Long-term Data Management Research Articles

Storage over Rendition. Call for a Sustainable Infrastructure in the Digital Textual Heritage Sector with a Particular Interest in Digital Scholarly Editions

A significant amount of human and pecuniary resources has gone into the production of the long line of digital scholarly editions that within recent decades have sprung to life in Scandinavia, in the Baltic region, as well as in the rest of Europe. Notwithstanding the heritage perspective, the sector is paradoxically characterized by a presentist preoccupation with instant results – first and foremost with the rendition of the given data set. Concerns for long-term data management perspectives – that is: interest in the post-production afterlife of the data – is relatively meagre. This goes for project managers, for project host institutions, and for the research foundations financially supporting the projects. So, despite harmonizing initiatives at production level and pre-edition compilation initiatives, such incentives promote a situation of insulated digital scholarly editions focusing on unique URLs and distinctive qualities of the given material. This hinders project synergy in the production phase. Moreover, it hinders the construction of long-term and sustainable data management solutions. To remedy this situation, we propose a clear division of labour between the tasks of data production, of data rendering, and of data storing. This division should ideally be sought at an institutional level. This will secure the accumulation of know-how in teams refining the respective workflows. In addition, we encourage private and public foundations to bolster this infrastructure by making project compliance a criterion for funding.

Legal Compliance and Good Data Stewardship in Data Sharing Plans

The sharing of health-related data is governed by myriad federal and state (and sometimes international) requirements. Moreover, these health-related data sharing rules are evolving, which poses a substantial challenge to creating long-term data management and sharing plans for National Institutes of Health (NIH)-funded or conducted research. To comply with inconsistent and evolving legal standards, the author recommends use of HIPAA’s Expert Determination Method to de-identify data. In addition, in light of concerns about whether de-identification of data is sufficient protection of privacy, and in the absence of a federal law prohibiting the re-identification of individuals in de-identified data sets, the author recommends utilizing contractual controls on the use of de-identified data and restrictions on downstream disclosures of that data.

Injury surveillance in community cricket: A new inning for South Africa.

Published injury rates amongst elite and club-level youth cricketers highlight the need to implement injury risk-reducing strategies amongst the youth cricketing population. Data from sports injury surveillance systems are a prerequisite for the development and evaluation of strategies to reduce injury risk. Therefore, collecting injury surveillance data is a positive move towards reducing injuries in cricket. In South Africa, a systematic, standardised, evidence-informed injury surveillance system currently does not exist for community levels of play, namely, in cricket-playing high schools and cricket clubs. Although injury surveillance systems exist at elite levels, the obvious differences in elite versus community cricket settings mean that these systems cannot be implemented in their current form at community-level cricket. An innovative model is required to implement an injury surveillance system in community cricket.Clinical implicationsThis article proposes and describes a new research–practice partnership model to implement a systematic, standardised, evidence-informed injury surveillance system at cricket-playing high schools or cricket clubs within South Africa. Once this model has been employed, database systems will need to be established to allow long-term data management and sharing.

Improving Data Quality in Clinical Research Informatics Tools.

Maintaining data quality is a fundamental requirement for any successful and long-term data management. Providing high-quality, reliable, and statistically sound data is a primary goal for clinical research informatics. In addition, effective data governance and management are essential to ensuring accurate data counts, reports, and validation. As a crucial step of the clinical research process, it is important to establish and maintain organization-wide standards for data quality management to ensure consistency across all systems designed primarily for cohort identification, allowing users to perform an enterprise-wide search on a clinical research data repository to determine the existence of a set of patients meeting certain inclusion or exclusion criteria. Some of the clinical research tools are referred to as de-identified data tools. Assessing and improving the quality of data used by clinical research informatics tools are both important and difficult tasks. For an increasing number of users who rely on information as one of their most important assets, enforcing high data quality levels represents a strategic investment to preserve the value of the data. In clinical research informatics, better data quality translates into better research results and better patient care. However, achieving high-quality data standards is a major task because of the variety of ways that errors might be introduced in a system and the difficulty of correcting them systematically. Problems with data quality tend to fall into two categories. The first category is related to inconsistency among data resources such as format, syntax, and semantic inconsistencies. The second category is related to poor ETL and data mapping processes. In this paper, we describe a real-life case study on assessing and improving the data quality at one of healthcare organizations. This paper compares between the results obtained from two de-identified data systems i2b2, and Epic Slicedicer, and discuss the data quality dimensions' specific to the clinical research informatics context, and the possible data quality issues between the de-identified systems. This work in paper aims to propose steps/rules for maintaining the data quality among different systems to help data managers, information systems teams, and informaticists at any health care organization to monitor and sustain data quality as part of their business intelligence, data governance, and data democratization processes.

Aggregating Local Features of Convolutional Neural Network for Material Image Retrieval

Large-scale microscopic images in materials science need to be indexed and managed using practical management tools. Content-based Image Retrieval (CBIR), which indexes and searches images based on the image features, allows for long-term data management in large-scale image datasets. Considering the difference between material microscopy images and natural ones, we propose a novel CBIR method for material microscopic images. In the proposed method, convolutional neural networks (CNN) are used to extract local features from an image, and the scale-invariant feature transform (SIFT) model is used to generate a keypoint density map (KDM). Experiments on a material microscopic image dataset show that the proposed method achieves an approving retrieval performance.

Coordination of long-term data management in the Gulf of Mexico: Lessons learned and recommendations from two years of cross-agency collaboration

This paper presents a summary of collaborative work, lessons learned, and suggestions for next steps in coordinating long-term data management in the Gulf of Mexico in the years following the Deepwater Horizon oil spill (DWH). A decade of increased research and monitoring following the DWH has yielded a vast amount of diverse data collected from response and assessment efforts as well as ongoing restoration efforts. To maximize the benefits of this data through proper management and coordination, a cross-agency and organization Long-Term Data Management (LTDM) working group was established in 2017 with sponsorship from NOAA’s Office of Response and Restoration (OR&R) and NOAA’s National Marine Fisheries Service Restoration Center (NMFS RC) and facilitated by the University of New Hampshire’s Coastal Response Research Center. This paper will describe the LTDM working group’s efforts to foster collaboration, data sharing, and best data management practices among the many state, federal, academic and non-governmental entities working to restore and improve the coastal environment in the Gulf following the DWH. Through collaborative workshops and working groups, participants have helped to characterize region-specific challenges, identify areas for growth, leverage existing connections, and develop recommended actions for stakeholders at all organizational levels who share an interest in data coordination and management activities.

Biodiversity: Local Implementation – Keynote

A prevalent challenge to the uniform curation of biodiversity data is the extent to which individual initiatives can manage the production, organization, description, maintenance, and administration of digital assets in the long term. Funding agents have historically supported cutting-edge, niche projects that lacked long-term data management plans. To ensure that new initiatives are resilient and long-lasting, funding agents have increasingly emphasized the importance of building sustainable digital projects at the local level with clear administrative structures that ensure both longevity and wide-scale integration into the larger data environment. It is no longer enough to have a great idea - that great idea must be situated within a larger framework of maintenance and repair. If our data are to withstand the changing and uncertain information and infrastructure landscape, we must articulate a comprehensive set of best practices and workflows that facilitate the integration of local knowledge into global spaces. These best practices must address some thorny issues: How do we create and support systems that are resilient to economic, technological, and standards-level change? How do we bring context to new solutions and initiatives within the broader picture of the data lifecycle? How do we ensure that institutions have a ready pool of trained and diverse information professionals from which to hire and support this evolving landscape? This session will discuss a bird’s-eye view of the biodiversity informatics workflow emphasizing the local considerations of biodiversity work, including the lifecycle of biodiversity data within institutions and the challenges introduced at each stage of the data management process. Resiliency requires that each of the following elements must be addressed in any biodiversity informatics plan: Metadata, schema, and other descriptive standards Scholarly credit and intellectual property Publishing models User services and interface design Workflows and administration of data Training of core workforce to support work in the long term Metadata, schema, and other descriptive standards Scholarly credit and intellectual property Publishing models User services and interface design Workflows and administration of data Training of core workforce to support work in the long term This keynote will examine the challenges and opportunities at the local level of biodiversity work, and what must be done to support scientific research, policy, and public engagement at the global level. We will demonstrate how the Informatics program at the National Museum of Natural History has integrated these ideas and strategies with the mission of the Smithsonian Institution to create, preserve, and disseminate knowledge. By making our data comprehensive, accessible, and well managed, we contribute to the creation of a digital-first Smithsonian reaching a global audience of researchers and educators now and long into the future.

BioCollect - A modern cloud application for standards-base field data recording

Many organisations running citizen science projects don’t have access to or the knowledge or means to develop databases and apps for their projects. Some are also concerned about long-term data management and also how to make the data that they collect accessible and impactful in terms of scientific research, policy and management outcomes. To solve these issues, the Atlas of Living Australia (ALA) has developed BioCollect. BioCollect is a sophisticated, yet simple to use tool which has been built in collaboration with hundreds of real users who are actively involved in field data capture. It has been developed to support the needs of scientists, ecologists, citizen scientists and natural resource managers in the field-collection and management of biodiversity, ecological and natural resource management (NRM) data. BioCollect is a cloud-based facility hosted by the ALA and also includes associated mobile apps for offline data collection in the field. BioCollect provides form-based structured data collection for: Ad-hoc survey-based records; Method-based systematic structured surveys; and Activity-based projects such as natural resource management intervention projects (eg. revegetation, site restoration, seed collection, weed and pest management, etc.). This session will cover how BioCollect is being used for citizen science in Australia and some of the features of the tool.

Interlaboratory comparison of <i>δ</i><sup>13</sup>C and <i>δ</i>D measurements of atmospheric CH<sub>4</sub> for combined use of data sets from different laboratories

Abstract. We report results from a worldwide interlaboratory comparison of samples among laboratories that measure (or measured) stable carbon and hydrogen isotope ratios of atmospheric CH4 (δ13C-CH4 and δD-CH4). The offsets among the laboratories are larger than the measurement reproducibility of individual laboratories. To disentangle plausible measurement offsets, we evaluated and critically assessed a large number of intercomparison results, some of which have been documented previously in the literature. The results indicate significant offsets of δ13C-CH4 and δD-CH4 measurements among data sets reported from different laboratories; the differences among laboratories at modern atmospheric CH4 level spread over ranges of 0.5 ‰ for δ13C-CH4 and 13 ‰ for δD-CH4. The intercomparison results summarized in this study may be of help in future attempts to harmonize δ13C-CH4 and δD-CH4 data sets from different laboratories in order to jointly incorporate them into modelling studies. However, establishing a merged data set, which includes δ13C-CH4 and δD-CH4 data from multiple laboratories with desirable compatibility, is still challenging due to differences among laboratories in instrument settings, correction methods, traceability to reference materials and long-term data management. Further efforts are needed to identify causes of the interlaboratory measurement offsets and to decrease those to move towards the best use of available δ13C-CH4 and δD-CH4 data sets.

AusPlots Rangelands field data collection and publication: Infrastructure for ecological monitoring

The TERN AusPlots Rangelands field data collection system has been developed to facilitate simple and efficient data collection by ecologists operating in the Australian outback. The infrastructure provides tooling for ‘clean’ data collection on mobile (tablet) devices, associated data storage in a cloud-based server infrastructure, facilities for data curation and management and interfaces with the Australian Ecological Knowledge and Observation System (ÆKOS) data repository for long-term data management and semantic enrichment. In this paper, we introduce the AusPlots Rangelands field data collection solution, providing a systems-level view and motivating its development through the discussion of key functional requirements. We provide an outline of the ÆKOS data repository and demonstrate that the combined system provides a unique end-to-end data collection, curation, archiving and publishing mechanism for ecological data.

Co-benefits of Energy and Buildings Data: The Case For supporting Data Access to Achieve a Sustainable Built Environment

Supporting the development of a strong evidence base on which to improve the energy performance of buildings requires having access to research from different ‘levels’ of data. This includes high-level studies to carefully constructed representative samples, exploratory and investigative studies. As sensors and data collection becomes more widely applied within buildings (and the broader built environment) a clear articulation of the potential benefits and risks of data access is needed to avoid unintended consequences and regressive positions to data access.The objective of this work is to identify and discuss the co-benefits of energy and built environment data and the mechanisms needed to enable them. We outline a number of potential benefits and limitations of making energy and buildings data more widely available.Access to and linking/ matching together data can provide numerous benefits, including: research benefits, education and training, academic benefits, funder benefits, policy benefits, among others. However, there are also concerns of making data accessible including: privacy, management of access and communication protocols, commercial sensitivity, intellectual property, and archiving and legacy repository. The mechanisms needed to support data access should include requirements from funders for long-term data management and sharing, funding available for data archaeology, journal requirements for publication, government support and evaluation requests, and industry interest in capturing wider benefits from proprietary data.

The North American Carbon Program Multi-scale Synthesis and Terrestrial Model Intercomparison Project – Part 2: Environmental driver data

Abstract. Ecosystems are important and dynamic components of the global carbon cycle, and terrestrial biospheric models (TBMs) are crucial tools in further understanding of how terrestrial carbon is stored and exchanged with the atmosphere across a variety of spatial and temporal scales. Improving TBM skills, and quantifying and reducing their estimation uncertainties, pose significant challenges. The Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) is a formal multi-scale and multi-model intercomparison effort set up to tackle these challenges. The MsTMIP protocol prescribes standardized environmental driver data that are shared among model teams to facilitate model–model and model–observation comparisons. This paper describes the global and North American environmental driver data sets prepared for the MsTMIP activity to both support their use in MsTMIP and make these data, along with the processes used in selecting/processing these data, accessible to a broader audience. Based on project needs and lessons learned from past model intercomparison activities, we compiled climate, atmospheric CO2 concentrations, nitrogen deposition, land use and land cover change (LULCC), C3 / C4 grasses fractions, major crops, phenology and soil data into a standard format for global (0.5° × 0.5° resolution) and regional (North American: 0.25° × 0.25° resolution) simulations. In order to meet the needs of MsTMIP, improvements were made to several of the original environmental data sets, by improving the quality, and/or changing their spatial and temporal coverage, and resolution. The resulting standardized model driver data sets are being used by over 20 different models participating in MsTMIP. The data are archived at the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC, http://daac.ornl.gov) to provide long-term data management and distribution.

Developing Data Management Services at the Johns Hopkins University

Big data challenges have stimulated national and international initiatives in building inter-connected data repositories and integrated data resources as well as long-term data management and data stewardship to support cross-disciplinary scientific data discovery and reuse. To champion such efforts, Johns Hopkins University (JHU) created and developed a new model of data management services (DMS) encompassing a continuum of Storage→Archiving→Preservation→Curation layers to provide data managing and sharing through the JHU Data Archive (DA). To examine this model of data management services, we contextualized the JHU DMS in a case study drawing upon document analysis and interviews with key stakeholders. Our investigation revealed distinct dimensions of the JHU DMS/DA into environmental responsiveness (see Environmental Responsiveness section for explanation), socio-technical readiness, and marketing and collaboration strategies. We further articulated opportunities, challenges and success determinants of the DMS within its institutional context. We intend for the case study to stimulate further discussion and research on alternative options and extensions of the DMS model in other institutions or contexts.

A flexible and extensible approach to automated CAD/CAM format classification

There are hundreds of distinct 3D, CAD and engineering file formats. As engineering design and analysis has become increasingly digital, the proliferation of file formats has created many problems for data preservation, data exchange, and interoperability. In some situations, physical file objects exist on legacy media and must be identified and interpreted for reuse. In other cases, file objects may have varying representational expressiveness.We introduce the problem of automated file recognition and classification in emerging digital engineering environments, where all design, manufacturing and production activities are “born digital.” The result is that massive quantities and varieties of data objects are created during the product lifecycle.This paper presents an approach to automated identification of engineering file formats. This work operates independent of any modeling tools and can identify families of related file objects as well as variations in versions. This problem is challenging as it cannot assume any a priori knowledge about the nature of the physical file object. Applications for these methods include support for a number of emerging applications in areas such as forensic analysis, data translation, as well as digital curation and long-term data management.