74,092 research outputs found
Enabling FAIR research in Earth Science through research objects
Data-intensive science communities are progressively adopting FAIR practices that enhance the
visibility of scientific breakthroughs and enable reuse. At the core of this movement, research objects
contain and describe scientific information and resources in a way compliant with the FAIR principles
and sustain the development of key infrastructure and tools. This paper provides an account of
the challenges, experiences and solutions involved in the adoption of FAIR around research objects
over several Earth Science disciplines. During this journey, our work has been comprehensive, with
outcomes including: an extended research object model adapted to the needs of earth scientists;
the provisioning of digital object identifiers (DOI) to enable persistent identification and to give due
credit to authors; the generation of content-based, semantically rich, research object metadata through natural language processing, enhancing visibility and reuse through recommendation systems and
third-party search engines; and various types of checklists that provide a compact representation of
research object quality as a key enabler of scientific reuse. All these results have been integrated in
ROHub, a platform that provides research object management functionality to a wealth of applications
and interfaces across different scientific communities. To monitor and quantify the community uptake
of research objects, we have defined indicators and obtained measures via ROHub that are also
discussed herein.Published550-5645IT. Osservazioni satellitariJCR Journa
ASTRO Journals' Data Sharing Policy and Recommended Best Practices.
Transparency, openness, and reproducibility are important characteristics in scientific publishing. Although many researchers embrace these characteristics, data sharing has yet to become common practice. Nevertheless, data sharing is becoming an increasingly important topic among societies, publishers, researchers, patient advocates, and funders, especially as it pertains to data from clinical trials. In response, ASTRO developed a data policy and guide to best practices for authors submitting to its journals. ASTRO's data sharing policy is that authors should indicate, in data availability statements, if the data are being shared and if so, how the data may be accessed
Assigning Creative Commons Licenses to Research Metadata: Issues and Cases
This paper discusses the problem of lack of clear licensing and transparency
of usage terms and conditions for research metadata. Making research data
connected, discoverable and reusable are the key enablers of the new data
revolution in research. We discuss how the lack of transparency hinders
discovery of research data and make it disconnected from the publication and
other trusted research outcomes. In addition, we discuss the application of
Creative Commons licenses for research metadata, and provide some examples of
the applicability of this approach to internationally known data
infrastructures.Comment: 9 pages. Submitted to the 29th International Conference on Legal
Knowledge and Information Systems (JURIX 2016), Nice (France) 14-16 December
201
Building a Disciplinary, World-Wide Data Infrastructure
Sharing scientific data, with the objective of making it fully discoverable,
accessible, assessable, intelligible, usable, and interoperable, requires work
at the disciplinary level to define in particular how the data should be
formatted and described. Each discipline has its own organization and history
as a starting point, and this paper explores the way a range of disciplines,
namely materials science, crystallography, astronomy, earth sciences,
humanities and linguistics get organized at the international level to tackle
this question. In each case, the disciplinary culture with respect to data
sharing, science drivers, organization and lessons learnt are briefly
described, as well as the elements of the specific data infrastructure which
are or could be shared with others. Commonalities and differences are assessed.
Common key elements for success are identified: data sharing should be science
driven; defining the disciplinary part of the interdisciplinary standards is
mandatory but challenging; sharing of applications should accompany data
sharing. Incentives such as journal and funding agency requirements are also
similar. For all, it also appears that social aspects are more challenging than
technological ones. Governance is more diverse, and linked to the discipline
organization. CODATA, the RDA and the WDS can facilitate the establishment of
disciplinary interoperability frameworks. Being problem-driven is also a key
factor of success for building bridges to enable interdisciplinary research.Comment: Proceedings of the session "Building a disciplinary, world-wide data
infrastructure" of SciDataCon 2016, held in Denver, CO, USA, 12-14 September
2016, to be published in ICSU CODATA Data Science Journal in 201
SpaceNet MVOI: a Multi-View Overhead Imagery Dataset
Detection and segmentation of objects in overheard imagery is a challenging
task. The variable density, random orientation, small size, and
instance-to-instance heterogeneity of objects in overhead imagery calls for
approaches distinct from existing models designed for natural scene datasets.
Though new overhead imagery datasets are being developed, they almost
universally comprise a single view taken from directly overhead ("at nadir"),
failing to address a critical variable: look angle. By contrast, views vary in
real-world overhead imagery, particularly in dynamic scenarios such as natural
disasters where first looks are often over 40 degrees off-nadir. This
represents an important challenge to computer vision methods, as changing view
angle adds distortions, alters resolution, and changes lighting. At present,
the impact of these perturbations for algorithmic detection and segmentation of
objects is untested. To address this problem, we present an open source
Multi-View Overhead Imagery dataset, termed SpaceNet MVOI, with 27 unique looks
from a broad range of viewing angles (-32.5 degrees to 54.0 degrees). Each of
these images cover the same 665 square km geographic extent and are annotated
with 126,747 building footprint labels, enabling direct assessment of the
impact of viewpoint perturbation on model performance. We benchmark multiple
leading segmentation and object detection models on: (1) building detection,
(2) generalization to unseen viewing angles and resolutions, and (3)
sensitivity of building footprint extraction to changes in resolution. We find
that state of the art segmentation and object detection models struggle to
identify buildings in off-nadir imagery and generalize poorly to unseen views,
presenting an important benchmark to explore the broadly relevant challenge of
detecting small, heterogeneous target objects in visually dynamic contexts.Comment: Accepted into IEEE International Conference on Computer Vision (ICCV)
201
Data Driven Discovery in Astrophysics
We review some aspects of the current state of data-intensive astronomy, its
methods, and some outstanding data analysis challenges. Astronomy is at the
forefront of "big data" science, with exponentially growing data volumes and
data rates, and an ever-increasing complexity, now entering the Petascale
regime. Telescopes and observatories from both ground and space, covering a
full range of wavelengths, feed the data via processing pipelines into
dedicated archives, where they can be accessed for scientific analysis. Most of
the large archives are connected through the Virtual Observatory framework,
that provides interoperability standards and services, and effectively
constitutes a global data grid of astronomy. Making discoveries in this
overabundance of data requires applications of novel, machine learning tools.
We describe some of the recent examples of such applications.Comment: Keynote talk in the proceedings of ESA-ESRIN Conference: Big Data
from Space 2014, Frascati, Italy, November 12-14, 2014, 8 pages, 2 figure
Geoscience data publication: practices and perspectives on enabling the FAIR guiding principles
© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Kinkade, D., & Shepherd, A. Geoscience data publication: practices and perspectives on enabling the FAIR guiding principles. Geoscience Data Journal, (2021): https://doi.org/10.1002/gdj3.120.ntroduced in 2016, the FAIR Guiding Principles endeavour to significantly improve the process of today's data-driven research. The Principles present a concise set of fundamental concepts that can facilitate the findability, accessibility, interoperability and reuse (FAIR) of digital research objects by both machines and human beings. The emergence of FAIR has initiated a flurry of activity within the broader data publication community, yet the principles are still not fully understood by many community stakeholders. This has led to challenges such as misinterpretation and co-opted use, along with persistent gaps in current data publication culture, practices and infrastructure that need to be addressed to achieve a FAIR data end-state. This paper presents an overview of the practices and perspectives related to the FAIR Principles within the Geosciences and offers discussion on the value of the principles in the larger context of what they are trying to achieve. The authors of this article recommend using the principles as a tool to bring awareness to the types of actions that can improve the practice of data publication to meet the needs of all data consumers. FAIR Guiding Principles should be interpreted as an aspirational guide to focus behaviours that lead towards a more FAIR data environment. The intentional discussions and incremental changes that bring us closer to these aspirations provide the best value to our community as we build the capacity that will support and facilitate new discovery of earth systems.The writing of this article was supported by the NSF, grant no. 1924618
Recommended from our members
Conversations on a probable future: interview with Beatrice Fazi
No description supplie
- …