archives, these data are often hard to find and use by other researchers in the field.
The goal of Work Package 7 of the the HaS (Humanities at Scale) DARIAH project is to develop an open humanities data platform for the humanities. Work in task 7.1 is a joint effort of Data Archiving and Networked Services (DANS), Centre National de la Recherche Scientifique (CNRS) and the University of Göttingen – State and University Library (UGOE-SUB).
This report gives an overview of the various aspects that are connected to open access publishing of research data in the humanities. After the introduction, where we give definitions of key concepts, we describe the research data life cycle.
We present an overview of the different stakeholders involved and we look into advantages and obstacles for researchers to share research data. Furthermore, a description of the European data repositories is given, followed by certification standards of trusted digital data repositories.
The possibility of data citation is important for sharing open data and is also described in this report. We also discuss the standards and use of metadata in the humanities. Finally, we discuss best practice example of open access research data system in the humanities: the French open research data ecosystem.
With this report we provide information and guidance on open access publishing of humanities research data for researchers. The report is the result of a desk study towards the current state of open access research data and the specific challenges for humanities. It will serve as input for Task 7.2., which will deliver a design and sustainability plan for an open humanities data platform, and for Task 7.3, which will deliver this platform.