Fostering Data Sharing in Multidisciplinary Research Communities: A Case Study in the Geospatial Domain

Our research focuses on a user support system for the realisation of two aims; firstly, the consistent management and support of a definition procedure for a distributed simulation while storing and re-using definition parameters, and secondly, the remote execution of a distributed simulation system with a single manufacturing system designer. In this paper, we clarify the requirements of the user support system. We propose requirement functions and a fundamental structure of the user support system. We develop a user support system that is based on the use of the distributed simulation network system and the synchronisation method developed in the past research. The user support system is confirmed using a case to evaluate a hypothetical manufacturing system in which motors are produced. With this manufacturing system, it is assumed that three separate companies design the manufacturing system concurrently and that each designer is located away from the other designers in Japan. It is also assumed that a designer from one of the three companies executes the distributed simulation system using the user support system. The case study was then carried out to evaluate the performance of the co-operative work.

More and more companies are implementing integrated user support systems with their new software applications. Because of their high complexity, user support systems require multidisciplinary teamwork. Technical writers and their fellow team workers are consequentially presented with a range of new opportunities and new challenges, both on a personal and on a professional level. Opportunities include the chance to learn and benefit from new methods and techniques, and the opening to design and develop truly integrated systems. Among the challenges are cooperation between people from different backgrounds and disciplines, the relatively high expense of multidisciplinary teamwork and the heavy demands on project management. In this paper, these and other opportunities and challenges are discussed and illustrated by examples taken from a project at Shell International.

Advances in usability and design techniques (e.g. user-centered design) try to facilitate the use of interactive systems. However, users still have to adapt to interactive systems, i.e. they have to learn the steps required to accomplish a task either by trial and error or by obtaining help. While advanced users are usually able to adapt without much effort this is far from being the case with beginners. Some interactive systems offer different interaction styles in an attempt to meet the needs of all types of user but this is not the case with all interactive systems. In this paper we present the USS framework, which aims to be the basis for the adaptation of interactive systems to user needs through task automation. The work is illustrated by means of a case study.

Advances in usability and design techniques (e.g. user-centered design) try to facilitate the use of interactive systems. However, users still have to adapt to interactive systems, i.e. they have to learn the steps required to accomplish a task either by trial and error or by obtaining help. While advanced users are usually able to adapt without much effort this is far from being the case with beginners. Some interactive systems offer different interaction styles in an attempt to meet the needs of all types of user but this is not the case with all interactive systems. In this sense, we present an approach to support the use of any interactive system making use of enriched models and picture-driven computing to achieve tasks automation. The USS tool (User Support System) is the basis to the adaptation of interactive systems accordingly to the users' needs. The approach provides the foundation for the addition of help (based on demonstration) to any graphical user interfaces (GUI) facilitating learning and use. The work is illustrated by a case study and completed with a preliminary user evaluation which provides insights about the validity of the approach.

Advances in sequencing technologies and declining costs are increasing the accessibility of large-scale biodiversity genomic datasets. To maximize the impact of these data, a careful, considered approach to data management is essential. However, challenges associated with the management of such datasets remain, exacerbated by uncertainty among the research community as to what constitutes best practices. As an interdisciplinary team with diverse data management experience, we recognize the growing need for guidance on comprehensive data management practices that minimize the risks of data loss, maximize efficiency for stand-alone projects, enhance opportunities for data reuse, facilitate Indigenous data sovereignty and uphold the FAIR and CARE Guiding Principles. Here, we describe four fictional personas reflecting differing user experiences with data management to identify data management challenges across the biodiversity genomics research ecosystem. We then use these personas to demonstrate realistic considerations, compromises and actions for biodiversity genomic data management. We also launch the Biodiversity Genomics Data Management Hub (https://genomicsaotearoa.github.io/data-management-resources/), containing tips, tricks and resources to support biodiversity genomics researchers, especially those new to data management, in their journey towards best practice. The Hub also provides an opportunity for those biodiversity researchers whose expertise lies beyond genomics and are keen to advance their data management journey. We aim to support the biodiversity genomics community in embedding data management throughout the research lifecycle to maximize research impact and outcomes.

SynopsisStandards-based data management facilitates data preservation, discoverability, and access for effective data reuse within research groups and across communities of researchers. Data sharing requires community consensus on standards for data management, such as storage and formats for digital data preservation, metadata (i.e., contextual data about the data) that should be recorded and stored, and data access. Video imaging is a valuable tool for measuring time-varying phenotypes in organismal biology, with particular application for research in functional morphology, comparative biomechanics, and animal behavior. The raw data are the videos, but videos alone are not sufficient for scientific analysis. Nearly endless videos of animals can be found on YouTube and elsewhere on the web, but these videos have little value for scientific analysis because essential metadata such as true frame rate, spatial calibration, genus and species, weight, age, etc. of organisms, are generally unknown. We have embarked on a project to build community consensus on video data management and metadata standards for organismal biology research. We collected input from colleagues at early stages, organized an open workshop, “Establishing Standards for Video Data Management,” at the Society for Integrative and Comparative Biology meeting in January 2017, and then collected two more rounds of input on revised versions of the standards. The result we present here is a rubric consisting of nine standards for video data management, with three levels within each standard: good, better, and best practices. The nine standards are: (1) data storage; (2) video file formats; (3) metadata linkage; (4) video data and metadata access; (5) contact information and acceptable use; (6) camera settings; (7) organism(s); (8) recording conditions; and (9) subject matter/topic. The first four standards address data preservation and interoperability for sharing, whereas standards 5–9 establish minimum metadata standards for organismal biology video, and suggest additional metadata that may be useful for some studies. This rubric was developed with substantial input from researchers and students, but still should be viewed as a living document that should be further refined and updated as technology and research practices change. The audience for these standards includes researchers, journals, and granting agencies, and also the developers and curators of databases that may contribute to video data sharing efforts. We offer this project as an example of building community consensus for data management, preservation, and sharing standards, which may be useful for future efforts by the organismal biology research community.

Objective To explore the empirical study on multidisciplinary follow-up model in quality control of bariatric and metabolic surgery. Methods The clinical data of 110 patients, who underwent surgery in the department of bariatric and metabolic surgery of the First Affiliated Hospital of Nanjing Medical University from January 2016 to December 2016, were retrospectively analyzed. In the first half year, the follow-up was mainly conducted by surgeons. In the second half year, a multidisciplinary follow-up group was established. These patients were divided into multidisciplinary follow-up management group (research group) and traditional management group (control group). There were 57 cases in the research group with 16 males and 41 females, and the mean age was (46.1±10.9) years. There were 53 cases in the control group with 14 males and 39 females, and the mean age was (42.2±8.3) years. The multidisciplinary follow-up mode includes bariatric surgeons, case managers, endocrinologist and dietitian; while the traditional management follow-up group only includes bariatric surgeons and case managers. Postoperative follow-up rate, patients’ satisfaction to medical services, postoperative nutritional complications, postoperative gastrointestinal complications and excess weight loss between these two groups were compared. Results The 1-year follow-up rate in the research group was 82.5%, which was significantly higher than the control group (P<0.05); the rate of excess weight loss in the research group was (83.1+16.1)%, which was significantly higher than the control group; the rate of patients' satisfaction to surgical outcomes was 93% (53/57) in the research group, which was significantly higher than the control group (73.6%, 39/53, χ2=4.410, P<0.05); the rate of nutritional complications in the research group was 5.3% (3/57), which was significantly lower than the control group (22.6%, 12/53, χ2=4.961, P<0.05). Conclusion Multidisciplinary follow-up model can adapt to the needs of multidisciplinary treatment in metabolic surgery, improve the follow-up rate, increase the rate of postoperative excess weight loss and patients’ satisfaction, and decrease postoperative complication rate. Thus, it has obvious advantages compared with the traditional management model. Key words: Obesity; Bariatric and metabolic surgery; Multidisciplinary follow-up model; Quality control

This article explores how organizations can leverage Lakehouse technology to build robust AI-ready data strategies. Lakehouse architecture represents a paradigm shift in data management by unifying data lake flexibility with data warehouse reliability. The integration of this technology enables organizations to overcome traditional barriers to AI implementation through unified storage, efficient processing, and comprehensive governance. By examining key components including data ingestion, preparation, metadata management, governance, and scalable infrastructure, the article illustrates how Lakehouse technology establishes a foundation for advanced AI applications like predictive analytics, recommendation systems, natural language processing, and automated decision-making. The article addresses common implementation challenges and provides solutions for data governance, quality assurance, infrastructure scaling, and integration with AI/ML tools, offering organizations practical guidance for transforming their data infrastructure into a catalyst for AI innovation.

Objective – In consideration of emerging national Research Data Management (RDM) policy and infrastructure, this literature review seeks answers to the following questions: 1) What is the most effective way for a Canadian research university to build capacity among library and campus-wide research support staff, with a view towards providing coordinated RDM support services for our researcher community?2) What international training models and course offerings are available and appropriate for a local context?3) What national guidelines and best practices for pedagogical design and delivery can be adapted for a local context? Methods – This literature review synthesizes a total of 13 sources: 9 articles, 2 book chapters, and 2 whitepapers. The whitepapers were selected for a narrative literature review because of their focus on case studies detailing train-the-trainer models. Within the 13 sources we found 14 key case studies. This review serves as a supplement to the 2017 CARL Portage Training Expert Group white paper, “Research Data Management Training Landscape in Canada,” the focus of which was to identify RDM training gaps in order to recommend a coordinated approach to RDM training in a national environment. Results – The narrative review of case studies revealed three thematic areas. Firstly, pedagogical challenges were identified, including the need to target training to RDM support staff such as librarians and researchers, as they comprise distinct groups of trainees with divergent disciplinary vocabularies and incentives for training. Secondly, the case studies cover a broad range of pedagogical models including single or multiple sessions, self-directed or instructor-led, in-person or online instruction, and a hybrid of the two. Finally, RDM training also emerged as a key factor in community building within library staff units, among service units on campus, and with campus research communities. Conclusion – RDM training programs at local institutions should be guided by a set of principles aligned with the training methods, modes of assessment, and infrastructure development timeline outlined in a national training strategy. When adapting principles and training strategies to a local context, the following trends in the literature should be considered: librarians and researchers must have meaningful incentives to undertake training in RDM or to join a community of practice; disciplinary-specific instruction is preferable to general instruction; a librarian’s own training opportunities will influence their ability to provide discipline-specific RDM instruction to researchers; in-person training opportunities improve learning retention and produce beneficial secondary effects, whereas online instruction is most effective when paired with an in-person component; generalized third-party RDM training should be adapted to local context to be meaningful. Future directions for RDM training will integrate into open access and digital scholarship training, and into cross-disciplinary, open science communities of practice.

Australia invests significant resources in environmental data acquisition, management and publication. Although data are abundant, users are typically hampered by an inability to discover, access and re-use the data. The National Environmental Information Infrastructure (NEII) activity will improve the effectiveness and efficiency of discovering, accessing and re-using environmental data beyond its primary purpose. The NEII is envisioned as a federation of environmental data nodes adopting common standards to create a data infrastructure with an initial focus on nationally significant environmental data. Its primary focus is on the discovery and re-use of national environmental data that is already well-managed, but that currently has limited application beyond its original business purpose. In common with spatial data infrastructures, the NEII encompasses common data models, exchange formats and standard network protocols along with centralised catalogues of uniform metadata descriptions. It also includes standardised models for describing environmental measurements, monitoring sites and methods used to observe the environment to address the unique requirements of environmental information. These architectural elements are more fully described in the NEII Reference Architecture (Bureau of Meteorology, 2014b, www.neii.gov.au). The Bureau of Meteorology is leading the development of the NEII including provision of core coordination and integration infrastructure, as well as the governance and collaboration framework for its development and operation. However its ongoing success is dependent on establishing enduring partnerships with major national environmental information organisations. The NEII Programme is framed around five focus areas including (a) Communication, (b) Engagement, (c) Policies and frameworks, (d) Data management, and (e) ICT build. This paper provides a more detailed overview of one component of the Data Management area regarding the development of the NEII Conformance Framework. Conformance means following established guidelines, specifications and standards or working towards them. A conformance framework is a structured set of guidelines that detail the levels at which a participant provides services that comply with the NEII architecture. The NEII Conformance Framework has been developed to (a) enable partners to set priorities against specific expectations that will jointly lead to developing a sustainable federated environmental information system; (b) enable users of the NEII to rapidly assess the suitability of data for their business needs; and (c) provide a common measurement system to monitor progress in the development of the NEII. The framework adopts a capability maturity model approach to describe the steady improvement required for NEII nodes to progress from a basic data release (experimental data service with limited operational support) to an enduring federated system providing environmental information. The design of the model is informed by other maturity-based approaches for open data such as the Open Data Institute's certification approach (Open Data Institute 2015). The paper also presents a worked example of the conformance framework against the Bureau of Meteorology's Australian Hydrologic Geospatial Fabric NEII data services. The paper concludes with an overview of major learnings from the programme to-date. These relate primarily to reducing the cost of participation in NEII by data custodians given the challenge of delivering data using NEII standards; and developing approaches to better support users to use the portfolio of NEII data given some of the methods of data delivery may be new to non-technical users. The NEII programme has prioritised both these and developed work packages to achieve improvements.

WikiPathways (wikipathways.org) captures the collective knowledge represented in biological pathways. By providing a database in a curated, machine readable way, omics data analysis and visualization is enabled. WikiPathways and other pathway databases are used to analyze experimental data by research groups in many fields. Due to the open and collaborative nature of the WikiPathways platform, our content keeps growing and is getting more accurate, making WikiPathways a reliable and rich pathway database. Previously, however, the focus was primarily on genes and proteins, leaving many metabolites with only limited annotation. Recent curation efforts focused on improving the annotation of metabolism and metabolic pathways by associating unmapped metabolites with database identifiers and providing more detailed interaction knowledge. Here, we report the outcomes of the continued growth and curation efforts, such as a doubling of the number of annotated metabolite nodes in WikiPathways. Furthermore, we introduce an OpenAPI documentation of our web services and the FAIR (Findable, Accessible, Interoperable and Reusable) annotation of resources to increase the interoperability of the knowledge encoded in these pathways and experimental omics data. New search options, monthly downloads, more links to metabolite databases, and new portals make pathway knowledge more effortlessly accessible to individual researchers and research communities.

The International GNSS Service provides data and products to support a wide range of global, multidisciplinary scientific research. The service has established a hierarchy of components to facilitate its mission: a globally distributed network of Tracking Stations, Data Centers, Analysis Centers, a Central Bureau, and a Governing Board. The Data Centers, in conjunction with the Central Bureau, serve as the primary means of distributing GNSS data, products, and general information to the user community through ftp and Web servers and email services. The requirements of analysis centers and the scientific community have evolved over the lifetime of the IGS, requiring enhancement and extension of the supporting data center infrastructure. The diversity of IGS data and products extends today from the realm of the real-time and near real-time to the long-term archive and thus forms a basis for multidisciplinary research spanning decades. Reliability of all components is a key requirement within the IGS and is accomplished through the geographic distribution of data centers and the creation of independent, redundant, parallel channels for the transmission of data and products. We discuss the development of the IGS data infrastructure, current status, and plans for future enhancements. Descriptions of IGS data and products and associated metadata are also included.

[full article, abstract in English; abstract in Lithuanian] The purpose of this paper is to develop an understanding of factors that affect the respondents to drop out of an already started survey on research data management. We decided to take a questionnaire on data management survey at Vilnius University and Oulu University implemented in 2017 as a case study. The data for the analysis was collected using the questionnaire, which was used in multinational research for Data Literacy and Research Data Management, performed by a group of researchers in more than ten countries, initiated by Serap Kurbanoğlu and Joumana Boustany. This paper describes the analysis of 1 185 survey samples, of which 515 were unfinished and 670 finished in both universities. For the analysis of the data, we used Framework for Web Survey Participation created by Andy Peytchev (2009). The collected data was analyzed using IBM SPSS Statistics ver. 19 with descriptive and inferential statistical tests. The most significant factors on deciding not to finish the survey were the length of the survey, the scientific field, experience, age and the topic of the survey. No statistically significant difference was measured between those who finished the survey and unfinished evaluating the data by gender and job position. An important factor in not finishing the survey was the design of the survey.

Variation in Results of Three Biology‐Focused Search Engines: A Case Study Using North American Tree Species

Hepatic metastasis from colorectal cancer (mCRC) is best treated with a multidisciplinary approach. Conflicting data exist regarding the impact of preoperative chemotherapy on morbidity and mortality after hepatectomy. We hypothesized that preoperative chemotherapy does not adversely impact complications or mortality associated with hepatectomy. A retrospective analysis was performed and included patients with mCRC who underwent hepatectomy from 1996 to 2006. Patients were separated into two groups: those who received preoperative chemotherapy and those who did not. Univariate and multivariate analyses were performed to determine the factors associated with morbidity and mortality. Kaplan-Meier analyses were performed to determine disease-free survival (DFS) and overall survival (OS). One hundred eighty-six patients were analyzed: 112 (60%) received preoperative chemotherapy for a median of 4.2 months. Eighty patients (43%) underwent major hepatectomy. When comparing the two groups, there were no differences in hepatic tumor size (median 3 cm; p = 0.35), type of resection (p = 0.62), stage (p = 0.44) or location (p = 0.10) of the primary tumor, preoperative carcinoembryonic antigen (CEA) level (p = 0.80), or number of nodes in lymphadenectomy (p = 0.62). Only number of positive nodes after colectomy (p = 0.02), age (p < or = 0.0001), and combined resection/radiofrequency ablation (RFA) (p = 0.004) were statistically different between the two groups. There was no difference in rates of morbidity (p = 0.81), mortality (p = 0.29), DFS (p = 0.25) or OS (p = 0.30). We conclude that the use of preoperative chemotherapy did not increase the risk of complications or death for patients undergoing hepatectomy for metastatic colorectal cancer. Pre-hepatectomy chemotherapy appears to be safe and is an important part of the multidisciplinary approach for this disease.