• 2020-2024close
• Open Access close
• Open Source close
• Publications close
• Research software close
• Other research products close
• Other ORP type close
• AU close

Altered microclimatic conditions and higher disturbance at forest edges create environmental stress and modify resource gradients from edge to interior, changing the selection pressures acting on individuals. Although community-weighted trait-mean (CWM) shifts along edge gradients have been widely documented at the species level, it is unclear how edge effects act at the individual level, and whether the direction of intraspecific trait shifts mirrors that of CWM shifts in response to edge effects. On 20 islands in the Thousand Island Lake, China, we established 484 plots (2×2 m) in a stratified random design across distances of 0 – 128 m from the forest edge. Within each plot, we sampled leaves (n=34,768) from within and among all 2,993 individuals of 68 species and measured five leaf traits (leaf area, LA; specific leaf area, SLA; leaf dry matter content, LDMC; thickness, LT; chlorophyll content, LCC). Using generalized linear mixed models, we found that different leaf traits exhibited contrasting shifts in inter- vs. intraspecific trait variation in response to edge effects. For SLA, LT, and LCC, negative covariance between inter- and intraspecific trait shifts resulted in dampening of community-wide trends compared to CWM response to edge effects. In contrast, the community-wide trend for LDMC was reinforced due to positive covariance between inter- and intraspecific trait shifts, while for LA the direction of covariance shifted from negative to positive on small vs. large islands. Together, edge effects alter selection regimes in reassembling plant communities. Predicting the community-wide consequences depends on the degree to which there is negative vs. positive covariance between species sorting and within-species adaptation. The widely-used CWM approach can mask contrasting trait selection pressures acting on individuals within local populations. Individual-level trait variation can improve understanding of community re-assembly trajectories in response to global environmental change. Funding provided by: National Natural Science Foundation of ChinaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809Award Number: 31870401Funding provided by: National Natural Science Foundation of ChinaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809Award Number: 31361123001Funding provided by: National Natural Science Foundation of ChinaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809Award Number: 31930073Funding provided by: University of Western AustraliaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001801Award Number: International Postgraduate Research ScholarshipFunding provided by: University of Western AustraliaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001801Award Number: Australian Postgraduate AwardFunding provided by: University of Western AustraliaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001801Award Number: Safety-net Top-up ScholarshipsFunding provided by: University of Western AustraliaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001801Award Number: Travel AwardFunding provided by: Commonwealth Scientific and Industrial Research OrganisationCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100000943Award Number: Julius Career Award

In April 2021, a group from the Open Knowledge Community (OKC) coalition drawn from the Curtin Open Knowledge Initiative (COKI) and members of the global research communication community came together in a virtual Sprint exercise to build and expand on Open Knowledge User Stories created during a virtual workshop in 2020. We identified a range of Open Knowledge persona representing open knowledge stakeholders that includes institutional consortia, higher education institutions (universities, research institutes, libraries), publishers and research funders. Sprint participants contributed expertise and experience as stakeholders, and we added user stories from two workshops some participants had attended. In developing the user stories we identified the types of data to meet the open knowledge information needs outlined by the stakeholders. We consolidated the data needs, grouped them into themes and identified availability in terms of: data analysis available from COKI now; COKI analysis coming soon; existing COKI analysis in development; and data needing work. This document provides a framework and outline of data needs for stakeholders in the open knowledge scholarly research community.

This study was the first to systematically map local government (LG) action on creating a healthy, sustainable, and equitable food system in Australia, focusing on NSW and Victoria. We searched the websites of all LGs in NSW and Victoria for policy documents with actions related to a healthy, sustainable, and equitable food system. We then analysed these documents against a framework of recommendations for LG action on addressing food system challenges. This infographic provides a visual representation of the study's findings.

This is an exploratory interview to collect information about the challenges experienced by multi-disciplinary data intensive software teams and how they deal with the challenges. Research Team: Ulrike Maria Graetsch (Monash University), Hourieh Khalajzadeh (Deakin University), Mojtaba Shahin (RMIT University), Rashina Hoda (Monash University), John Grundy (Monash University)

28 July 2022: Version two of the self-assessment tool was designed to be completed independently by researchers. An objective is to have Research Data Champions (RDC) complete the tool before and after participation and use score changes to demonstrate the effectiveness of the program. Accordingly, responses have been changed from anonymous to confidential to allow linking of data. The answers are now displayed in a random order for each question to encourage careful thought. The tool was also amended to include more detailed feedback using display logic to help RDCs understand their results. Each section is assigned a separate score in the survey flow to facilitate this. The feedback sections include links to suggested resources; these are also emailed to respondents in PDF form after they have completed the survey. *********** The Research Data Management (RDM) good practice self-assessment tool is developed by Curtin University Library for the Research Data Champion (RDC) Program. The program focuses on training, promotion and advocacy for research reproducibility; good research data management practice; data management planning; and FAIR principles. The self-assessment tool was developed to provide RDCs with a model of RDM good practice, which will help the RDCs to identify a path for further training and development. The tool will help the Program Lead to identify deep-dive training topics as well as to measure the effectiveness of the program. It was developed with the intention to complete as a group in a workshop setting. The tool is divided into 5 sections: general knowledge, organisation and retention, analysis and writing, data sharing, and data ethics. Each section contains statements that represent the behaviour or practice at various experience levels. The tool attempts to provide respondent practices at 4 levels of RDM experience: Newbie: I do not have a RDM strategy and do not know where to seek help. Amateur: I’m aware of the importance of a good RDM strategy but unsure how to develop one. I’m aware of the support available, but do not know who to ask. Pro: I have good understanding of RDM and have an effective RDM strategy in place for my own projects, and I know of people who can connect me with someone who can give advice. Hero: I’m able to adapt RDM strategies to different contexts and understand the implications of RDM on research outcomes; I’m well-connected with the right people who can give advice. The tool was developed using Qualtrics. For each question, there are 4 statements, each has a score from 1 to 4 representing the 4 experience levels: newbie, amateur, pro, and hero. At the end of the survey, a total score will be presented to the respondent. For areas that may not be relevant to all research, the “N/A” option is available. All questions are optional because respondents are guided by the facilitator in the workshop. The creators have made this tool available in 2 formats. QSF can be imported directly into Qualtrics and DOCX for editing outside of Qualtrics. Please note that the tool contains references to terminology and legislation specific to Curtin University and Western Australia and requires some amendments to be suitable for use elsewhere.