Automated Author ProfileFaust, Christina
0000-0002-8824-7424
Faust, Christina
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 8.8 (sum of 5 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The data provided here are model iteration objects and rasters needed to run the multi-scale modelling process and predict how the host condition affects probability of Hendra virus shedding.The dataset contains predictions of three proxies for host conditions (including food shortage, rehabilitation admissions and formation of a new roost) across eastern Australia in 2008-2019. The Roost Species Distribution Model (SDM) has predictions of roost suitability. These are monthly, spatially explicit predictions of particular conditions or probability of roost occupations. The model objects are iterations of models that were initially trained on data held in figshare (https://figshare.com/s/ddb5a1584609b20f6596).These data objects are linked with code provided at https://github.com/hanlab-ecol/BatOneHealth to be able to run the models and analyses. This includes comparisons of virus predictions of seven different multiscale model structures to observed Hendra virus shedding in field surveys. The purpose of this study was to determine if quantifying and incorporating host condition into epidemiological models improves predictions of virus shedding in space and time. The data objects relate to the 1,000 iterations run of this process to better able to account for uncertainty.
Authors
- Kramer, A.M. ;
- Faust, C.L. ;
- Castellanos, A.A. ;
- Fischhoff, I.R. ;
- Peel, A.J. ;
- Eby, P. ;
- Ruiz-Aravena, M. ;
- Borremans, B. ;
- Plowright, R.K. ;
- Han, B.A.
Full-text screening of 269 articles which have been retained after title/abstract screening of 2,994 records. This dataset contains: I) Articles included after full-text screening and extracted data; II) Articles excluded after full-text screening and reasons for exclusion; III) Description of each data extraction column and list of abbreviations; IV) Tables summarising the extracted data.
Authors
- Catalano, Stefano ;
- Faust, Christina
Snail species, abundance and physicochemical water factors by contact site and season.
Authors
- Trienekens, Suzan ;
- Faust, Christina ;
- Besigye, Fred ;
- Pickering, Lucy ;
- Tukahebwa, Edridah ;
- Seeley, Janet ;
- Lamberton, Poppy
Time to reinfection and microsatellite data for epidemiological and genetic analyses
Authors
- Trienekens, Suzan ;
- Faust, Christina ;
- Meginnis, Keila ;
- Pickering, Lucy ;
- Ericsson, Olivia ;
- Nankasi, Andrina ;
- Moses, Arinaitwe ;
- Tukahebwa, Edridah ;
- Lamberton, Poppy
Dataset to underpin associated article.
Authors
- Faust, Christina ;
- Crotti, Marco ;
- Moses, A ;
- Ogutto, D ;
- Wamboko, A ;
- Adriko, M ;
- Adekanle, E ;
- Kabatereine, N ;
- Tukahebwa, E ;
- Norton, A ;
- Gower, C ;
- Webster, J ;
- Lamberton, Poppy