Common Application Programming Interface Research Articles

A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research

Spectral Indices derived from multispectral remote sensing products are extensively used to monitor Earth system dynamics (e.g. vegetation dynamics, water bodies, fire regimes). The rapid increase of proposed spectral indices led to a high demand for catalogues of spectral indices and tools for their computation. However, most of these resources are either closed-source, outdated, unconnected to a catalogue or lacking a common Application Programming Interface (API). Here we present “Awesome Spectral Indices” (ASI), a standardized catalogue of spectral indices for Earth system research. ASI provides a comprehensive machine readable catalogue of spectral indices, which is linked to a Python library. ASI delivers a broad set of attributes for each spectral index, including names, formulas, and source references. The catalogue can be extended by the user community, ensuring that ASI remains current and enabling a wider range of scientific applications. Furthermore, the Python library enables the application of the catalogue to real-world data and thereby facilitates the efficient use of remote sensing resources in multiple Earth system domains.

Seven years since the launch of the Matchmaker Exchange: The evolution of genomic matchmaking.

The Matchmaker Exchange (MME) was launched in 2015 to provide a robust mechanism to discover novel disease-gene relationships. It operates as a federated network connecting databases holding relevant data using a common application programming interface, where two or more users are looking for a match for the same gene (two-sided matchmaking). Seven years from its launch, it is clear that the MME is making outstanding contributions to understanding the morbid anatomy of the genome. The number of unique genes present across the MME has steadily increased over time; there are currently >13,520 unique genes (~68% of all protein-coding genes) connected across the MME's eight genomic matchmaking nodes, GeneMatcher, DECIPHER, PhenomeCentral, MyGene2, seqr, Initiative on Rare and Undiagnosed Disease, PatientMatcher, and the RD-Connect Genome-Phenome Analysis Platform. The collective data set accessible across the MME currently includes more than 120,000 cases from over 12,000 contributors in 98 countries. The discovery of potential new disease-gene relationships is happening daily and international collaborative teams are moving these advances forward to publication, now numbering well over 500. Expansion of data sharing into routine clinical practice by clinicians, genetic counselors, and clinical laboratories has ensured access to discovery for even more individuals with undiagnosed rare genetic diseases. Tens of thousands of patients and their family members have been directly or indirectly impacted by the discoveries facilitated by two-sided genomic matchmaking. MME supports further connections to the literature (PubCaseFinder) and to human and model organism resources (Monarch Initiative) and scientists (ModelMatcher). Efforts are now underway to explore additional approaches to matchmaking at the gene or variant level where there is only one querier (one-sided matchmaking). Genomic matchmaking has proven its utility over the past 7 years and will continue to facilitate discoveries in the years to come.

DJMol: An open-source modeling platform for computational chemistry and materials science with a Python interpreter.

We present a modular and extendable software suite, DJMol, for performing molecular simulations and it is demonstrated with DFTB+, Siesta, Atomic Simulation Environment, and OpenMD codes. It supports many of the standard features of an integrated development environment and consists of a structure builder and viewer, which could be connected with these electronic structure codes along with a set of data analyzers. This program comprises Java and Python modules and its libraries to carry out a different set of modeling tasks in materials science and chemistry. By adopting a Python interpreter into the software, a range of scriptable Python codes, such as Pymatgen can be incorporated into this programmable modeling platform. DJMol, through its common application programming interface (API), supports multiple modeling codes in the backend and several post-processing tools. It benefits an experienced user by increasing efficiency, while a nonexpert user by easy to use API.

Enabling FAIR Discovery of Rare Disease Digital Resources.

Integration of heterogenous resources is key for Rare Disease research. Within the EJP RD, common Application Programming Interface specifications are proposed for discovery of resources and data records. This is not sufficient for automated processing between RD resources and meeting the FAIR principles. To design a solution to improve FAIR for machines for the EJP RD API specification. A FAIR Data Point is used to expose machine-actionable metadata of digital resources and it is configured to store its content to a semantic database to be FAIR at the source. A solution was designed based on grlc server as middleware to implement the EJP RD API specification on top of the FDP. grlc reduces potential API implementation overhead faced by maintainers who use FAIR at the source.

Model-based cloud resource management with TOSCA and OCCI

With the advent of cloud computing, different cloud providers with heterogeneous cloud services (compute, storage, network, applications, etc.) and their related Application Programming Interfaces (APIs) have emerged. This heterogeneity complicates the implementation of an interoperable cloud system. Several standards have been proposed to address this challenge and provide a unified interface to cloud resources. The Open Cloud Computing Interface (OCCI) thereby focuses on the standardization of a common API for Infrastructure-as-a-Service (IaaS) providers, while the Topology and Orchestration Specification for Cloud Applications (TOSCA) focuses on the standardization of a template language to enable the proper definition of the topology of cloud applications and their orchestrations on top of a cloud system. TOSCA thereby does not define how the application topologies are created on the cloud. Therefore, we analyze the conceptual similarities between the two approaches and we study how we can integrate them to obtain a complete standard-based approach to manage both Cloud Infrastructure and Cloud application layers. We propose an automated extensive mapping between the concepts of the two standards, and we provide TOSCA Studio, a model-driven tool chain for TOSCA that conforms to OCCI. TOSCA Studio allows to graphically design cloud applications as well as to deploy and manage them at runtime using a fully model-driven cloud orchestrator based on the two standards. Our contribution is validated by successfully transforming and deploying three cloud applications: WordPress, Node Cellar and Multi-Tier.

The NCI Genomic Data Commons

The National Cancer Institute (NCI) Genomic Data Commons (GDC) contains more than 2.9 petabytes of genomic and associated clinical data from more than 60 NCI-funded and other contributed cancer genomics research projects. The GDC consists of five applications over a common data model and a common application programming interface.

High-accuracy and video-rate lifetime extraction from time correlated single photon counting data on a graphical processing unit

Graphical Processing Units (GPUs) are a powerful alternative to central processing units, especially for data-parallel, video-rate processing of large data volumes. In the complex scenario of high-performance, multichannel Time Correlated Single Photon Counting (TCSPC), a huge amount of data is potentially generated by the acquisition system. Exploiting a dedicated, external, programmable elaboration unit enables a high degree of flexibility to perform different types of analysis. In this paper, we present a GPU-based application that leverages the common unified device architecture application programming interface for video-rate and accurate lifetime extraction from TCSPC data acquired at a rate of up to 10 Gbit/s.

Deep Relations in Nordic and Baltic Biodiversity e-Infrastructures (DeepDive)

The vision of the DeepDive program (https://neic.no/deepdive) is to establish a regional infrastructure network consisting of Nordic and Baltic data centers and information systems and to provide seamlessly operating regional data services, tools, and virtual laboratories. The program is funded by the Nordic e-Infrastructure Collaboration (https://neic.no) and was launched in 2017. Here we present some of the results and outcomes from the technical collaborations in the network. We will show examples of integration of biodiversity data services and portals though common Application Programming Interfaces (APIs) and Graphical User Interfaces (GUIs), describe our program to foster a biodiversity informatics community in the region, and explain advances in system interoperability that have been achieved over the past three years. We will also highlight the technical plans for further development and long-term sustainability of our Nordic and Baltic e-infrastructure network and make suggestions for further linkage to international information systems and ESFRI infrastructures.

Semantically Linking Specimens and Images

Many of the world’s natural history collections are creating high resolution digital images of their specimens. They often make these available on the web through some form or zoomable viewer. For historical reasons, a hotchpotch of technologies are used to achieve this. This diversity has lead to two issues. Firstly, maintenance becomes costly as technologies need replacing. Secondly there is little chance to share data between institutions or provide a unified user experience. A researcher visiting four different virtual collections may have four very different experiences. Similar issues exist in the archives and libraries disciplines. They also need to share high resolution, annotated images of the physical objects in their care. In response to this issue many have coalesced around the International Image Interoperability Framework (IIIF). IIIF is a set of shared application programming interface (API) specifications for interoperable functionality in digital image repositories. It separates the notion of a viewer, which may be used as part of a website or other application, and the web services that feed data to that viewer. By using a common API for serving data about images, different viewers can be used to view the same images, thus providing an upgrade path that does not require replacement of viewer and server software at the same time and allows different viewers to be used for the same image data. Potentially more importantly, it facilitates the construction of applications that view data from different collections as if they were in the same place. From the researcher’s point of view, the experience could be the same whether the virtual specimen is hosted locally or in a museum on another continent. There is one important thing that has been deliberately omitted from the IIIF standard. This has both enabled its rapid adoption but also makes it incomplete for building research applications. IIIF transmits no semantic data about the subject of the images, only labels. The IIIF data therefore needs to be bound to semantically rich data about the specimens being viewed, in some uniform way. Consortium of Taxonomic Facilities (CETAF ) specimen identifiers are now widely adopted by natural history collections in Europe. Each individual collection object is designated by a URI chosen and maintained by the institution owning the specimen (Groom et al. 2017, Güntsch et al. 2018, Güntsch et al. 2017, HYAM et al. 2012). Under Linked Data conventions, content negotiation is used at the server so that users accessing an object using a web-browser are redirected to a human-readable representation of the object, typically a web-page, whilst software systems requiring machine-processable representations are redirected to an RDF-encoded metadata record. CETAF specimen identifiers are therefore ideal partners for IIIF representations of specimens. But how should we join the two together in a semantically rich way that will be generally understandable? SYNTHESYS+ is a European Commission funded programme that facilitates collaboration and network building among European natural history collections. It is concerned with both physical and virtual access to the 390 million specimens of plants and animals housed in participating institutions. Under Task 4.3 of this project, we have been working to create a reliable way to link between the RDF metadata about specimens and images of those specimens in IIIF as well as from images of specimens back to metadata of those specimens. By January 2021, we aim to have ten exemplar institutions publishing IIIF manifest files linked to CETAF identifiers for a few million specimens and for this to act as a catalyst for wider adoption in the natural history community. This presentation gives an update on the rollout of these implementations, paying particular attention to the challenges of semantically annotating specimens with images.

The basic matrix library (BML) for quantum chemistry

The basic matrix library package (BML) provides a common application programming interface (API) for linear algebra and matrix functions in C and Fortran for quantum chemistry codes. The BML API is matrix format independent. Currently the dense, compressed sparse row, and ELLPACK-R sparse matrix data types are available, each with different implementations. We show how the second-order spectral projection (SP2) algorithm used to compute the electronic structure of a molecular system represented with a tight-binding Hamiltonian can be successfully implemented with the aid of this library.

Matchmaker Exchange.

In well over half of the individuals with rare disease who undergo clinical or research next-generation sequencing, the responsible gene cannot be determined. Some reasons for this relatively low yield include unappreciated phenotypic heterogeneity; locus heterogeneity; somatic and germline mosaicism; variants of uncertain functional significance; technically inaccessible areas of the genome; incorrect mode of inheritance investigated; and inadequate communication between clinicians and basic scientists with knowledge of particular genes, proteins, or biological systems. To facilitate such communication and improve the search for patients or model organisms with similar phenotypes and variants in specific candidate genes, we have developed the Matchmaker Exchange (MME). MME was created to establish a federated network connecting databases of genomic and phenotypic data using a common application programming interface (API). To date, seven databases can exchange data using the API (GeneMatcher, PhenomeCentral, DECIPHER, MyGene2, matchbox, Australian Genomics Health Alliance Patient Archive, and Monarch Initiative; the latter included for model organism matching). This article guides usage of the MME for rare disease gene discovery. © 2017 by John Wiley & Sons, Inc.

MzStudio: A Dynamic Digital Canvas for User-Driven Interrogation of Mass Spectrometry Data.

Although not yet truly ‘comprehensive’, modern mass spectrometry-based experiments can generate quantitative data for a meaningful fraction of the human proteome. Importantly for large-scale protein expression analysis, robust data pipelines are in place for identification of un-modified peptide sequences and aggregation of these data to protein-level quantification. However, interoperable software tools that enable scientists to computationally explore and document novel hypotheses for peptide sequence, modification status, or fragmentation behavior are not well-developed. Here, we introduce mzStudio, an open-source Python module built on our multiplierz project. This desktop application provides a highly-interactive graphical user interface (GUI) through which scientists can examine and annotate spectral features, re-search existing PSMs to test different modifications or new spectral matching algorithms, share results with colleagues, integrate other domain-specific software tools, and finally create publication-quality graphics. mzStudio leverages our common application programming interface (mzAPI) for access to native data files from multiple instrument platforms, including ion trap, quadrupole time-of-flight, Orbitrap, matrix-assisted laser desorption ionization, and triple quadrupole mass spectrometers and is compatible with several popular search engines including Mascot, Proteome Discoverer, X!Tandem, and Comet. The mzStudio toolkit enables researchers to create a digital provenance of data analytics and other evidence that support specific peptide sequence assignments.

Multiplierz v2.0: A Python-based ecosystem for shared access and analysis of native mass spectrometry data.

The continued evolution of modern mass spectrometry instrumentation and associated methods represents a critical component in efforts to decipher the molecular mechanisms which underlie normal physiology and understand how dysregulation of biological pathways contributes to human disease. The increasing scale of these experiments combined with the technological diversity of mass spectrometers presents several challenges for community-wide data access, analysis, and distribution. Here we detail a redesigned version of multiplierz, our Python software library which leverages our common application programming interface (mzAPI) for analysis and distribution of proteomic data. New features include support for a wider range of native mass spectrometry file types, interfaces to additional database search engines, compatibility with new reporting formats, and high-level tools to perform post-search proteomic analyses. A GUI desktop environment, mzDesktop, provides access to multiplierz functionality through a user friendly interface. multiplierz is available for download from: https://github.com/BlaisProteomics/multiplierz; and mzDesktop is available for download from: https://sourceforge.net/projects/multiplierz/.

Hierarchical Scanning Data Structure for Additive Manufacturing

In additive manufacturing, the digital information of required object model is transferred to additive manufacturing (AM) machine using a technology-independent de facto file format called STL. The approximation of the actual object surface employing STL file causes loss of geometrical and topological information and introduces error to the digital model. This may also limit the manufacturing repeatability between AM machine and processes. This research focuses on building a common data generation platform directly from the commonly used parametric surface model (B-rep). The generic data structure named as Hierarchical Scanning Data Structure (HSDS) is proposed in this research. HSDS will store the actual digital scanning information systematically and sequentially. A common application program interface (API) platform is also proposed in this research, which can access the HSDS and generate machine readable file for different existing AM control systems. The data stored in HSDS can be retrieved remotely and be used by different existing AM controller supporting the cloud/cyber manufacturing process and ensure the platform-independent object repeatability. The proposed framework is implemented with examples and results are compared with the existing system.

Attribute driven process architecture for additive manufacturing

In additive manufacturing (AM) process, the manufacturing attributes are highly dependent upon the execution of hierarchical plan. Among them, material deposition plan can frequently interrupt the AM process due to tool-path changes, tool start-stop and non-deposition time, which can be challenging during free-form part fabrication. In this paper, the layer geometries for both model and support structure are analyzed to identify the features that create change in deposition modality. First, the overhanging points on the part surface are identified using the normal vector direction of the model surface. A k-th nearest point algorithm is implemented to generate the 3d boundary support contour which is used to construct the support structure. Both model and support structures are sliced and contours are evaluated. The layer contour, plurality, concavity, number of contours, geometric shape, size and interior islands are considered to generate an AM deposition model. The proposed model is solved for minimizing the change in deposition modality by maximizing the continuity and connectivity in the material deposition plan. Both continuity and connectivity algorithms are implemented for model and support structure for free-form object. The proposed algorithm provides the optimum deposition direction that results in minimum number of tool-path segments and their connectivity while minimizing contour plurality effect. This information is stored as a generic digital file format named Part Attributable Motion (PAM). A common application program interface (API) platform is also proposed in this paper, which can access the PAM and generate machine readable file for different existing 3D printers. The proposed research is implemented on three free-form objects with complex geometry and parts are fabricated. Also, the build time is evaluated and the results are compared with the available 3d printing software. The layer geometries for AM are analyzed to minimize the change in deposition modality.Overhanging points are identified and k-th nearest point algorithm is implemented to construct the support structure.Continuity and connectivity algorithms are implemented for free-form shaped model and support structure.A generic digital file format named Part Attributable Motion (PAM) is proposed to store the outcome from the methodology.The build time is evaluated and the results are compared with the available 3d printing software.

From WSN towards WoT: Open API Scheme Based on oneM2M Platforms.

Conventional computing systems have been able to be integrated into daily objects and connected to each other due to advances in computing and network technologies, such as wireless sensor networks (WSNs), forming a global network infrastructure, called the Internet of Things (IoT). To support the interconnection and interoperability between heterogeneous IoT systems, the availability of standardized, open application programming interfaces (APIs) is one of the key features of common software platforms for IoT devices, gateways, and servers. In this paper, we present a standardized way of extending previously-existing WSNs towards IoT systems, building the world of the Web of Things (WoT). Based on the oneM2M software platforms developed in the previous project, we introduce a well-designed open API scheme and device-specific thing adaptation software (TAS) enabling WSN elements, such as a wireless sensor node, to be accessed in a standardized way on a global scale. Three pilot services are implemented (i.e., a WiFi-enabled smart flowerpot, voice-based control for ZigBee-connected home appliances, and WiFi-connected AR.Drone control) to demonstrate the practical usability of the open API scheme and TAS modules. Full details on the method of integrating WSN elements into three example systems are described at the programming code level, which is expected to help future researchers in integrating their WSN systems in IoT platforms, such as oneM2M. We hope that the flexibly-deployable, easily-reusable common open API scheme and TAS-based integration method working with the oneM2M platforms will help the conventional WSNs in diverse industries evolve into the emerging WoT solutions.

The Matchmaker Exchange: a platform for rare disease gene discovery.

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for "the needle in a haystack" to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can "match" these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.

Integrated platform and API for electrophysiological data.

Recent advancements in technology and methodology have led to growing amounts of increasingly complex neuroscience data recorded from various species, modalities, and levels of study. The rapid data growth has made efficient data access and flexible, machine-readable data annotation a crucial requisite for neuroscientists. Clear and consistent annotation and organization of data is not only an important ingredient for reproducibility of results and re-use of data, but also essential for collaborative research and data sharing. In particular, efficient data management and interoperability requires a unified approach that integrates data and metadata and provides a common way of accessing this information. In this paper we describe GNData, a data management platform for neurophysiological data. GNData provides a storage system based on a data representation that is suitable to organize data and metadata from any electrophysiological experiment, with a functionality exposed via a common application programming interface (API). Data representation and API structure are compatible with existing approaches for data and metadata representation in neurophysiology. The API implementation is based on the Representational State Transfer (REST) pattern, which enables data access integration in software applications and facilitates the development of tools that communicate with the service. Client libraries that interact with the API provide direct data access from computing environments like Matlab or Python, enabling integration of data management into the scientist's experimental or analysis routines.

An efficient truss structure optimization framework based on CAD/CAE integration and sequential radial basis function metamodel

In order to improve the performance and efficiency of truss structure optimization, this paper presents a general framework that embeds and seamlessly integrates commercial CAD and CAE software through common programming languages and application programming interface (API). Along with the automatic CAD/CAE integration, an adaptive metamodel-based optimization called sequential radial basis function (SRBF) is applied to truss structure optimization involving sizing, geometry and topology variables. SRBF distinguishingly features two-loops searching strategy, the "inner loop" and the "outer loop". The "inner loop" aims to search a feasible point through updating the factors of the augmented Lagrangian function. With the improved significant sampling space (ISSS) method, the "outer loop" sequentially generates new additional samples to update the RBF model. The continuous relaxation method is developed to deal with the mixed-discrete variables during the truss structure optimization. Applied to practical truss structure optimization problems from small scale to large scale, the proposed framework demonstrates feasibility of the CAD/CAE integration system during the structure modeling and analysis, and facilitates the truss structure optimization process. The comparison results between the SRBF and other approaches show that SRBF improves merit of searching global optimum and reduces the computation cost.

Neuroimaging Data Access and Query through a Common Application Programming Interface

Event Abstract Back to Event Neuroimaging Data Access and Query through a Common Application Programming Interface B. Nolan Nichols1*, Christian Haselgrove2, Jean-Baptiste Poline3 and Satrajit S. Ghosh4 1 Integrated Brain Imaging Center, University of Washington, Seattle, WA, United States 2 University of Massachusetts Medical School, Worcester, MA, United States 3 Neurospin-I2BM-CEA, Gif sur Yvette, France 4 McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States A large number of databases are developed to store and manage human neuroimaging data. While individual neuroscience databases provide mechanisms to query and download information within a given framework (e.g., Allen Institute, COINS, HID, IDA, LORIS, NIMS, XNAT), there is no standardized way to programmatically access related information stored in these heterogeneous systems. Creating a data exchange layer with a common interface to access and query shared brain imaging data will enable the development of interoperable client applications capable of consuming resources available across disparate brain imaging data management systems. We present a preliminary application programming interface (API) for providing uniform access to neuroimaging databases. Conceptually, the API is a service for accessing common entities (e.g., project, subject) and their relationships (e.g., subject wasAssociatedWith project) specified by the XCEDE data model [1] and defined in a lexicon [2]. Neuroimaging databases conforming to the API implement a mapping of their local resources to XCEDE entities and provide a mechanism to request resources. The API is not tied to a specific language or technology, but for web-accessible databases, REST is a natural fit. For example, a REST implementation of the API would respond to an HTTP request for a Subject URI (e.g., www.example.com/xcede_query/subject?uri={uri}) by listing the relationship and URI of related entiies (e.g, {wasAssociatedWith: projectURI} ). The response to an API request can return XCEDE XML or another format (e.g., JSON, RDF) conforming to the XCEDE data model [1]. Discussions with the developers of many neuroimaging databases are helping to refine this specification, and existing tools will speed the first implementation of the API [3]. Our goal is that in the near future, existing databases and those under development will implement this protocol and expose existing and newly acquired datasets in a common data access framework. This work was conducted with the Neuroimaging Task Force of the INCF Program on Standards for Datasharing.