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Advances in machine learning in medical imaging are occurring at a rapid pace in research laboratories both at academic institutions and in industry. Important artificial intelligence (AI) tools for diagnostic imaging include algorithms for disease detection and classification, image optimization, radiation reduction, and workflow enhancement. Although advances in foundational research are occurring rapidly, translation to routine clinical practice has been slower. In August 2018, the National Institutes of Health assembled multiple relevant stakeholders at a public meeting to discuss the current state of knowledge, infrastructure gaps, and challenges to wider implementation. The conclusions of that meeting are summarized in two publications that identify and prioritize initiatives to accelerate foundational and translational research in AI for medical imaging. This publication summarizes key priorities for translational research developed at the workshop including: (1) creating structured AI use cases, defining and highlighting clinical challenges potentially solvable by AI; (2) establishing methods to encourage data sharing for training and testing AI algorithms to promote generalizability to widespread clinical practice and mitigate unintended bias; (3) establishing tools for validation and performance monitoring of AI algorithms to facilitate regulatory approval; and (4) developing standards and common data elements for seamless integration of AI tools into existing clinical workflows. An important goal of the resulting road map is to grow an ecosystem, facilitated by professional societies, industry, and government agencies, that will allow robust collaborations between practicing clinicians and AI researchers to advance foundational and translational research relevant to medical imaging.

The International Materials Resource Registries (IMRR) working group of the Research Data Alliance (RDA) was created to spur initial development of a federated registry system to allow for easier discovery and access to materials data. As part of this effort, a controlled vocabulary and metadata schema were developed with contributions from members of the working group and other experts. Here we describe the process, the resulting vocabulary and XML schema, and lessons learned in the development and use of the schema.

In the past decade, numerous public and private sector documents have highlighted the need for materials data to facilitate advanced technologies in myriad industrial and economic sectors. These documents have been analyzed to identify prevalent gaps in the establishment of an interconnected materials data infrastructure akin to that envisioned in the federal agency-wide Materials Genome Initiative. This internal report uses a uniform schematic format to portray these gaps, illustrate progress in addressing the gaps, and propose an MML roadmap of action items to further address the gaps.

NIST is leading the development of the Research Data Framework (RDaF) with involvement and input from national and international leaders in the broad research data stakeholder community. Research data is defined here as the recorded factual material commonly accepted in the scientific community as necessary to validate research findings. The overarching goal of the RDaF is to provide the stakeholder community with a structured approach to develop a customizable strategy for the management of research data. The audience for the RDaF is the entire research data community, including all organizations and individuals engaged in any activities concerned with research data management, from Chief Executive Officers and Chief Data Officers to librarians and researchers. This document describes the motivation for, and the development of, a Preliminary Framework Core, and identifies the next steps in further development of the RDaF. The research data environment is rapidly changing, and this Framework shall remain a living document. Revisions will be made as we, the stakeholders of the RDaF, gain experience with its application and use.

As a result of a number of national initiatives, we are seeing rapid growth in the data important to materials science that are available over the web. Consequently, it is becoming increasingly difficult for researchers to learn what data are available and how to access them. To address this problem, the Research Data Alliance (RDA) Working Group for International Materials Science Registries (IMRR) was established to bring together materials science and information technology experts to develop an international federation of registries that can be used for global discovery of data resources for materials science. A resource registry collects high-level metadata descriptions of resources such as data repositories, archives, websites, and services that are useful for data-driven research. By making the collection searchable, it aids scientists in industry, universities, and government laboratories to discover data relevant to their research and work interests. We present the results of our successful piloting of a registry federation for materials science data discovery. In particular, we out a blueprint for creating such a federation that is capable of amassing a global view of all available materials science data, and we enumerate the requirements for the standards that make the registries interoperable within the federation. These standards include a protocol for exchanging resource descriptions and a standard metadata schema for encoding those descriptions. We summarize how we leveraged an existing standard (OAI-PMH) for metadata exchange. Finally, we review the registry software developed to realize the federation and describe the user experience.

Scientific progress requires the ability of scientists to build on the results produced by those who preceded them. Because of this, there is concern that irreproducible scientific results are being reported. We suggest that while reproducibility can be an important hallmark of good science, it is not often the most important indicator. The discipline of metrology, or measurement science, describes a measurement result as a value and the uncertainty around that value. We propose a systematic process for considering the sources of uncertainty in a scientific study that can be applied to virtually all disciplines of scientific research. We suggest that a research study can be characterized by how sources of uncertainty in the study are reported and mitigated. Such activities can add to the value of scientific results and the ability to share data effectively.

We present here a summary of a workshop held 1-3 May 2018 at the National Physical Laboratory, Teddington, UK in which the focus was how the national metrology institutes of the world might help to address challenges in reproducibility of research. The workshop brought together experts from the measurement and wider research communities (Physical-, Data- and Life-sciences, Engineering, and Geology) to understand the issues and to explore how good measurement practice and principles can foster confidence in research findings [1, 2, 3], including how we can tackle the challenge posed by increasing data volumes in both industry and research. The workshop involved 63 participants from metrology laboratories (38), academia (16), industry (5), funding agencies (2), and publishers (2). The participants came from UK, US, Korea, France, Germany, Australia, Bosnia and Herzegovina, Canada, Turkey, and Singapore.

This document describes the types of official IVOA documents and the process by which documents are advanced from Working Drafts to formal Recommendations and from Notes to Endorsed Notes

As a National Metrology Institute (NMI), the USA National Institute of Standards and Technology (NIST) scientists, engineers and technology experts conduct research across a full spectrum of physical science domains. NIST is a non-regulatory agency within the U.S. Department of Commerce with a mission to promote U.S. innovation and industrial competitiveness by advancing measurement science, standards, and technology in ways that enhance economic security and improve our quality of life. NIST research results in the production and distribution of standard reference materials, calibration services, and datasets. These are generated from a wide range of complex laboratory instrumentation, expert analyses, and calibration processes. In response to a government open data policy, and in collaboration with the broader research community, NIST has developed a federated Open Access to Research (OAR) scientific data infrastructure aligned with FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Through the OAR initiatives, NIST’s Material Measurement Laboratory Office of Data and Informatics (ODI) recently released a new scientific data discovery portal and public data repository. These science-oriented applications provide dissemination and public access for data from across the broad spectrum of NIST research disciplines, including chemistry, biology, materials science (such as crystallography, nanomaterials, etc.), physics, disaster resilience, cyberinfrastructure, communications, forensics, and others. NIST’s public data consist of carefully curated Standard Reference Data, legacy high valued data, and new research data publications. The repository is thus evolving both in content and features as the nature of research progresses. Implementation of the OAR infrastructure is key to NIST’s role in sharing high integrity reproducible research for measurement science in a rapidly changing world.

IAU Commission 5, Documentation and Astronomical Data, continued its mission of promoting and supporting sound practices of data management, data dissemination, and data preservation over the past three years. The Commission also prepared its proposal for continuation, with some changes in emphasis, after the IAU's commission restructuring program. Below we report on the activities of the various Working Groups and the one Task Force in Commission 5.

A materials data infrastructure that enables the sharing and transformation of a wide range of materials data is an essential part of achieving the goals of the Materials Genome Initiative. We describe two high-level requirements of such an infrastructure as well as an emerging open-source implementation consisting of the Materials Data Curation System and the National Institute of Standards and Technology Materials Resource Registry.

The current push for rigor and reproducibility is driven by a desire for confidence in research results. Here, we suggest a framework for a systematic process, based on consensus principles of measurement science, to guide researchers and reviewers in assessing, documenting, and mitigating the sources of uncertainty in a study. All study results have associated ambiguities that are not always clarified by simply establishing reproducibility. By explicitly considering sources of uncertainty, noting aspects of the experimental system that are difficult to characterize quantitatively, and proposing alternative interpretations, the researcher provides information that enhances comparability and reproducibility.

Both the global research community and federal governments are embracing a move toward more open sharing of the products of research. Historically, the primary product of research has been peer-reviewed journal articles and published technical reports. However, advances in information technology, new 'open access' business models, and government policies are working to make publications and supporting materials much more accessible to the general public. These same drivers are blurring the distinction between the data generated through the course of research and the associated publications. These developments have the potential to significantly enhance the value of both publications and supporting digital research data, turning them into valuable assets that can be shared and reused by other researchers. The confluence of these shifts in the research landscape leads one to the conclusion that technical publications and their supporting research data must be bound together in a rational fashion. However, bringing these two research products together will require the establishment of new policies and a supporting data infrastructure that have essentially no precedent in the materials community, and indeed, are stressing many other fields of research. This document raises the key issues that must be addressed in developing these policies and infrastructure and suggests a path forward in creating the solutions.

Purpose This study aims to look at the changing way in which the Information Services Office (ISO) at the National Institute of Standards and Technology (NIST) provides services to NIST scientific and technical staff throughout their research and publishing cycles. These services include the more traditional services of a research library and publishing NIST technical reports and The Journal of Research of NIST and preserving and exhibiting scientific instruments and other artifacts. ISO has always prided itself on having a close relationship with its customers, providing a high level of service and developing new services to stay in front of NIST researcher needs. Through a concerted, strategic effort since the late 1990s, ISO has developed and promoted relationships with its key customers through its Lab Liaison Program. Design/methodology/approach This paper discusses the relationship ISO has developed with the Office of Data and Informatics (ODI), how this relationship was forged and how this collaboration will serve as a model for working with the other labs and programs at NIST. It will also discuss the risks and opportunities of this new collaborative service model, how ISO positioned itself to become an equal partner with ODI in the exploration of solutions to data management issues and the benefits of the relationship from ODI’s perspective. Findings A pattern of strategic changes to the services and activities offered by the Lab Liaison program has put ISO in the position to collaborate as peers with researchers at NIST. Originality/value This study provides an overview of how ISO made strategic decisions to incorporate non-traditional services to support data management at NIST.

IAU Commission 5 deals with data management issues, and its working groups and task groups deal specifically with information handling, with data centres and networks, with technical aspects of collection, archiving, storage and dissemination of data, with designations and classification of astronomical objects, with library services, editorial policies, computer communications, ad hoc methodologies, and with various standards, reference frames, etc., FITS, astronomys Flexible Image Transport System, the major data exchange format, is controlled, maintained and updated by the Working Group FITS.

Astronomy is similar to other scientific disciplines in that scholarly publication relies on the presentation and interpretation of data. But although astronomy now has archives for its primary research telescopes and associated surveys, the highly processed data that is presented in the peer-reviewed journals and is the basis for final analysis and interpretation is generally not archived and has no permanent repository. We have initiated a project whose goal is to implement an end-to-end prototype system which, through a partnership of a professional society, that society’s scholarly publications/publishers, research libraries, and an information technology substrate provided by the Virtual Observatory, will capture high-level digital data as part of the publication process and establish a distributed network of curated, permanent data repositories. The data in this network will be accessible through the research journals, astronomy data centers, and Virtual Observatory data discovery portals.

The U.S. Virtual Astronomical Observatory was a software infrastructure and development project designed both to begin the establishment of an operational Virtual Observatory (VO) and to provide the U.S. coordination with the international VO effort. The concept of the VO is to provide the means by which an astronomer is able to discover, access, and process data seamlessly, regardless of its physical location. This paper describes the origins of the VAO, including the predecessor efforts within the U.S. National Virtual Observatory, and summarizes its main accomplishments. These accomplishments include the development of both scripting toolkits that allow scientists to incorporate VO data directly into their reduction and analysis environments and high-level science applications for data discovery, integration, analysis, and catalog cross-comparison. Working with the international community, and based on the experience from the software development, the VAO was a major contributor to international standards within the International Virtual Observatory Alliance. The VAO also demonstrated how an operational virtual observatory could be deployed, providing a robust operational environment in which VO services worldwide were routinely checked for aliveness and compliance with international standards. Finally, the VAO engaged in community outreach, developing a comprehensive web site with on-line tutorials, announcements, links to both U.S. and internationally developed tools and services, and exhibits and hands-on training .... All digital products of the VAO Project, including software, documentation, and tutorials, are stored in a repository for community access. The enduring legacy of the VAO is an increasing expectation that new telescopes and facilities incorporate VO capabilities during the design of their data management systems., abridged for arXiv limit; 23 pages, 15 figures; to appear in a special issue of Astronomy & Computing

We present a sample of 1744 type 1 active galactic nuclei (AGNs) from the Sloan Digital Sky Survey (SDSS DR4) spectroscopic catalog with X-ray counterparts in the White-Giommi-Angelini Catalog (WGACAT) of ROSAT PSPC pointed observations. Of 1744 X-ray sources, 1410 (80.9%) are new AGN identifications. Of 4574 SDSS DR4 AGNs for which we found radio matches in the catalog of radio sources from the FIRST catalog, 224 turned up in our sample of SDSS X-ray AGNs. The sample objects are given in a catalog that contains optical and X-ray parameters along with radio emission parameters where available. We illustrate the content of our catalog and its potential for AGN science by providing statistical relationships for the catalog data. The potential of the morphological information is emphasized by confronting the statistics of optically resolved and unresolved AGNs. The immediate properties of the catalog objects include significant correlation of X-ray and optical fluxes, which is consistent with expectations. Also expected is the decrease of X-ray flux toward higher redshifts. The X-ray to optical flux ratio for the unresolved AGNs exhibits a decline toward higher redshifts, in agreement with previous results. The resolved AGNs, however, display the opposite trend. At a given optical brightness, X-ray fluxes of radio-quiet AGNs by a factor of 2. We caution, however, that because of the variety of selection effects present in both the WGACAT and the SDSS, the interpretation of any relationships based on our sample of X-ray AGNs requires a careful analysis of these effects.

We present a summary of the major contributions to the Special Session on Astronomical Data Management held at the IAU XXVI General Assembly in Prague in 2006. While recent years have seen enormous improvements in access to astronomical data, and the Virtual Observatory aims to provide astronomers with seamless access to on-line resources, more attention needs to be paid to ensuring the quality and completeness of those resources. For example, data produced by telescopes are not always made available to the astronomical community, and new instruments are sometimes designed and built with insufficient planning for data management, while older but valuable legacy data often remain undigitised. Data and results published in journals do not always appear in the data centres, and astronomers in developing countries sometimes have inadequate access to on-line resources. To address these issues, an ‘Astronomers' Data Manifesto’ has been formulated with the aim of initiating a discussion that will lead to the development of a ‘code of best practice’ in astronomical data management.

We describe an on-line system for automated classification of X-ray sources, ClassX, and present preliminary results of classification of the three major catalogs of ROSAT sources, RASS BSC, RASS FSC, and WGACAT, into six class categories: stars, white dwarfs, X-ray binaries, galaxies, AGNs, and clusters of galaxies. ClassX is based on a machine learning technology. It represents a system of classifiers, each classifier consisting of a considerable number of oblique decision trees. These trees are built as the classifier is 'trained' to recognize various classes of objects using a training sample of sources of known object types. Each source is characterized by a preselected set of parameters, or attributes; the same set is then used as the classifier conducts classification of sources of unknown identity. The ClassX pipeline features an automatic search for X-ray source counterparts among heterogeneous data sets in on-line data archives using Virtual Observatory protocols; it retrieves from those archives all the attributes required by the selected classifier and inputs them to the classifier. The user input to ClassX is typically a file with target coordinates, optionally complemented with target IDs. The output contains the class name, attributes, and class probabilities for all classified targets. We discuss ways to characterize and assess the classifier quality and performance and present the respective validation procedures. Based on both internal and external validation, we conclude that the ClassX classifiers yield reasonable and reliable classifications for ROSAT sources and have the potential to broaden class representation significantly for rare object types.

We report on deep imaging in two filters with the planetary camera of the Hubble Space Telescope of five QSOs at redshift of ~2, with a range of optical and radio luminosity. The observations included a suite of point-spread function (PSF) observations that were used to construct new PSF models, described elsewhere by Dumont et al. The new PSF models were used to remove the QSO nucleus from the images. We find that the host galaxies have resolved flux of the order of 10% of the QSO nuclei and are generally luminous and blue, indicating active star formation. While most have clearly irregular morphologies, the bulk of the flux can be modeled approximately by an R1/4 law. However, all host galaxies also have an additional approximately exponential luminosity profile beyond a radius of about 08, as also seen in ground-based data with larger telescopes. The QSOs all have a number of nearby faint blue companions that may be young galaxies at the QSO redshift. We discuss implications for evolution of the host galaxies, their spheroidal populations, and central black holes.

We examine cosmic ray rejection methodology on data collected from InSb and Si:As detectors. The application of an Up-the-Ramp sampling technique with cosmic ray identification and mitigation is the focus of this study. This technique is valuable for space-based observatories which are exposed to high-radiation environments. We validate the Up-the-Ramp approach on radiation-test data sets with InSb and Si:As detectors which were generated for SIRTF. The Up-the-Ramp sampling method studied in this paper is over 99.9% effective at removing cosmic rays and preserves the structure and photometric quality of the image to well within the measurement error.

We present an algorithm to optimally process uniformly sampled array image data obtained with a nondestructive readout. The algorithm discards full wells, removes cosmic ray (particle) hits and other glitches, and makes a nearly optimum estimate of the signal on each pixel. The algorithm also compresses the data. The computer requirements are modest, and the results are robust. The results are shown and compared to results of Fowler sampled and processed data. Non-ideal detector performance may require some additional code, but this is not expected to cost much processing time. Known types of detector faults are addressed., This paper has been accepted for publication in the PASP

While software and algorithms have become increasingly important in astronomy, the majority of authors who publish computational astronomy research do not share the source code they develop, making it difficult to replicate and reuse the work. In this paper we discuss the importance of sharing scientific source code with the entire astrophysics community, and propose that journals require authors to make their code publicly available when a paper is published. That is, we suggest that a paper that involves a computer program not be accepted for publication unless the source code becomes publicly available. The adoption of such a policy by editors, editorial boards, and reviewers will improve the ability to replicate scientific results, and will also make the computational astronomy methods more available to other researchers who wish to apply them to their data., Comment: Accepted by Astronomy and Computing. 10 pages