Reuse‐oriented data publishing: How to make the shared research data friendlier for researchers

The real-time system of the JT-60 has a cascade structure with the major and the minor control loops. The multi-variable feedback control is adopted in the major control loop in order to adjust non-linear effects which will appear in the high-/beta/region. By applying the concept of 'phase shift control' to the control program of the major control loop, a flexible tokamak discharge control is expected to be performed with taking account of machine protection.

This essay identifies and analyses the life cycle trend of the department store industry in Hong Kong. The main research question is whether its development follows a pattern that can be explained by retail life cycle theory. The tracing of its life cycle trend line is the main contribution. Methodologically, the trend line is plotted on the basis of triangulating a number of quantitative and qualitative data sets. Quantitative data sets used include time series longitudinal data constructed from government statistics, while qualitative data consist of historical data from archival documents, and semi-structured interviews of retailing professionals. The significant findings are that Hong Kong's department store industry approximates the pattern described in retail life cycle theory and that it has in recent years entered a stage of maturity.

Biodiversity research, conservation practices, natural resource management, and scientific deci- sion-making increasingly depend on the sharing and integration of large amounts of primary data. In recent years, there has been an appeal increased sharing of biodiversity data, however, many scientists actively or passively resist sharing data. Some major cultural and technological obstacles exist among scientists, such as keeping data private to conduct other analyses, conflicts of interests with colleagues, lack of benefits, unfa- miliarity with public databases, lack of user-friendly data submission tools, and lack of time and funding. One solution to improve the culture of data sharing is to provide benefits to scientists who share data (e.g. data citations). Recently, some organizations and scientists have advocated data publishing under peer review as a reward mechanism for individuals involved in data creation, management and sharing, and as a way to effectively increase the use and reuse of data. New data journals have been launched to fulfill the function of publishing data. In fact, besides the advocate of scholarly publication of data, an improved joint data archiv- ing policy by databases and scientific journals may be more practically feasible to improve data sharing in a broader sense. In this article we review recent progress in data sharing and publishing and discuss to what extent data papers can boost data sharing and how to define 'data sharing' and 'data publishing'. We also provide suggestions for improving data sharing by individual scientists, data repositories, journals, and fund- ing agencies/institutions.

In the current era of systems biology research, there is a need for the integrative analysis of binary and quantitative genomics data sets measured on the same objects. One standard tool of exploring the underlying dependence structure present in multiple quantitative data sets is the simultaneous component analysis (SCA) model. However, it does not have any provisions when a part of the data are binary. To this end, we propose the generalized SCA (GSCA) model, which takes into account the distinct mathematical properties of binary and quantitative measurements in the maximum likelihood framework. Like in the SCA model, a common low‐dimensional subspace is assumed to represent the shared information between these two distinct types of measurements. To achieve a low rank solution, we propose to use a concave variant of the nuclear norm penalty. An efficient majorization algorithm is developed to fit this model with different concave penalties. Realistic simulations (low signal‐to‐noise ratio and highly imbalanced binary data) are used to evaluate the performance of the proposed model in recovering the underlying structure. Also, a missing value based cross‐validation procedure is implemented for model selection. We illustrate the usefulness of the GSCA model for exploratory data analysis of quantitative gene expression and binary copy number aberration measurements obtained from the GDSC1000 data sets.

Report on the Barbados Workshop

This report is an evaluation of the existing data on radiological background for soils in the vicinity of the Hanford Site. The primary purpose of this report is to assess the adequacy of the existing data to serve as a radiological background baseline for use in environmental restoration and remediation activities at the Hanford Site. The soil background data compiled and evaluated in this report were collected by the Pacific Northwest Laboratory (PNL) and Washington State Department of Health (DOH) radiation surveillance programs in southeastern Washington. These two programs provide the largest well-documented, quantitative data sets available to evaluate background conditions at the Hanford Site. The data quality objectives (DQOs) considered in this evaluation include the amount of data, number of sampling localities, spatial coverage, number and types of radionuclides reported, frequency of reporting, documentation and traceability of sampling and laboratory methods used, and comparability between sets of data. Although other data on soil radionuclide abundances around the Hanford Site exist, they are generally limited in scope and lack the DQOs necessary for consideration with the PNL and DOH data sets. Collectively, these two sources provide data on the activities of 25 radionuclides and four other parameters (gross alpha, gross beta, total uranium, and total thorium). These measurements were made on samples from the upper 2.5 cm of soil at over 70 localities within the region.

Research Summary Knowledge accumulation requires that we understand whether and when relationships identified in any research setting generalize to others—that is, suggesting domains where results hold (or not). Strategy scholars carefully identify how theoretical mechanisms operate in their chosen research contexts, but attend less to whether their findings apply in other contexts. Accordingly, we recommend reframing empirical contexts, typically described in terms of nominal categories (e.g., industries, countries, or time periods), by highlighting contextual attributes of the nominal settings (e.g., industry concentration or technological modularity), which in turn reflect more abstract conceptual categories (e.g., uncertainty, interdependence, and variance) across potential research contexts. This approach can help integrate prior findings and suggest future study contexts to better enhance our understanding of the research landscape. Managerial Summary Academic research in strategic management tends to derive findings in very specific nominal settings—particular industries, years, and regions. Since strategy practitioners operate across a wide variety of industries and regions, they need to assess whether available research findings are applicable in their own settings. We suggest that understanding whether and when research findings apply to unstudied settings can be facilitated by categorizing research settings using more abstract conceptual constructs (such as environment uncertainty, variance across firms, or interdependence between firms), rather than by the traditional emphasis on nominal settings. We discuss a variety of research setting attributes (such as industry concentration and technological modularity) that can aid the translation of extant research findings to settings where practitioners are operating.

Efforts have been made to improve gender diversity in the built environment professions. This sector continues to experience growth restrictions due to skill shortages. Some progress has been made at the entry level; however, the retention of women in professional roles within this sector continues to present a challenge. Support for women during maternity, upon return to work and while working with family has been identified as a barrier to women’s progression in this sector. Professional bodies are uniquely placed to support their female members. Therefore, this study had two aims: to examine current professional body support for members during and after maternity leave and to examine the experiences of female professionals, who have taken maternity leave whilst holding membership in a built environment professional body. This study adopted an embedded, mixed-method design, using a secondary, qualitative data set to augment the primary, quantitative data set, within one phase. Surveys were distributed directly to professional bodies and to professionals via social media (LinkedIn). The survey of the professional bodies identified a varied approach to maternity support. The professional member survey identified a lack of awareness of available support, and generally the need for more support during maternity and on return to work. In summary, professional bodies in the built environment could improve maternity support for members by improving communication and achieving parity in support across the various disciplines.

Purpose While many studies have discussed the regulatory constraints that hinder industrial development in sub-Saharan Africa, little attention has been paid to the behavior of those firms that succeed despite a challenging business environment. The purpose of this paper is to fill this gap by focusing on specific strategies of a subset of successful industrial firms in Kenya. Design/methodology/approach The paper draws on two data sets. First, a quantitative data set based on a survey of food processing firms provides an overall profile of the sub-sector and the strategies employed by successful Kenyan firms. Second, qualitative in-depth case studies unpack the concept of strategy from the perspective of the firm, with the aim of showing the links between vision and strategy and the adaptive nature of firm strategy. Findings The quantitative data set reveals that the most important strategies used by agri-processing firms are differentiation strategies (selling at a premium), cost reduction strategies and niche strategies. A second major finding, based on the case study interviews, is that Kenyan firms adopt a combination of strategies to cope with the volatile business environment and grow their market. Furthermore, the qualitative interviews reveal that the vision of the leader is linked to firm strategy and firms follow an adaptive approach to strategy development. Originality/value The paper’s original contribution is the conclusion that while the existing typologies of strategy were acknowledged by respondents, their actual strategies were composites resulting from adaptive strategy development. This conclusion was made possible by the paper’s mixed methods approach.

Problem learners in Ghanaian schools are often marginalized and/or ignored during the teaching and learning processes in the classroom. The study was carried out using the convergent parallel mixed method design with questionnaire, focus group discussion, and one-on-one interviews as the data collection instruments. The purposive, convenient, and stratified random sampling techniques were used in selecting a total sample size of 112 participants consisting of identified problem learners, teachers, parents, heads of institutions, and experts in childhood learning and development. The quantitative data set were analyzed using the descriptive statistics while the qualitative data set were analyzed using the data analysis spiral. The findings revealed that the main causes of the learning and retention challenges among problem learners are more of environmental factors than the perceived hereditary and spiritual factors. It is recommended that the Ministry of Education should organize sensitization education workshops and seminars for teachers, heads of institutions, and the student body in the elementary schools in Ghana aimed at fostering an understanding of the conditions of problem learners. This would help eradicate the stigmatization against problem learners while equipping the teachers with the pragmatic strategies to improve their learning outcomes.

Today, as data sets used in computations grow in size and complexity, the technologies developed over the years to deal with scientific data sets have become less efficient and effective. Many frequently used operations, such as eigenvector computation, could quickly exhaust our desktop workstations once the data size reaches certain limits. On the other hand, the high-dimensional data sets we collect every day don't relieve the problem. Many conventional metric designs that build on quantitative or categorical data sets cannot be applied directly to heterogeneous data sets with multiple data types. While building new machines with more resources might conquer the data size problems, the complexity of today's computations requires a new breed of projection techniques to support analysis of the data and verification of the results. We introduce the concept of a data signature, which captures the essence of a scientific data set in a compact format, and use it to conduct analysis as if using the original. A time-dependent climate simulation data set demonstrates our approach and presents the results.

As a national library, the British Library aims to deliver valuable services to both individual researchers and other research institutions, including those recognised by UK Research and Innovation as ‘Independent Research Organisations’. The British Library’s Scholarly Communications Toolkit is a series of ‘quick start’ guides to important elements of scholarly communications, including open access, research data management, publishing, research impact, and copyright. While addressing internal needs, they have been developed to also meet the needs of external organisations that may not have in-house support for scholarly communications. Being openly licensed and published in multiple formats, they are easily adapted to different contexts of use. This poster outlines the British Library’s approach to supporting scholarly communications both internally and externally, and introduces the series of leaflets which will be publicly available by the date of the 2020 Munin Conference on Scholarly Publishing.

PurposeThe study aims to investigate the utilisation of open research data repositories (RDRs) for storing and sharing research data in higher learning institutions (HLIs) in Tanzania.Design/methodology/approachA survey research design was employed to collect data from postgraduate students at the Nelson Mandela African Institution of Science and Technology (NM-AIST) in Arusha, Tanzania. The data were collected and analysed quantitatively and qualitatively. A census sampling technique was employed to select the sample size for this study. The quantitative data were analysed using the Statistical Package for the Social Sciences (SPSS), whilst the qualitative data were analysed thematically.FindingsLess than half of the respondents were aware of and were using open RDRs, including Zenodo, DataVerse, Dryad, OMERO, GitHub and Mendeley data repositories. More than half of the respondents were not willing to share research data and cited a lack of ownership after storing their research data in most of the open RDRs and data security. HILs need to conduct training on using trusted repositories and motivate postgraduate students to utilise open repositories (ORs). The challenges for underutilisation of open RDRs were a lack of policies governing the storage and sharing of research data and grant constraints.Originality/valueResearch data storage and sharing are of great interest to researchers in HILs to inform them to implement open RDRs to support these researchers. Open RDRs increase visibility within HILs and reduce research data loss, and research works will be cited and used publicly. This paper identifies the potential for additional studies focussed on this area.

Now a day’s Data publishing process is the compulsory for visualizing the data sets to other parties. But in publishing process there is thread for data set owners by disclosing the sensitive information. For avoiding those problems we using anonymization techniques for secure data publishing. Most Anonymization techniques such as Generalization and Bucketization are using now. But these anonymization techniques have some limitation. Generalization for k-anonymity losses considerable amount of information for high- dimensional data and Bucketization does not prevent membership disclosure, because bucketization publishes the QI values in their original forms, It requires a clear separation between QI’s and SA’s, but in many data sets, it is unclear which attributes are QI’s and which are SA’s and it breaks the attributes correlations between the QI’s and the SA’s. This paper introduces new anonymization technique slicing to overcome all the drawbacks of bucketization and generalization Keywords: - Sensitive information, High dimensional data, data anonymization, data publishing, data security, generalization and bucketization.

BackgroundClinical and analytical information on laboratory data of neonates in scientific publications is sparse and incomplete. Furthermore, interpreting neonatal laboratory data can be complex due to their time-dependent and developmental physiology, and paucity of well-established age-appropriate reference ranges for neonates. This study aims to develop publication recommendations to report laboratory data of neonates to enhance the quality of these data in research and clinical care.MethodsA modified Delphi approach was used to develop recommendations in cooperation with the International Neonatal Consortium. A Core Group, including different stakeholders, was responsible for developing the recommendations, in collaboration with a Reflection Group, responsible for providing additional input.ResultsThe recommendations were classified into three categories: ‘Clinical Characteristics’, ‘Bio-analytical Information’ and ‘Data-analytical Information’. These were each divided into ‘Core Data’ (always to be reported) and ‘Supplemental Considerations’ (to be reported when considered relevant to the study).ConclusionOur recommendations provide guidance on standardization of neonatal laboratory data in publications. This will enhance the comparison, replication, and application of study results in research initiatives and clinical practice. Furthermore, these recommendations also serve as foundational work to develop reference ranges for neonatal laboratory values by standardizing the quality of information needed for such efforts.ImpactStandardized reporting of neonatal laboratory data in scientific publications will enhance the comparison, replication, and application of study results in research initiatives and clinical practice, as well as improve reporting to regulatory agencies.To integrate multistakeholder perspectives, a modified Delphi approach was used to develop publication recommendations which strengthens the applicability of the recommendations.Implementation of standardization will likely improve the overall quality of neonatal clinical research and neonatal healthcare.In addition, these recommendations are foundational to develop reference ranges for neonatal laboratory values by standardizing the quality of information needed for such efforts.