Linking Research Data Research Articles

Optimization of Enterprise Resource Planning (ERP) Applications on The Crew Contract Extension System At PT. Sinarmas LDA Maritime

Technological developments are needed in today's digital era because they can help all human activities. PT. Sinarmas LDA Maritime created an Enterprise Resource Planning (ERP) application. The ERP application is used to simplify employees' work in inputting data and monitoring the working period of the crew and the validity period of documents. However, in using the ERP application, there are obstacles to the crew contract extension system, which still uses paper forms, so inputting data into the application takes a long time. Besides that, the ERP application is very dependent on the internet network. This study aims to determine the constraints in optimizing Enterprise Resource Planning (ERP) applications on the crew contract extension system with the addition of problem solvers. This research method uses a qualitative description approach by conducting observations, interviews, literature studies, and documentation directed at competent and directly involved informants and using SWOT analysis techniques to determine strategic factors for optimizing Enterprise Resource Planning (ERP) applications by linking research data results. Based on the research that has been done, it was found that optimizing the Enterprise Resource Planning (ERP) application in the crew contract extension system is filling in crew data that still uses paper forms and is dependent on the internet network. Using a paper form is changed to using a G-form so that the input of crew data for extended counters is more effective and efficient. As well as the need for outreach to the ship's crew in order to know the stages of filling in the data. The results of the SWOT analysis show that the Enterprise Resource Planning (ERP) application is in a progressive strategy position, meaning that obstacles in optimizing ERP applications can be overcome because the company is able to take advantage of its strengths and opportunities.

Harmonized Research Information for Classifying and Linking Research Data

A well-described, harmonized in standard formats and linked metadata accelerates and refines the search process. Hence, researchers can ensure that their data is properly organized, easily discoverable, and accessible to others. Machine-readable metadata is especially essential for automatic discovery of research datasets and outputs. It facilitates the search and retrieval of data with increased accuracy, which saves the user time in finding crucial information within their field of research. This requires rich and categorized metadata that allows computers to automatically retrieve and sort relevant search results. Well documented metadata which is harmonized, standardized and serialized in linked data formats increases not only the accuracy of the search results within a field of research, but can also provide relevant and helpful suggestions within disciplinary and interdisciplinary fields of research.

How research data deliver non-academic impacts: A secondary analysis of UK Research Excellence Framework impact case studies.

This study investigates how research data contributes to non-academic impacts using a secondary analysis of high-scoring impact case studies from the UK's Research Excellence Framework (REF). A content analysis was conducted to identify patterns, linking research data and impact. The most prevalent type of research data-driven impact related to "practice" (45%), which included changing how professionals operate, changing organizational culture and improving workplace productivity or outcomes. The second most common category was "government impacts", including reducing government service costs and enhancing government effectiveness or efficiency. Impacts from research data were developed most frequently through "improved institutional processes or methods" (40%) and developing impact via pre-analyzed or curated information in reports (32%), followed by "analytic software or methods" (26%). The analysis found that research data on their own rarely generate impacts. Instead they require analysis, curation, product development or other forms of significant intervention to leverage broader non-academic impacts.

Open Metadata for Research Data Discovery in Canada

ABSTRACTThe potential for reusing research data is inextricably tied to how discoverable these data are to other researchers. Currently in Canada, cross-disciplinary discovery of research data is limited. This article discusses the processes followed and results achieved by the Portage Data Discovery Metadata Working Group in its efforts to support the development of the Federated Research Data Repository discovery service in Canada. Ideas around metadata standards, best practices for harvesting research data, developing common data models, and challenges associated with linking research data to other research outputs are explored.

Spatial discovery and the research library

AbstractAcademic libraries have always supported research across disciplines by integrating access to diverse contents and resources. They now have the opportunity to reinvent their role in facilitating interdisciplinary work by offering researchers new ways of sharing, curating, discovering, and linking research data. Spatial data and metadata support this process because location often integrates disciplinary perspectives, enabling researchers to make their own research data more discoverable, to discover data of other researchers, and to integrate data from multiple sources. The Center for Spatial Studies at the University of California, Santa Barbara (UCSB) and the UCSB Library are undertaking joint research to better enable the discovery of research data and publications. The research addresses the question of how to spatially enable data discovery in a setting that allows for mapping and analysis in a GIS while connecting the data to publications about them. It suggests a framework for an integrated data discovery mechanism and shows how publications may be linked to associated data sets exposed either directly or through metadata on Esri's Open Data platform. The results demonstrate a simple form of linking data to publications through spatially referenced metadata and persistent identifiers. This linking adds value to research products and increases their discoverability across disciplinary boundaries.

NHash: Randomized N-Gram Hashing for Distributed Generation of Validatable Unique Study Identifiers in Multicenter Research.

BackgroundA unique study identifier serves as a key for linking research data about a study subject without revealing protected health information in the identifier. While sufficient for single-site and limited-scale studies, the use of common unique study identifiers has several drawbacks for large multicenter studies, where thousands of research participants may be recruited from multiple sites. An important property of study identifiers is error tolerance (or validatable), in that inadvertent editing mistakes during their transmission and use will most likely result in invalid study identifiers.ObjectiveThis paper introduces a novel method called "Randomized N-gram Hashing (NHash)," for generating unique study identifiers in a distributed and validatable fashion, in multicenter research. NHash has a unique set of properties: (1) it is a pseudonym serving the purpose of linking research data about a study participant for research purposes; (2) it can be generated automatically in a completely distributed fashion with virtually no risk for identifier collision; (3) it incorporates a set of cryptographic hash functions based on N-grams, with a combination of additional encryption techniques such as a shift cipher; (d) it is validatable (error tolerant) in the sense that inadvertent edit errors will mostly result in invalid identifiers.MethodsNHash consists of 2 phases. First, an intermediate string using randomized N-gram hashing is generated. This string consists of a collection of N-gram hashes f 1, f 2, ..., f k. The input for each function f i has 3 components: a random number r, an integer n, and input data m. The result, f i(r, n, m), is an n-gram of m with a starting position s, which is computed as (r mod |m|), where |m| represents the length of m. The output for Step 1 is the concatenation of the sequence f 1(r 1, n 1, m 1), f 2(r 2, n 2, m 2), ..., f k(r k, n k, m k). In the second phase, the intermediate string generated in Phase 1 is encrypted using techniques such as shift cipher. The result of the encryption, concatenated with the random number r, is the final NHash study identifier.ResultsWe performed experiments using a large synthesized dataset comparing NHash with random strings, and demonstrated neglegible probability for collision. We implemented NHash for the Center for SUDEP Research (CSR), a National Institute for Neurological Disorders and Stroke-funded Center Without Walls for Collaborative Research in the Epilepsies. This multicenter collaboration involves 14 institutions across the United States and Europe, bringing together extensive and diverse expertise to understand sudden unexpected death in epilepsy patients (SUDEP).ConclusionsThe CSR Data Repository has successfully used NHash to link deidentified multimodal clinical data collected in participating CSR institutions, meeting all desired objectives of NHash.

Linking research data with scholarly publications

Motivation: in recent decades, a vast gulf has opened between the mass of accumulating research data and the massively expanding literature describing and analysing those data. The problem is not so much data generation per se, but rather, the way in which we’ve buried the knowledge embodied in those data: there is now so much information available that we simply no longer know what we know, and finding what we want is hard, because he knowledge we seek is often spread across thousands of databases and millions of articles in thousands of journals. The intellectual energy required to search this array of archives, and the time and money this wastes, has prompted the development of new software tools to help link these resources, and ultimately liberate the knowledge that’s been systematically trapped within them. Results: to address some of these issues, we have developed Utopia Documents. Building on Utopia, a suite of semantically integrated protein sequence/structure visualisation and analysis tools (1,2), Utopia Documents is a PDF reader that integrates Utopia’s functionality with research articles. The system was piloted in a project with Portland Press to create the Semantic Biochemical Journal (BJ) (3) – in this project, Utopia Documents was used to transform static document features into objects that can be linked, annotated, visualised and analysed interactively, thereby transforming the reading experience and making further analysis from within a PDF file possible for the first time. The Semantic BJ was launched in December 2009 (see www.biochemj. org/bj/424/3/), and Utopia Documents is now being used by BJ editors within their routine publication pipelines. With support from other publishers, and groups like SeqAhead, this new software could also make significant advances towards tighter coupling of NGS literature and data in future.

Use of digital repositories by chemistry researchers: results of a survey

PurposeThis paper aims to present findings from a survey that aimed to identify the issues around the use and linkage of source and output repositories and the chemistry researchers' expectations about their use.Design/methodology/approachThis survey was performed by means of an online questionnaire and structured interviews with academic and research staff in the field of chemistry. A total of 38 people took part in the online questionnaire survey and 17 participated in face‐to‐face interviews, accounting for 55 responses in total.FindingsMembers of academic and research staff in chemistry from institutions in the UK were, in general, favourably disposed towards the idea of linking research data and published research outputs, believing that this facility would be either a significant advantage or useful for the research conducted in the domain. Further information about the nature of the research that they conduct, the type of data that they produce, the sharing and availability of research data and the use and expectations of source and output repositories is also discussed.Research limitations/implicationsInterpretation of the results must recognise that the majority of the interviewees worked in the area of theoretical/computational chemistry and therefore their views may not be representative of other chemistry research fields.Originality/valueSuch data was essential for the business analysis that described the functional requirements for the development of the key deliverable of the source‐to‐output repositories (StORe) project, the pilot middleware, which aimed to facilitate and demonstrate the bi‐directional links between source and output repositories. It also enabled the identification of workflows in research practice and contributed to the prime aspiration of the StORe project which was to invest new value to the intellectual products of academic research.

Data bases for variations research.

particularly information that allows longitudinal follow-up of patients. Researchers and policy makers have recognized the potential of existing administrative and other large data bases for this purpose, and widespread interest in and use of these data have become more common. Examples of the current focus on large data base research beyond MEDTEP and the PORTs include: 1) the Health Care Financing Administration (HCFA) proposed requirement for peer review organizations to focus quality assurance efforts where large data base studies have shown significant variation'; 2) the 1989 Congress-mandated study by AHCPR on the feasibility of linking research data bases to Federal and non-Federal adminis-