The ORCID Researcher Advisory Council (ORAC) is a diverse group of researchers who provide valuable perspectives and advice to ORCID staff and the ORCID Board to ensure that ORCID provides value and utility to researchers and facilitates research and innovation.

stakeholders

TARGET INSTITUTIONS:● Public research performing organisations (RPOs): Higher Education Institutions andResearch organizations● Research funding organizations (RFOs): Ministry of Education, Youth and Sports, CzechScience Foundation, Technology Agency of the Czech Republic etc.● Policymakers: Ministry of Education, Youth and Sports; Research, Development andInnovation Council (R&D&I Council)● Libraries: National library, National Library of Technology, academic libraries● Publishers based in Czechia● Service providers, research infrastructuresTARGET GROUPS:● Researchers● Librarians● Open Science/Open Access managers/coordinators● CRIS system managers● Repository managers● Other research support positions, e.g. data stewards, data curators

CEAP products would have wide utility for diverse stakeholders within the conservation community. CEAP has evolved into an assessment and research initiative directed at determining not only the impacts of conservation practices, but also evaluating procedures to more effectively manage agricultural landscapes in order to address environmental quality goals at local, regional, and national scales (Maresch et al. 2008).

Please annotate how you have felt about the current situation with the Team-preneurship class here. I will compile your annotations into a document that I can present at the next organizational meeting. I would like our needs and feelings to be represented in this process. Thanks, guys. 

C. Gopinath and themembers of Cividep, who have been working with garment workers,and Sister Celia, who was instrumental in forming the first tradeunion of domestic workers in the country

One of the primary uses of a model like this one is to improve the conversation between stakeholders and managers. The model can be valuable in helping managers and citizens arrive at realistic goals and to realize that there will be inherent risks associated with meeting those goals. For example, our analysis shows that reducing the probability of transmission by one half in five years using vaccination is not likely when we include uncertainty in the ability of managers to treat a targeted number of seronegative females. Forecasts suggested that there was virtually no chance of meeting that goal (Table 12). Similarly there was a 7% chance of reducing adult female seroprevalence below 40% using vaccination. We can nonetheless use this work to articulate what level of brucellosis suppression is feasible given current technology. For example, managers and stakeholders might agree that it is enough to be moving in the right direction with efforts to reduce risk of infection from brucellosis. In this case, a reasonable goal might be “Reduce the probability of exposure by 10% relative to the current median value.” The odds of meeting that goal using vaccination increased to 26%. With this less ambitious goal, vaccination increases the probability that the goal would be met relative to no action by a factor of only 1.4. This illustrates a fundamental trade-off in making management choices in the face of uncertainty: less ambitious goals are more likely to be met, but they offer smaller improvements in the probability of obtaining the desired outcome relative to no action.

Application Modern higher education institutions have unprecedentedly large and detailed collections of data about their students, and are growing increasingly sophisticated in their ability to merge datasets from diverse sources. As a result, institutions have great opportunities to analyze and intervene on student performance and student learning. While there are many potential applications of student data analysis in the institutional context, we focus here on four approaches that cover a broad range of the most common activities: data-based enrollment management, admissions, and financial aid decisions; analytics to inform broad-based program or policy changes related to retention; early-alert systems focused on successful degree completion; and adaptive courseware.

who will (and will not) control and define the learning process, who will (and will not) profit from the ways that learning processes are enacted, who will (and will not) have access to science and scholarship and the infrastructure necessary for creating it, who will (and will not) participate in the design of curriculum and assessment and learning spaces, who will (and will not) profit from the benefits of science and artistry, and who will (and will not) have opportunities to attend schools and colleges.

ownership in educational systems

Data collection on students should be considered a joint venture, with all parties — students, parents, instructors, administrators — on the same page about how the information is being used.

The design of interactions is driven by user requirements and their impact on the choices made in the implementation process. It is constrained by resource and technical system properties and by social and legal requirements.