APE in the wild: automated exploration of proteomics workflows in the bio.tools registry

The bio.tools registry is a main catalogue of computational tools in the life sciences. More than 17 000 tools have been registered by the international bioinformatics community. The bio.tools metadata schema includes semantic annotations of tool functions, that is, formal descriptions of tools' data types, formats, and operations with terms from the EDAM bioinformatics ontology. Such annotations enable the automated composition of tools into multistep pipelines or workflows. In this Technical Note, we revisit a previous case study on the automated composition of proteomics workflows. We use the same four workflow scenarios but instead of using a small set of tools with carefully handcrafted annotations, we explore workflows directly on bio.tools. We use the Automated Pipeline Explorer (APE), a reimplementation and extension of the workflow composition method previously used. Moving "into the wild" opens up an unprecedented wealth of tools and a huge number of alternative workflows. Automated composition tools can be used to explore this space of possibilities systematically. Inevitably, the mixed quality of semantic annotations in bio.tools leads to unintended or erroneous tool combinations. However, our results also show that additional control mechanisms (tool filters, configuration options, and workflow constraints) can effectively guide the exploration toward smaller sets of more meaningful workflows.Proteomic

Quantum Langevin theory of excess noise

In an earlier work [P. J. Bardroff and S. Stenholm], we have derived a fully quantum mechanical description of excess noise in strongly damped lasers. This theory is used here to derive the corresponding quantum Langevin equations. Taking the semi-classical limit of these we are able to regain the starting point of Siegman's treatment of excess noise [Phys. Rev. A 39, 1253 (1989)]. Our results essentially constitute a quantum derivation of his theory and allow some generalizations.Comment: 9 pages, 0 figures, revte

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Workflow Discovery with Semantic Constraints: The SAT-Based Implementation of APE

Science today is increasingly computational, and many researchers regularly face the need of creating purpose-specific computational pipelines for their specific data analysis problems. The manual composition and implementation of such workflows regularly costs valuable research time. Hence, many scientists wish for a system that would only require an abstract description of their intended data analysis process, and from there automatically compose and implement suitable workflows. In this paper we describe APE (the Automated Pipeline Explorer), a new implementation of a synthesis-based workflow discovery framework that aims to accomplish such automated composition. The framework captures the required technical domain knowledge in the form of tool and type taxonomies and functional tool annotations. Based on this semantic domain model, the framework allows users to specify their intents about workflows at an abstract, conceptual level in the form of natural-language templates. Internally, APE maps them to a temporal logic and translates them into a propositional logic instance of the problem that can be solved by an off-the-shelf SAT solver. From the solutions provided by the solver, APE then constructs executable workflow implementations. First applications of APE on realistic scientific workflow scenarios have shown that it is able to efficiently synthesize meaningful workflows. We use an example from the geospatial application domain as a running example in this paper

Computing Camps for Girls : A First-Time Experience at the University of Limerick

Increasing the number of females in ICT-related university courses has been a major concern for several years. In 2015, we offered a girls-only computing summer camp for the first time, as a new component in our education and outreach activities to foster students’ interest in our discipline. In this paper, we describe the motivation for the camp and how we designed the program, and we report our experiences and survey findings from the first two editions of the camp. They can provide guidance for planning further events targeting females, and help to integrate awareness about underrepresentation of females in other activities

Computing Camps for Girls : A First-Time Experience at the University of Limerick

Increasing the number of females in ICT-related university courses has been a major concern for several years. In 2015, we offered a girls-only computing summer camp for the first time, as a new component in our education and outreach activities to foster students’ interest in our discipline. In this paper, we describe the motivation for the camp and how we designed the program, and we report our experiences and survey findings from the first two editions of the camp. They can provide guidance for planning further events targeting females, and help to integrate awareness about underrepresentation of females in other activities

Computing Camps for Girls : A First-Time Experience at the University of Limerick

Increasing the number of females in ICT-related university courses has been a major concern for several years. In 2015, we offered a girls-only computing summer camp for the first time, as a new component in our education and outreach activities to foster students’ interest in our discipline. In this paper, we describe the motivation for the camp and how we designed the program, and we report our experiences and survey findings from the first two editions of the camp. They can provide guidance for planning further events targeting females, and help to integrate awareness about underrepresentation of females in other activities

Ontology of core concept data types for answering geo-analytical questions

In Geographic Information Systems (GIS), analysts answer questions by designing workflows that transform a certain type of data into a certain type of goal. Semantic data types help constrain the application of computational methods to those that are meaningful for such a goal. This prevents pointless computations and helps analysts design effective workflows. Yet, to date it remains unclear which types would be needed in order to ease geo-analytical tasks. The data types and formats used in GIS still allow for huge amounts of syntactically possible but nonsensical method applications. Core concepts of spatial information and related geo-semantic distinctions have been proposed as abstractions to help analysts formulate analytic questions and to compute appropriate answers over geodata of different formats. In essence, core concepts reflect particular interpretations of data which imply that certain transformations are possible. However, core concepts usually remain implicit when operating on geodata, since a concept can be represented in a variety of forms. A central question therefore is: Which semantic types would be needed to capture this variety and its implications for geospatial analysis? In this article, we propose an ontology design pattern of core concept data types that help answer geo-analytical questions. Based on a scenario to compute a liveability atlas for Amsterdam, we show that diverse kinds of geo-analytical questions can be answered by this pattern in terms of valid, automatically constructible GIS workflows using standard sources

Loose programming of GIS workflows with geo-analytical concepts

Loose programming enables analysts to program with concepts instead of procedural code. Data transformations are left underspecified, leaving away procedural details and exploiting knowledge about the applicability of functions to data types. To synthesize workflows of high quality for a geo-analytical task, the semantic type system needs to reflect knowledge of Geographic Information Systems (GIS) on a level that is deep enough to capture geo-analytical concepts and intentions, yet shallow enough to generalize over GIS implementations. Recently, core concepts of spatial information and related geo-analytical concepts were proposed as a way to add the required abstraction level to current geodata models. The core concept data types (CCD) ontology is a semantic type system that can be used to constrain GIS functions for workflow synthesis. However, to date, it is unknown what gain in precision and workflow quality can be expected. In this article, we synthesize workflows by annotating GIS tools with these types, specifying a range of common analytical tasks taken from an urban livability scenario. We measure the quality of automatically synthesized workflows against a benchmark generated from common data types. Results show that CCD concepts significantly improve the precision of workflow synthesis