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Includes bibliographical references.

Includes bibliographical references (leaves 130-135), The problem definition examined in this thesis is how employee share incentive schemes are taxed in the hands of employers and employees. This involved an analysis of the new section 8B and 8C of the Income Tax Act ("the Act") as well as the old section 8A. Sections 10, 1 1 (a), 56 and the Fourth, Seventh and Eighth Schedules to the Act were also studied. Case law was considered where applicable. Other areas that were investigated include the impact of IFRS 2 on employee share incentive schemes, the requirements of the Companies Act, the JSE Listing Requirements and Corporate Governance Guidance. Conclusions are drawn and recommendations include: • the amendment of certain sections of the Act (including making provision for the deductibility of expenses incurred and settled by way of issuing shares); • the issue of guidelines by SARS with relation to the taxation of share incentive schemes and the interaction between section 8C and the Eighth Schedule; • the introduction of a wider selection of "approved" employee share incentive schemes in the line of section 8B to fit the different needs of companies; • the alignment of the Income Tax Act with the Broad-Based Black Economic Empowerment Act; and • the availability of public information on employee share incentive schemes.

Contemporary research – particularly when addressing the most significant, transdisciplinary research challenges – cannot effectively be done without a range of skills relating to data. This includes the principles and practice of Open Science and research data management and curation, the use of a range of data platforms and infrastructures, large scale analysis, statistics, visualisation and modelling techniques, software development and annotation. A Task Group has been set up to address the critical need for skills around data science, research data management, and open science practice by pulling together existing and emerging initiatives and resources into a sustainably run, globally applicable foundational programme in Research Data Science, particularly for Low and Middle Income Countries (LMICs). The approach for this is to run a series of Schools which typically run for two weeks that provide a foundation in the wide range of topics described above to Early Career Researchers (ECR’s), with a focus on supporting those from LMIC’s. Since August 2016 these schools have run in a variety of locations, namely Trieste, Italy; Sao Paulo, Brazil; Brisbane, Australia and Kigali, Rwanda. We have delivered the curriculum to approximately 250 students from over 30 countries in a wide variety of disciplines (e.g. Bioinformatics, Earth, Atmospheric and Climate Sciences, Economics and Physics). The school covers a wide variety of technical topics such as Programming, Machine Learning and Cloud Computing but also spends a significant amount of time on more social areas such as Research Data Management and Authorship in the 21st century. Open Science in particular is delivered in a way that is both implicit and explicit. Implicit in terms of the teaching of topics that enable Open Science, such as sharing tools such as Git, the use of Persistent Identifiers and the management of data in general, and explicit in terms of running specific module on Open Science. This module, however, does not take a didactic “you must do Open Science” approach as many ECR’s are often isolated and without support of such approaches. The module is based on asking the students to reflect on aspects of Open Science, particularly from an ethical standpoint and hence to enable these students to approach Open Science in a more robust fashion, determining what practices they can adopt and the benefits they will gain. This ethical approach is amplified by the use of exercises throughout the school which relate this ethical aspect of Open Science with the specific module being taught. The impact of this approach has been exemplified by the comments on how useful the Open Science aspects have been to them and have spreading the practices in their home institutions.

Contemporary research – particularly when addressing the most significant, transdisciplinary research challenges – cannot effectively be done without a range of skills relating to data. This includes the principles and practice of Open Science and research data management and curation, the use of a range of data platforms and infrastructures, large scale analysis, statistics, visualisation and modelling techniques, software development and annotation. A Task Group has been set up to address the critical need for skills around data science, research data management, and open science practice by pulling together existing and emerging initiatives and resources into a sustainably run, globally applicable foundational programme in Research Data Science, particularly for Low and Middle Income Countries (LMICs). The approach for this is to run a series of Schools which typically run for two weeks that provide a foundation in the wide range of topics described above to Early Career Researchers (ECR’s), with a focus on supporting those from LMIC’s. Since August 2016 these schools have run in a variety of locations, namely Trieste, Italy; Sao Paulo, Brazil; Brisbane, Australia and Kigali, Rwanda. We have delivered the curriculum to approximately 250 students from over 30 countries in a wide variety of disciplines (e.g. Bioinformatics, Earth, Atmospheric and Climate Sciences, Economics and Physics). The school covers a wide variety of technical topics such as Programming, Machine Learning and Cloud Computing but also spends a significant amount of time on more social areas such as Research Data Management and Authorship in the 21st century. Open Science in particular is delivered in a way that is both implicit and explicit. Implicit in terms of the teaching of topics that enable Open Science, such as sharing tools such as Git, the use of Persistent Identifiers and the management of data in general, and explicit in terms of running specific module on Open Science. This module, however, does not take a didactic “you must do Open Science” approach as many ECR’s are often isolated and without support of such approaches. The module is based on asking the students to reflect on aspects of Open Science, particularly from an ethical standpoint and hence to enable these students to approach Open Science in a more robust fashion, determining what practices they can adopt and the benefits they will gain. This ethical approach is amplified by the use of exercises throughout the school which relate this ethical aspect of Open Science with the specific module being taught. The impact of this approach has been exemplified by the comments on how useful the Open Science aspects have been to them and have spreading the practices in their home institutions.

Contemporary research – particularly when addressing the most significant, transdisciplinary research challenges – cannot effectively be done without a range of skills relating to data. This includes the principles and practice of Open Science and research data management and curation, the use of a range of data platforms and infrastructures, large scale analysis, statistics, visualisation and modelling techniques, software development and annotation. A Task Group has been set up to address the critical need for skills around data science, research data management, and open science practice by pulling together existing and emerging initiatives and resources into a sustainably run, globally applicable foundational programme in Research Data Science, particularly for Low and Middle Income Countries (LMICs). The approach for this is to run a series of Schools which typically run for two weeks that provide a foundation in the wide range of topics described above to Early Career Researchers (ECR’s), with a focus on supporting those from LMIC’s. Since August 2016 these schools have run in a variety of locations, namely Trieste, Italy; Sao Paulo, Brazil; Brisbane, Australia and Kigali, Rwanda. We have delivered the curriculum to approximately 250 students from over 30 countries in a wide variety of disciplines (e.g. Bioinformatics, Earth, Atmospheric and Climate Sciences, Economics and Physics). The school covers a wide variety of technical topics such as Programming, Machine Learning and Cloud Computing but also spends a significant amount of time on more social areas such as Research Data Management and Authorship in the 21st century. Open Science in particular is delivered in a way that is both implicit and explicit. Implicit in terms of the teaching of topics that enable Open Science, such as sharing tools such as Git, the use of Persistent Identifiers and the management of data in general, and explicit in terms of running specific module on Open Science. This module, however, does not take a didactic “you must do Open Science” approach as many ECR’s are often isolated and without support of such approaches. The module is based on asking the students to reflect on aspects of Open Science, particularly from an ethical standpoint and hence to enable these students to approach Open Science in a more robust fashion, determining what practices they can adopt and the benefits they will gain. This ethical approach is amplified by the use of exercises throughout the school which relate this ethical aspect of Open Science with the specific module being taught. The impact of this approach has been exemplified by the comments on how useful the Open Science aspects have been to them and have spreading the practices in their home institutions.

The RDA-SHARC (SHAring Reward & Credit) interest group is an interdisciplinary volunteer member-based group set up as part of RDA (Research Data Alliance) to unpack and improve crediting and rewarding mechanisms in the sharing process throughout the data life cycle. Background and objectives of this group are reported here. Notably, one of the objectives is to promote the inclusion of data sharing activities in the research (& researchers) assessment scheme at national and European levels. To this aim, the RDA-SHARC-IG is developing two assessment grids using criteria to establish if data are compliant to the F.A.I.R principles (findable /accessible / interoperable / reusable) based on previous works on FAIR data management (Reymonet et al., 2018; Wilkinson et al., 2018; and E.U.Guidelines*): 1/ The self-assessment grid to be used by a scientist as a ‘checklist’ to identify her/his own activities and to pinpoint the hurdles that hinder efficient sharing and reuse of his/her data by all potential users. 2/ The two-level grid (quick/extensive) to be used by the evaluator to assess the quality of the researcher/scientist sharing practice, over a given period, taking into account the means & support available over that period. Assessment criteria are classified according their importance with regards to FAIRness (essential / recommended / desirable) meanwhile good practices are recommended for critical steps. To implement a highly fair assessment of the sharing process, appropriate criteria must be selected in order to design optimal generic assessment grids. This process requires participation, time and input from volunteer scientists data producers/users from various fields.

This deliverable lists the types of the data produced in the RDA TIGER project via the RDA TIGER personnel. The data storage and management solutions, and their role in the data lifetime are discussed. The DMP also includes information on the project and partner allocation of resources, data security, ethics and exploitation.

MA (Communication), North-West University, Potchefstroom Campus Limited research currently exists on the use of WhatsApp as an internal two-way symmetrical communication platform that can be used to manage relationships between organisations and internal stakeholders. In order to determine how WhatsApp can be used to manage relationships between internal stakeholders, the NWU’s student welfare organisation (SRCS) was used as a case study. Students usually serve on the NWU’s student-driven community service project, SRCS, for a year only, thus indicating a lack of sufficient relationship management within the structure. This causes the structure to become unstable and unsustainable. The SRCS should thus manage their relationships with internal stakeholders in order to ensure that the structure remains stable and sustainable. Data was gathered through theoretical exploration, observation and content analysis, focus groups and semi-structured interviews. It was concluded that WhatsApp can be used by the SRCS to manage the relationships with their internal stakeholders through the various features and capabilities of WhatsApp, combined with the four relationship management strategies (disclosure, usefulness of information, interactivity and involvement, and dialogue). The use of the relationship management strategies should also lead to the relationship outcomes (trust, control mutuality, commitment, relationship satisfaction), ensuring that the relationship is being successfully managed, which then leads to the sustainability and stability of the SRCS. These results demonstrate that it is the interactive and participatory characteristics of WhatsApp that allow two-way symmetrical communication to take place without much effort. The focus should thus be placed on communicating strategically on WhatsApp. Masters