Search

To enhance confidence in decision making, research administrators and funding agencies require insight into the performance of research-active institutions. Focusing on 42 Australian higher education providers and 236 fields of research, the Excellence in Research for Australia process (ERA) reports on research activity relative to local and global benchmarks. The ERA report is compiled for release every three to five years and uses a citation-focused methodology that depends on institutional self-reporting of research outputs. It is of interest to explore additional data sources and analysis methods to complement the ERA process. To facilitate this, the Curtin Open Knowledge Initiative (COKI) has constructed a pilot system, demonstrating the feasibility of conducting an on-demand, ERA-like analysis for research-active institutions (globally), using journal-level metadata from the ARC and article-level metadata from publicly available datasets. Given a sufficiently comprehensive dataset, containing output-affiliation links, output citation data, and the journal assignment that was planned for ERA 2023, we show that the COKI pilot system is able to generate ERA-like benchmarks and indicators, aligned with ERA 2018 methodology and proposed ERA 2023 methodology. Analysis is conducted for ANZSRC fields of research between the years 2011-2021 and includes calculation of dynamic RCI boundaries, the proposed high-performance indicator, and citation centiles. Determining the actual institutional scores, used to inform the citation-based ERA panels, is also feasible given a dataset comparable to that submitted by institutions for previous ERA rounds (containing outputs and FoR apportionments for each institution). We demonstrate this is possible in principle using open data on institutional affiliation of outputs (a ‘byline approach’), together with the ERA 2018 and ERA 2023 journal lists to assign outputs to FoRs. In a fully automated system this demonstration data would be replaced with either institutional submissions based on a census date, or an algorithmic FoR assignment process.

Additional file 2. Workflow summarising the methodological approach employed in this study to produce a DNA sequence resource.

Substantial experimental datasets defining the subcellular location of Arabidopsis (Arabidopsis thaliana) proteins have been reported in the literature in the form of organelle proteomes built from mass spectrometry data (approximately 2,500 proteins). Subcellular location for specific proteins has also been published based on imaging of chimeric fluorescent fusion proteins in intact cells (approximately 900 proteins). Further, the more diverse history of biochemical determination of subcellular location is stored in the entries of the Swiss-Prot database for the products of many Arabidopsis genes (approximately 1,800 proteins). Combined with the range of bioinformatic targeting prediction tools and comparative genomic analysis, these experimental datasets provide a powerful basis for defining the final location of proteins within the wide variety of subcellular structures present inside Arabidopsis cells. We have analyzed these published experimental and prediction data to answer a range of substantial questions facing researchers about the veracity of these approaches to determining protein location and their interrelatedness. We have merged these data to form the subcellular location database for Arabidopsis proteins (SUBA), providing an integrated understanding of protein location, encompassing the plastid, mitochondrion, peroxisome, nucleus, plasma membrane, endoplasmic reticulum, vacuole, Golgi, cytoskeleton structures, and cytosol (www.suba.bcs.uwa.edu.au). This includes data on more than 4,400 nonredundant Arabidopsis protein sequences. We also provide researchers with an online resource that may be used to query protein sets or protein families and determine whether predicted or experimental location data exist; to analyze the nature of contamination between published proteome sets; and/or for building theoretical subcellular proteomes in Arabidopsis using the latest experimental data.