Research Data Management Platform Research Articles

Practice of a Load Shifting Algorithm for Enhancing Community-Scale RES Utilization

Amidst the recent energy crisis, the pivotal roles of resource efficiency and renewable energy sources (RES) for sustainable development have become apparent. The transition to sustainability involves decentralized energy solutions empowering local communities to generate, store, and utilize their energy, diminishing the reliance on centralized systems and potentially transforming them into resources for power flexibility. Addressing the above necessitates, amongst other elements, the adoption of advanced demand-side management (DSM) strategies. In response, we introduce a versatile algorithm investigating the impact of DSM on the community scale, designed to maximize the utilization of renewable energy produced from local installations. Integrated as an ancillary module in a research data management platform, the algorithm underwent testing using historical datasets collected from end-consumers and a small-scale RES installation. This study not only offers insights for energy stakeholders, but also establishes theoretical parameters that can inform subsequent decision-making processes in the field.

Globus automation services: Research process automation across the space–time continuum

Research process automation–the reliable, efficient, and reproducible execution of linked sets of actions on scientific instruments, computers, data stores, and other resources–has emerged as an essential element of modern science. We report here on new services within the Globus research data management platform that enable the specification of diverse research processes as reusable sets of actions, flows, and the execution of such flows in heterogeneous research environments. To support flows with broad spatial extent (e.g., from scientific instrument to remote data center) and temporal extent (from seconds to weeks), these Globus automation services feature: (1) cloud hosting for reliable execution of even long-lived flows despite sporadic failures; (2) a simple specification and extensible asynchronous action provider API, for defining and executing a wide variety of actions and flows involving heterogeneous resources; (3) an event-driven execution model for automating execution of flows in response to arbitrary events; and (4) a rich security model enabling authorization delegation mechanisms for secure execution of long-running actions across distributed resources. These services permit researchers to outsource and automate the management of a broad range of research tasks to a reliable, scalable, and secure cloud platform. We present use cases for Globus automation services, describe their design and implementation, present microbenchmark studies, and review experiences applying the services in a range of applications.

Building a Research Data Management Platform Under the Perspective of Data Life Cycle Theory

Building a Research Data Management Platform Under the Perspective of Data Life Cycle Theory

Global presence of open-source research data management platform for libraries: the Dataverse project

PurposeThis paper aims to provide statistical information on the worldwide spread of the open-source research data management application, the Dataverse Project, to librarians, data managers and information managers who are considering using the application at their own institution.Design/methodology/approachTo produce a list of dataverse repositories, the official Dataverse website was evaluated, and JSON data were downloaded and parsed. Data standardisation was performed to assess the state of installations in various nations and continents across the world.FindingsGlobally, the Dataverse repositories have seen a rise in overall installations. The year 2020 alone saw a 23.21% rise. In a country-by-country comparison, the USA (13) has the most dataverse installations, while Europe (25) has the highest number of installations worldwide.Originality/valueThis research will be useful to librarians, data managers and information managers, among others, who want to learn more about Dataverse repositories throughout the world before deploying at their local level.

Playing Well on the Data FAIRground: Initiatives and Infrastructure in Research Data Management

Over the past five years, Elsevier has focused on implementing FAIR and best practices in data management, from data preservation through reuse. In this paper we describe a series of efforts undertaken in this time to support proper data management practices. In particular, we discuss our journal data policies and their implementation, the current status and future goals for the research data management platform Mendeley Data, and clear and persistent linkages to individual data sets stored on external data repositories from corresponding published papers through partnership with Scholix. Early analysis of our data policies implementation confirms significant disparities at the subject level regarding data sharing practices, with most uptake within disciplines of Physical Sciences. Future directions at Elsevier include implementing better discoverability of linked data within an article and incorporating research data usage metrics.

Metadata is key - the most important data after data

Metadata is key - the most important data after data

Research Data Management Tools and Workflows: Experimental Work at the University of Porto

Research datasets include all kinds of objects, from web pages to sensor data, and originate in every domain. Concerns with data generated in large projects and well-funded research areas are centered on their exploration and analysis. For data in the long tail, the main issues are still how to get data visible, satisfactorily described, preserved, and searchable.
 Our work aims to promote data publication in research institutions, considering that researchers are the core stakeholders and need straightforward workflows, and that multi-disciplinary tools can be designed and adapted to specific areas with a reasonable effort. For small groups with interesting datasets but not much time or funding for data curation, we have to focus on engaging researchers in the process of preparing data for publication, while providing them with measurable outputs. In larger groups, solutions have to be customized to satisfy the requirements of more specific research contexts.
 We describe our experience at the University of Porto in two lines of enquiry. For the work with long-tail groups we propose general-purpose tools for data description and the interface to multi-disciplinary data repositories. For areas with larger projects and more specific requirements, namely wind infrastructure, sensor data from concrete structures and marine data, we define specialized workflows. In both cases, we present a preliminary evaluation of results and an estimate of the kind of effort required to keep the proposed infrastructures running. 
 The tools available to researchers can be decisive for their commitment. We focus on data preparation, namely on dataset organization and metadata creation. For groups in the long tail, we propose Dendro, an open-source research data management platform, and explore automatic metadata creation with LabTablet, an electronic laboratory notebook. For groups demanding a domain-specific approach, our analysis has resulted in the development of models and applications to organize the data and support some of their use cases. Overall, we have adopted ontologies for metadata modeling, keeping in sight metadata dissemination as Linked Open Data.

The research data management platform (RDMP): A novel, process driven, open-source tool for the management of longitudinal cohorts of clinical data.

BackgroundThe Health Informatics Centre at the University of Dundee provides a service to securely host clinical datasets and extract relevant data for anonymized cohorts to researchers to enable them to answer key research questions. As is common in research using routine healthcare data, the service was historically delivered using ad-hoc processes resulting in the slow provision of data whose provenance was often hidden to the researchers using it. This paper describes the development and evaluation of the Research Data Management Platform (RDMP): an open source tool to load, manage, clean, and curate longitudinal healthcare data for research and provide reproducible and updateable datasets for defined cohorts to researchers.ResultsBetween 2013 and 2017, RDMP tool implementation tripled the productivity of data analysts producing data releases for researchers from 7.1 to 25.3 per month and reduced the error rate from 12.7% to 3.1%. The effort on data management reduced from a mean of 24.6 to 3.0 hours per data release. The waiting time for researchers to receive data after agreeing a specification reduced from approximately 6 months to less than 1 week. The software is scalable and currently manages 163 datasets. A total 1,321 data extracts for research have been produced, with the largest extract linking data from 70 different datasets.ConclusionsThe tools and processes that encompass the RDMP not only fulfil the research data management requirements of researchers but also support the seamless collaboration of data cleaning, data transformation, data summarization and data quality assessment activities by different research groups.

Ranking Dublin Core descriptor lists from user interactions: a case study with Dublin Core Terms using the Dendro platform

Dublin Core descriptors capture metadata in most repositories, and this includes recent repositories dedicated to datasets. DC descriptors are generic and are being adapted to the requirements of different communities with the so-called Dublin Core Application Profiles that rely on the agreement within user communities, taking into account their evolving needs. In this paper, we propose an automated process to help curators and users discover the descriptors that best suit the needs of a specific research group in the task of describing and depositing datasets. Our approach is supported on Dendro, a prototype research data management platform, where an experimental method is used to rank and present DC Terms descriptors to the users based on their usage patterns. User interaction is recorded and used to score descriptors. In a controlled experiment, we gathered the interactions of two groups as they used Dendro to describe datasets from selected sources. One of the groups viewed descriptors according to the ranking, while the other had the same list of descriptors throughout the experiment. Preliminary results show that (1) some DC Terms are filled in more often than others, with different distribution in the two groups, (2) descriptors in higher ranks were increasingly accepted by users in detriment of manual selection, (3) users were satisfied with the performance of the platform, and (4) the quality of description was not hindered by descriptor ranking.

A process-driven platform to manage datasets for research

ABSTRACT
 Objectives
 
 Accumulate, manage and control shared access to research data; 
 Transform and maintain transformation state information about research data; 
 Analyse and investigate data in related sets using open and bespoke tools; 
 Publish extracted data to a secure safe haven environment.
 
 ApproachThe Research Data Management Platform (RDMP) is a set of data structures and processes, sharing a core Catalogue, to manage electronic health records, genomic data and imaging data throughout their lifecycle from identification and acquisition to safe disposal or archival and retention in secured Safe Havens (SH).
 The architecture components of the RDMP consist of the Catalogue and five internal processes: Data Load, Catalogue Management, Data Quality, Data Summary, and Data Extraction. These are designed to enforce rigorous information governance standards relevant to the processing and anonymisation of personal identifiable data. The Catalogue serves as the single ‘source of truth’ about the datasets which all RDMP processes consult. This facilitates repeatable, reliable and auditable operations on the data.
 The novelty of the RDMP is that it dynamically and seamlessly captures and preserves data transformation processes along with the primary research data to promote reuse and curation of continuously accruing research data repositories in a secure SH environment. Thus, the RDMP brings transparency and reproducibility that benefits research programmes in a way that archival of static data objects does not.
 ResultsThe RDMP has been in production use since July 1st 2014. There are 107 datasets configured in the Catalogue, with up to 67 dataset extractions for each of 48 research projects. It has provided data for 32 high-impact journal papers published in the last year.
 Improvements in turnaround time:
 
 Research project data provision reduced from six months to two weeks;
 Data loading reduced from two days to a few hours; 
 Research query response reduced from days to within a day, due to improved and standardised metadata catalogue
 
 ConclusionThe RDMP is a key component in automating the regular release of datasets and rationalising dataset changes over time to ensure reliable delivery of extracts to research projects. The tools and processes comprising the RDMP not only fulfil the RDM requirements of researchers, but also support seamless collaboration of data cleaning, data transformation, data summarisation and data quality assessment activities by different research groups.

Secure provision of unconsented radiological imaging for research

ABSTRACT
 Objectives
 
 Design and implement an architecture for managing unconsented DICOM imaging
 Maintain sufficient data to define research cohorts when data quality is unknown
 Perform project-level linkage and extraction into a Safe Haven (SH) environment
 Extract large image volumes for multiple projects with limited storage constraints
 Provide applications for an imaging research workflow within the SH environment
 Serve as a prototype for the Farr/NHS Scotland project to create a research dataset from Scotland’s national PACS
 
 ApproachThe software architecture builds on the Research Data Management Platform (RDMP) developed at Dundee’s Health Informatics Centre (HIC) within Farr@Dundee. The RDMP provides core services common to loading any dataset, with configuration and extensibility points for dataset-specific implementations. This architecture augments the RDMP with scalable micro-services performing peripheral functions.
 Images are sourced from the local PACS server in Ninewells Hospital and cached securely within HIC using an implementation for the RDMP with a custom server to query/retrieve data.
 Data stored in the catalogue should be anonymous, according to the Scottish SH model. The imaging dataset is poorly understood, with several potentially identifiable free-text fields which may contain information required for defining suitable research cohorts. The load process only permits verified metadata fields into the anonymised catalogue; a Mongo database stores other data for later analysis, should a field subsequently be required for cohort definition.
 A DICOM extraction implementation is provided, using DICOM Confidential for anonymisation and a project-specific remapping of DICOM GUIDs.
 Two provisioning methods have been designed. A basic copy when sufficient storage is available, and a more sophisticated method using a custom filesystem to provide separate project-specific views onto shared image files.
 ResultsA full end-to-end solution has been developed, from initial caching through to provisioning anonymised images. Two imaging cohorts have been loaded, one with over 5000 studies. NHS Tayside CT and MR data since 2008 is currently being loaded.
 Two projects have had anonymised extracts released using the ‘copy’ method. The custom filesystem method has been developed and tested with limited amounts of data.
 This work has highlighted anonymisation, cohort creation and SH issues which require further exploration.
 ConclusionA production system for securely providing linked DICOM imaging to researchers has been implemented, serving as a testbed for a national system which will provide a unique population-level resource for researchers.

High-Content Screening Data Management for Drug Discovery in a Small- to Medium-Size Laboratory: Results of a Collaborative Pilot Study Focused on User Expectations as Indicators of Effectiveness

High-content screening (HCS) technology provides a powerful vantage point to approach biological problems; it allows analysis of cell parameters, including changes in cell or protein movement, shape, or texture. As part of a collaborative pilot research project to improve bioscience research data integration, we identified HCS data management as an area ripe for advancement. A primary goal was to develop an integrated data management and analysis system suitable for small- to medium-size HCS programs that would improve research productivity and increase work satisfaction. A system was developed that uses Labmatrix, a Web-based research data management platform, to integrate and query data derived from a Cellomics STORE database. Focusing on user expectations, several barriers to HCS productivity were identified and reduced or eliminated. The impact of the project on HCS research productivity was tested through a series of 18 lab-requested integrated data queries, 7 of which were fully enabled, 7 partially enabled, and 4 enabled through data export to standalone data analysis tools. The results are limited to one laboratory, but this pilot suggests that through an "implementation research" approach, a network of small- to medium-size laboratories involved in HCS projects could achieve greater productivity and satisfaction in drug discovery research.