There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

Cite as:Reuben Binns, Max Van Kleek, Michael Veale, Ulrik Lyngs, Jun Zhao and Nigel Shadbolt (2018) 'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions. ACM Conference on Human Factors in Computing Systems (CHI'18), April 21–26, Montreal, Canada. doi: 10.1145/3173574.3173951Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to 'meaningful information about the logic' behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles---under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best' approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.

SUMMARYThe first Provenance Challenge was set up in order to provide a forum for the community to understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a functional magnetic resonance imaging workflow was defined, which participants had to either simulate or run in order to produce some provenance representation, from which a set of identified queries had to be implemented and executed. Sixteen teams responded to the challenge, and submitted their inputs. In this paper, we present the challenge workflow and queries, and summarize the participants' contributions.

No abstract

This paper studies the routing, modulation format, and spectrum allocation problem in elastic fiberoptical networks for static traffic. Elastic networks, based on Nyquist wavelength division multiplexing or optical orthogonal frequency division multiplexing, can efficiently utilize the optical fiber's bandwidth in an elastic manner by partitioning the bandwidth into hundreds or even thousands of subcarriers. Besides the amplified spontaneous emission noise, the nonlinear impairments of each connection is explicitly considered by utilizing an analytical model to calculate the nonlinear interference from other connections propagating in the same fibers. The objective of our work is to minimize the bandwidth, i.e., the number of used subcarriers, across the network, while satisfying demands on throughput and quality for all connections. A novel integer linear program formulation and low-complexity heuristics are proposed. Simulation results are presented to demonstrate the effectiveness of the proposed approaches. Compared with transmission reach-based benchmark methods, our methods can achieve up to 31% bandwidth reduction.Index Terms-NWDM, OOFDM, elastic optical networks, routing and spectrum assignment, physical impairments.

Abstract. e-Science experiments are those performed using computerbased resources such as database searches, simulations or other applications. Like their laboratory based counterparts, the data associated with an e-Science experiment are of reduced value if other scientists are not able to identify the origin, or provenance, of those data. Provenance is the term given to metadata about experiment processes, the derivation paths of data, and the sources and quality of experimental components, which includes the scientists themselves, related literature, etc. Consequently provenance metadata are valuable resources for e-Scientists to repeat experiments, track versions of data and experiment runs, verify experiment results, and as a source of experimental insight. One specific kind of in silico experiment is a workflow. In this paper we describe how we can assemble a Semantic Web of workflow provenance logs that allows a bioinformatician to browse and navigate between experimental components by generating hyperlinks based on semantic annotations associated with them. By associating well-formalized semantics with workflow logs we take a step towards integration of process provenance information and improved knowledge discovery.

a b s t r a c tScientific workflows are a popular mechanism for specifying and automating data-driven in silico experiments. A significant aspect of their value lies in their potential to be reused. Once shared, workflows become useful building blocks that can be combined or modified for developing new experiments. However, previous studies have shown that storing workflow specifications alone is not sufficient to ensure that they can be successfully reused, without being able to understand what the workflows aim to achieve or to re-enact them. To gain an understanding of the workflow, and how it may be used and repurposed for their needs, scientists require access to additional resources such as annotations describing the workflow, datasets used and produced by the workflow, and provenance traces recording workflow executions.In this article, we present a novel approach to the preservation of scientific workflows through the application of research objects-aggregations of data and metadata that enrich the workflow specifications. Our approach is realised as a suite of ontologies that support the creation of workflow-centric research objects. Their design was guided by requirements elicited from previous empirical analyses of workflow decay and repair. The ontologies developed make use of and extend existing well known ontologies, namely the Object Reuse and Exchange (ORE) vocabulary, the Annotation Ontology (AO) and the W3C PROV ontology (PROVO). We illustrate the application of the ontologies for building Workflow Research Objects with a case-study that investigates Huntington's disease, performed in collaboration with a team from the Leiden University Medial Centre (HG-LUMC). Finally we present a number of tools developed for creating and managing workflow-centric research objects.

SUMMARYTaverna is a workflow workbench developed as part of the UK's my Grid project. Taverna's provenance model captures both internal provenance locally generated in Taverna and external provenance gathered from third-party data providers. This model also supports overlaying secondary provenance over the primary logs and lineage. This design is motivated by the particular properties of bioinformatics data and services used in Taverna. A Semantic Web of provenance, Ouzo, is built to combine the above different provenance by means of semantic annotations. This paper shows how Ouzo can be mined by a provenance usage component, Provenance Query and Answer (ProQA). ProQA supports provenance retrievals as well as provenance abstraction, aggregation, and semantic reasoning. ProQA is implemented as a suite APIs which can be deployed as provenance services to compose system provenance workflows that analyse experiment results using the provenance records. We show how these features of Taverna's provenance support us in answering the questions from the provenance challenge workshop and a set of additional provenance queries.