Facilitating and Improving Environmental Research Data Repository Interoperability

Environmental research data repositories provide much needed services for data preservation and data dissemination to diverse communities with domain specific or programmatic data needs and standards. Due to independent development these repositories serve their communities well, but were developed with different technologies, data models and using different ontologies. Hence, the effectiveness and efficiency of these services can be vastly improved if repositories work together adhering to a shared community platform that focuses on the implementation of agreed upon standards and best practices for curation and dissemination of data. Such a community platform drives forward the convergence of technologies and practices that will advance cross-domain interoperability. It will also facilitate contributions from investigators through standardized and streamlined workflows and provide increased visibility for the role of data managers and the curation services provided by data repositories, beyond preservation infrastructure. Ten specific suggestions for such standardizations are outlined without any suggestions for priority or technical implementation. Although the recommendations are for repositories to implement, they have been chosen specifically with the data provider/data curator and synthesis scientist in mind

Diez reglas sencillas para una exitosa colaboración transdisciplinar

El presente artículo es la versión en castellano de la publicación: KNAPP, B.; BARDENET, R.; BERNABEU, M.O.; BORDAS, R.; BRUNA, M.; CALDERHEAD, B. ET AL. (2015) “Ten Simple Rules for a Successful Cross-Disciplinary Collaboration”. PLoS Comput Biol 11(4): e1004214, disponible en: https://doi.org/10.1371/journal.pcbi.1004214. La traducción, autorizada por la entidad editora, ha sido llevada a cabo por Ona Lorda Roure y Leila Adim, colaboradoras del Instituto de Investigación TransJus y supervisada por el Dr. Juli Ponce Solé, Director del TransJus. En la misma se han incluido algunas notas aclaratorias para el lector en español, así como bibliografía complementaria en español.[spa] En el auge de las colaboraciones interdisciplinarias entre los distintos campos científicos, la transdisciplinariedad se presenta como la clave para encontrar soluciones a una variedad de problemas globales. Este trabajo, situado en el marco de la biología informática, se centra en exponer una lista extensa de reglas y consejos útiles para lograr una exitosa sinergia entre los varios colaboradores de un proyecto transdisciplinar. Se trata, de hecho, de una guía que pretende dirigirse tanto a investigadores noveles como a aquellos investigadores consolidados que se adentran en un espacio transdisciplinar por primera vez. En particular, este trabajo expone los beneficios principales de establecer una colaboración transdisciplinar, así como los problemas que de ella puedan surgir.[cat] En l'auge de les col·laboracions interdisciplinàries entre els diferents camps científics, la transdisciplinarietat es presenta com la clau per trobar solucions a una varietat de problemes globals. Aquest treball, situat en el marc de la biologia informàtica, es centra en exposar una llista extensa de regles i consells útils per aconseguir una reeixida sinergia entre els varis col·laboradors d'un projecte transdisciplinar. Es tracta, de fet, d'una guia que pretén dirigir-se tant a recercadors novells com a aquells recercadors consolidats que s'endinsen en un espai transdisciplinar per primera vegada. En particular, aquest treball exposa els beneficis principals d'establir una col·laboració transdisciplinar, així com els problemes que d'ella puguin sorgir.[eng] At a time of increasing interdisciplinary collaboration between different scientific fields, cross-disciplinarity represents a key for finding solutions to a variety of global problems. This work, located within the framework of computer biology, focuses on exposing an extensive list of rules and useful tips to achieve a successful synergy among the various collaborators of a transdisciplinary project. It is, in fact, a guide aimed at addressing both first-time researchers and consolidated researchers who enter a transdisciplinary space for the first time. In particular, this work exposes the main benefits of establishing a cross-disciplinary collaboration, as well as the problems that may arise from it

Open is not enough

The solutions adopted by the high-energy physics community to foster reproducible research are examples of best practices that could be embraced more widely. This first experience suggests that reproducibility requires going beyond openness

Help with Data Management for the Novice and Experienced Alike

With the powerful analyses and resources they enable, digital humanities tools have captivated researchers from many different fields who want to use them to study science. Digital tools, as well as funding agencies, research communities, and academic administrators, require researchers to think carefully about how they conceptualize, manage, and store data, and about what they plan to do with that data once a given project is over. The difficulties of developing strategies to address these problems can prevent new researchers from sticking with digital tools and flummox even experienced researchers. To help overcome the data hurdle, we present five principles to help researchers, novice and experienced alike, conceptualize and plan for their data. We illustrate the use of those principles with two digital projects from the history of science, the Embryo Project and the Marine Biological Laboratory History Project, and their associated HPS Repository for data. The principles apply beyond the digital realm, so those who collect and manage data by more traditional means will also find them useful

Open is not enough

The solutions adopted by the high-energy physics community to foster reproducible research are examples of best practices that could be embraced more widely. This first experience suggests that reproducibility requires going beyond openness.Peer reviewe

The challenges of research data management in cardiovascular science: a DGK and DZHK position paper-executive summary

The sharing and documentation of cardiovascular research data are essential for efficient use and reuse of data, thereby aiding scientific transparency, accelerating the progress of cardiovascular research and healthcare, and contributing to the reproducibility of research results. However, challenges remain. This position paper, written on behalf of and approved by the German Cardiac Society and German Centre for Cardiovascular Research, summarizes our current understanding of the challenges in cardiovascular research data management (RDM). These challenges include lack of time, awareness, incentives, and funding for implementing effective RDM; lack of standardization in RDM processes; a need to better identify meaningful and actionable data among the increasing volume and complexity of data being acquired; and a lack of understanding of the legal aspects of data sharing. While several tools exist to increase the degree to which data are findable, accessible, interoperable, and reusable (FAIR), more work is needed to lower the threshold for effective RDM not just in cardiovascular research but in all biomedical research, with data sharing and reuse being factored in at every stage of the scientific process. A culture of open science with FAIR research data should be fostered through education and training of early-career and established research professionals. Ultimately, FAIR RDM requires permanent, long-term effort at all levels. If outcomes can be shown to be superior and to promote better (and better value) science, modern RDM will make a positive difference to cardiovascular science and practice. The full position paper is available in the supplementary materials

Jupyter notebooks as discovery mechanisms for open science: Citation practices in the astronomy community

Citing data and software is a means to give scholarly credit and to facilitate access to research objects. Citation principles encourage authors to provide full descriptions of objects, with stable links, in their papers. As Jupyter notebooks aggregate data, software, and other objects, they may facilitate or hinder citation, credit, and access to data and software. We report on a study of references to Jupyter notebooks in astronomy over a 5-year period (2014-2018). References increased rapidly, but fewer than half of the references led to Jupyter notebooks that could be located and opened. Jupyter notebooks appear better suited to supporting the research process than to providing access to research objects. We recommend that authors cite individual data and software objects, and that they stabilize any notebooks cited in publications. Publishers should increase the number of citations allowed in papers and employ descriptive metadata-rich citation styles that facilitate credit and discovery