Legitimacy of reusing images from scientific papers addressed

It goes without saying that scientific research has to build on previous breakthroughs and publications. However, it feels quite counter-intuitive for data and their re-use to be legally restricted. Yet, that is what happens when copyright restrictions are placed on many scientific papers.

The discipline of taxonomy is highly reliant on previously published photographs, drawings and other images as biodiversity data. Inspired by the uncertainty among taxonomists, a team, representing both taxonomists and experts in rights and copyright law, has traced the role and relevance of copyright when it comes to images with scientific value. Their discussion and conclusions are published in the latest paper added in the EU BON Collection in the open science journal Research Ideas and Outcomes (RIO).

Taxonomic papers, by definition, cite a large number of previous publications, for instance, when comparing a new species to closely related ones that have already been described. Often it is necessary to use images to demonstrate characteristic traits and morphological differences or similarities. In this role, the images are best seen as biodiversity data rather than artwork. According to the authors, this puts them outside the scope, purposes and principles of Copyright. Moreover, such images are most useful when they are presented in a standardized fashion, and lack the artistic creativity that would otherwise make them ‘copyrightable works’.

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“It follows that most images found in taxonomic literature can be re-used for research or many other purposes without seeking permission, regardless of any copyright declaration,” says Prof. David J. Patterson, affiliated with both Plazi and the University of Sydney.

Nonetheless, the authors point out that, “in observance of ethical and scholarly standards, re-users are expected to cite the author and original source of any image that they use.” Such practice is “demanded by the conventions of scholarship, not by legal obligation,” they add.

However, the authors underline that there are actual copyrightable visuals, which might also make their way to a scientific paper. These include wildlife photographs, drawings and artwork produced in a distinctive individual form and intended for other than comparative purposes, as well as collections of images, qualifiable as databases in the sense of the European Protection of Databases directive.

In their paper, the scientists also provide an updated version of the Blue List, originally compiled in 2014 and comprising the copyright exemptions applicable to taxonomic works. In their Extended Blue List, the authors expand the list to include five extra items relating specifically to images.

“Egloff, Agosti, et al. make the compelling argument that taxonomic images, as highly standardized ‘references for identification of known biodiversity,’ by necessity, lack sufficient creativity to qualify for copyright. Their contention that ‘parameters of lighting, optical and specimen orientation’ in biological imaging must be consistent for comparative purposes underscores the relevance of the merger doctrine for photographic works created specifically as scientific data,” comments on the publication Ms. Gail Clement, Head of Research Services at the Caltech Library.

“In these cases, the idea and expression are the same and the creator exercises no discretion in complying with an established convention. This paper is an important contribution to the literature on property interests in scientific research data – an essential framing question for legal interoperability of research data,” she adds.

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Original source:

Egloff W, Agosti D, Kishor P, Patterson D, Miller J (2017) Copyright and the Use of Images as Biodiversity Data. Research Ideas and Outcomes 3: e12502. https://doi.org/10.3897/rio.3.e12502

Additional information:

The present study is a research outcome of the European Union’s FP7-funded project EU BON, grant agreement No 308454.

Guidelines for scholarly publishing of biodiversity data from Pensoft and EU BON

While development and implementation of data publishing and sharing practices and tools have long been among the core activities of the academic publisher Pensoft, it is well-understood that as part of scholarly publishing, open data practices are also currently in transition, and hence, require a lot of collaborative and consistent efforts to establish.

Based on Pensoft’s experience, and elaborated and updated during the Framework Program 7 EU BON project, a new paper published in the EU BON dedicated collection in the open science journal Research Ideas and Outcomes (RIO), outlines policies and guidelines for scholarly publishing of biodiversity and biodiversity-related data. Newly accumulated knowledge from large-scale international efforts, such as FORCE11 (Future of Research Communication and e-Scholarship), CODATA (The Committee on Data for Science and Technology), RDA (Research Data Alliance) and others, is also included in the Guidelines.

The present paper discusses some general concepts, including a definition of datasets, incentives to publish data and licences for data publishing. Furthermore, it defines and compares several routes for data publishing, namely: providing supplementary files to research articles; uploading them on specialised open data repositories, where they are linked to the research article; publishing standalone data papers; or making use of integrated narrative and data publishing through online import/download of data into/from manuscripts, such as the workflow provided by the Biodiversity Data Journal. Among the guidelines, there are also comprehensive instructions on preparation and peer review of data intended for publication.

Although currently available for journals using the developed by Pensoft journal publishing platform ARPHA, these strategies and guidelines could be of use for anyone interested in biodiversity data publishing.

Apart from paving the way for a whole new approach in data publishing, the present paper is also a fine example of science done in the open, having been published along with its two pre-submission public peer reviews. The reviews by Drs. Robert Mesibov and Florian Wetzel are both citable via their own Digital Object Identifiers (DOIs).

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Original source:

Penev L, Mietchen D, Chavan V, Hagedorn G, Smith V, Shotton D, Ó Tuama É, Senderov V, Georgiev T, Stoev P, Groom Q, Remsen D, Edmunds S (2017) Strategies and guidelines for scholarly publishing of biodiversity data. Research Ideas and Outcomes 3: e12431. https://doi.org/10.3897/rio.3.e12431

New collection in RIO Journal devoted to neuroscience projects from 2016 Brainhack events

A new collection devoted to neuroscience projects from 2016 Brainhack events has been launched in the open access journal Research Ideas and Outcomes (RIO). At current count, the “Brainhack 2016 Project Reports” collection features eight Project Reports, whose authors are applying open science and collaborative research to advance our understanding of the brain.

Seeking to provide a forum for open, collaborative projects in brain science the Brainhack organization has found a like-minded partner in the innovative open science journal RIO. The editor of the series is Dr. R. Cameron Craddock, Computational Neuroimaging Lab, Child Mind Institute and Nathan S. Kline Institute for Psychiatric Research, USA. He is joined by co-editors Dr. Pierre Bellec, Unité de neuroimagerie fonctionnelle, Centre de recherche de l’institut de gériatrie de Montréal, Canada, Dr. Daniel S. Margulies, Max Planck Research Group “Neuroanatomy & Connectivity“, Max Planck Institute for Human Cognitive and Brain Sciences, Dr. Nolan Nichols, Genetech, USA, and Dr. Jörg Pfannmöller, University of Greifswald, Germany.

The first project description published in the collection is a Software Management Plan presenting a comprehensive set of neuroscientific software packages demonstrating the huge potential of Gentoo Linux in neuroscience. The team of Horea-Ioan Ioanas, Dr. Bechara John Saab and Prof. Dr. Markus Rudin, affiliated with ETH and University of Zürich, Switzerland, make use of the flexibility of Gentoo’s environment to address many of the challenges in neuroscience software management, including system replicability, system documentation, data analysis reproducibility, fine-grained dependency management, easy control over compilation options, and seamless access to cutting-edge software release. The packages are available for the wide family of Gentoo distributions and derivatives. “Via Gentoo-prefix, these neuroscientific software packages are, in fact, also accessible to users of many other operating systems,” explain the researchers.

While quantifying lesions in a robust manner is fundamental for studying the effects of neuroanatomical changes in the post-stroke brain while recovering, manual lesion segmentation has been found to be a challenging and often subjective process. This is where the Semi-automated Robust Quantification of Lesions (SRQL) Toolbox comes in. Developed at the University of Southern California, Los Angeles, it optimizes quantification of lesions across research sites. “Specifically, this toolbox improves the performance of statistical analysis on lesions through standardizing lesion masks with white matter adjustment, reporting descriptive lesion statistics, and normalizing adjusted lesion masks to standard space,” explain scientists Kaori L. Ito, Julia M. Anglin, and Dr. Sook-Lei Liew.

Called Mindcontrol, an open-source web-based dashboard application lets users collaboratively quality control and curate neuroimaging data. Developed by the team of Anisha Keshavan and Esha Datta, both of University of California, San Francisco, Dr. Christopher R. Madan, Boston College, and Dr. Ian M. McDonough, The University of Alabama, Mindcontrol provides an easy-to-use interface, and allows the users to annotate points and curves on the volume, edit voxels, and assign tasks to other users. “We hope to build an active open-source community around Mindcontrol to add new features to the platform and make brain quality control more efficient and collaborative,” note the researchers.

At University of California, San Francisco, Anisha Keshavan, Dr. Arno Klein, and Dr. Ben Cipollini, created the open-source Mindboggle package, which serves to improve the labeling and morphometry estimates of brain imaging data. Using inspirations and feedback from a Brainhack hackathon, they built-up on Mindboggle to develop a web-based, interactive, brain shape 3D visualization of its outputs. Now, they are looking to expand the visualization, so that it covers other data besides shape information and enables the visual evaluation of thousands of brains.

Processing neuroimaging data on the cortical surface traditionally requires dedicated heavy-weight software suites. However, a team from Max Planck Institute for Human Cognitive and Brain Sciences, Free University Berlin, and the NeuroSpin Research Institute, France, have come up with an alternative. Operating within the neuroimaging data processing toolbox Nilearn, their Python package allows loading and plotting functions for different surface data formats with minimal dependencies, along with examples of their application. “The functions are easy to use, flexibly adapt to different use cases,” explain authors Julia M. Huntenburg, Alexandre Abraham, Joao Loula, Dr. Franziskus Liem, and Dr. Gaël Varoquaux. “While multiple features remain to be added and improved, this work presents a first step towards the support of cortical surface data in Nilearn.”

To further address the increasing necessity for tools specialised to process huge high-resolution brain imaging data in their anatomical detail, Julia M. Huntenburg gathers a separate team to work on another Python-based software. Being a user-friendly standalone package, this subset of CBSTools requires no additional installations, and allows for interactive data exploration at each processing stage.

Developed at the University of California, San Francisco, Cluster-viz is a web application that provides a platform for cluster-based interactive quality control of tractography algorithm outputs, explain the team of Kesshi M. Jordan, Anisha Keshavan, Dr. Maria Luisa Mandelli, and Dr. Roland G. Henry. It.

A project from the University of Warwick, United Kingdom, aims to extend the functionalities of the FSL neuroimaging software package in order to generate and report peak and cluster tables for voxel-wise inference. Dr. Camille Maumet and Prof. Thomas E. Nichols believe that the resulting extension “will be useful in the development of standardized exports of task-based fMRI results.”

More 2016 Brainhack projects are to be added to the collection.