Paul T. Groth

The FAIR Guiding Principles for scientific data management and stewardship

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.

The provenance of electronic data

It would include details of the processes that produced electronic data as far back as the beginning of time or at least the epoch of provenance awareness.

The anatomy of a nanopublication

As the amount of scholarly communication increases, it is increasingly difficult for specific core scientific statements to be found, connected and curated. Additionally, the redundancy of these statements in multiple fora makes it difficult to determine attribution, quality and provenance. To tackle these challenges, the Concept Web Alliance has promoted the notion of nanopublications (core scientific statements with associated context). In this document, we present a model of nanopublications along with a Named Graph/RDF serialization of the model. Importantly, the serialization is defined completely using already existing community-developed technologies. Finally, we discuss the importance of aggregating nanopublications and the role that the Concept Wiki plays in facilitating it.

Wings: Intelligent Workflow-Based Design of Computational Experiments

Describes the Wings intelligent workflow system that assists scientists with designing computational experiments by automatically tracking constraints and ruling out invalid designs, letting scientists focus on their experiments and goals.

The value of data

Data citation and the derivation of semantic constructs directly from datasets have now both found their place in scientific communication. The social challenge facing us is to maintain the value of traditional narrative publications and their relationship to the datasets they report upon while at the same time developing appropriate metrics for citation of data and data constructs.

The Semantic Web - ISWC 2013

As collaborative, or network science spreads into more science, engineering and medical fields, both the participants and their funders have expressed a very strong desire for highly functional data and information capabilities that are a) easy to use, b) integrated in a variety of ways, c) leverage prior investments and keep pace with rapid technical change, and d) are not expensive or timeconsuming to build or maintain. In response, and based on our accummulated experience over the last decade and a maturing of several key semantic web approaches, we have adapted, extended, and integrated several open source applications and frameworks that handle major portions of functionality for these platforms. At minimum, these functions include: an object-type repository, collaboration tools, an ability to identify and manage all key entities in the platform, and an integrated portal to manage diverse content and applications, with varied access levels and privacy options. At the same time, there is increasing attention to how researchers present and explain results based on interpretation of increasingly diverse and heterogeneous data and information sources. With the renewed emphasis on good data practices, informatics practitioners have responded to this challenge with maturing informatics-based approaches. These approaches include, but are not limited to, use case development; information modeling and architectures; elaborating vocabularies; mediating interfaces to data and related services on the Web; and traceable provenance. The current era of data-intensive research presents numerous challenges to both individuals and research teams. In environmental science especially, sub-fields that were data-poor are becoming data-rich (volume, type and mode), while some that were largely model/ simulation driven are now dramatically shifting to data-driven or least to data-model assimilation approaches. These paradigm shifts make it very hard for researchers used to one mode to shift to another, let alone produce products of their work that are usable or understandable by non-specialists. However, it is exactly at these frontiers where much of the exciting environmental science needs to be performed and appreciated.

The Requirements of Using Provenance in e-Science Experiments

In e-Science experiments, it is vital to record the experimental process for later use such as in interpreting results, verifying that the correct process took place or tracing where data came from. The process that led to some data is called the provenance of that data, and a provenance architecture is the software architecture for a system that will provide the necessary functionality to record, store and use process documentation to determine the provenance of data items. However, there has been little principled analysis of what is actually required of a provenance architecture, so it is impossible to determine the functionality they would ideally support. In this paper, we present use cases for a provenance architecture from current experiments in biology, chemistry, physics and computer science, and analyse the use cases to determine the technical requirements of a generic, technology and application-independent architecture. We propose an architecture that meets these requirements, analyse its features compared with other approaches and evaluate a preliminary implementation by attempting to realise two of the use cases.

Strong Mobility and Fine-Grained Resource Control in NOMADS

NOMADS is a Java-based agent system that supports strong mobility (i.e., the ability to capture and transfer the full execution state of migrating agents) and safe agent execution (i.e., the ability to control resources consumed by agents, facilitating guarantees of quality of service while protecting against denial of service attacks). The NOMADS environment is composed of two parts: an agent execution environment called Oasis and a new Java-compatible Virtual Machine (VM) called Aroma. The combination of Oasis and the Aroma VM provides key enhancements over today’s Java agent environments.

A protocol for recording provenance in service-oriented grids

Both the scientific and business communities, which are beginning to rely on Grids as problem-solving mechanisms, have requirements in terms of provenance. The provenance of some data is the documentation of process that led to the data; its necessity is apparent in fields ranging from medicine to aerospace. To support provenance capture in Grids, we have developed an implementation-independent protocol for the recording of provenance. We describe the protocol in the context of a service-oriented architecture and formalise the entities involved using an abstract state machine or a three-dimensional state transition diagram. Using these techniques we sketch a liveness property for the system.

The Altmetrics Collection

What paper should I read next? Who should I talk to at a conference? Which research group should get this grant? Researchers and funders alike must make daily judgments on how to best spend their limited time and money–judgments that are becoming increasingly difficult as the volume of scholarly communication increases. Not only does the number of scholarly papers continue to grow, it is joined by new forms of communication from data publications to microblog posts. To deal with incoming information, scholars have always relied upon filters. At first these filters were manually compiled compendia and corpora of the literature. But by the mid-20th century, filters built on manual indexing began to break under the weight of booming postwar science production. Garfield [1] and others pioneered a solution: automated filters that leveraged scientists own impact judgments, aggregating citations as “pellets of peer recognition.” [2]. These citation-based filters have dramatically grown in importance and have become the tenet of how research impact is measured. But, like manual indexing 60 years ago, they may today be failing to keep up with the literature’s growing volume, velocity, and diversity [3]. Citations are heavily gamed [4]–[6] and are painfully slow to accumulate [7], and overlook increasingly important societal and clinical impacts [8]. Most importantly, they overlook new scholarly forms like datasets, software, and research blogs that fall outside of the scope of citable research objects. In sum, citations only reflect formal acknowledgment and thus they provide only a partial picture of the science system [9]. Scholars may discuss, annotate, recommend, refute, comment, read, and teach a new finding before it ever appears in the formal citation registry. We need new mechanisms to create a subtler, higher-resolution picture of the science system. The Quest for Better Filters The scientometrics community has not been blind to the limitations of citation measures, and has collectively proposed methods to gather evidence of broader impacts and provide more detail about the science system: tracking acknowledgements [10], patents [11], mentorships [12], news articles [8], usage in syllabuses [13], and many others, separately and in various combinations [14]. The emergence of the Web, a “nutrient-rich space for scholars” [15], has held particular promise for new filters and lenses on scholarly output. Webometrics researchers have uncovered evidence of informal impact by examining networks of hyperlinks and mentions on the broader Web [16]–[18]. An important strand of webometrics has also examined the properties of article download data [7], [19], [20]. The last several years, however, have presented a promising new approach to gathering fine-grained impact data: tracking large-scale activity around scholarly products in online tools and environments. These tools and environments include, among others: social media like Twitter and Facebook online reference managers like CiteULike, Zotero, and Mendeley collaborative encyclopedias like Wikipedia blogs, both scholarly and general-audience scholarly social networks, like ResearchGate or Academia.edu conference organization sites like Lanyrd.com Growing numbers of scholars are using these and similar tools to mediate their interaction with the literature. In doing so, they are leaving valuable tracks behind them–tracks with potential to show informal paths of influence with unprecedented speed and resolution. Many of these tools offer open APIs, supporting large-scale, automated mining of online activities and conversations around research objects [21]. Altmetrics [22], [23] is the study and use of scholarly impact measures based on activity in online tools and environments. The term has also been used to describe the metrics themselves–one could propose in plural a “set of new altmetrics.” Altmetrics is in most cases a subset of both scientometrics and webometrics; it is a subset of the latter in that it focuses more narrowly on scholarly influence as measured in online tools and environments, rather than on the Web more generally. Altmetrics may support finer-grained maps of science, broader and more equitable evaluations, and improvements to the peer-review system [24]. On the other hand, the use and development of altmetrics should be pursued with appropriate scientific caution. Altmetrics may face attempts at manipulation similar to what Google must deal with in web search ranking. Addressing such manipulation may, in-turn, impact the transparency of altmetrics. New and complex measures may distort our picture of the science system if not rigorously assessed and correctly understood. Finally, altmetrics may promote an evaluation system for scholarship that many argue has become overly focused on metrics.