Automated Author ProfileHead, Megan L
Head, Megan L
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: 6.3 (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 were analysed for the presence of selective reporting of non-significance and/or reverse P-hacking in the field of behavioural ecology.
On 1 February 2018, we searched for articles published between 1990 and 2018 in three leading behavioural ecology journals: Animal Behaviour, Behavioral Ecology, and Behavioral Ecology and Sociobiology, that had ‘experiment* AND control*’ in the ‘topic’ category in the ISI Web of Science database (1081 articles). We then searched these articles for tests of whether the mean value of confounding variables differed between treatment(s) and control groups where subjects were randomly assigned to groups. We only included confounding variables that involved measurements made on test subjects or groups of subjects (e.g. body mass, blood glucose, brood size). To identify papers with suitable data, we first read Abstract, Methods and Results to see if there was any indication that the study was likely to include tests for confounding variables differing between control and treatment groups (i.e. studies that involved experimental manipulation). We recorded: the name of the confounding variable, P-values or statistical significance statements associated with tests for a difference, the sample sizes.
Authors
- Vrtilek, Milan ;
- Chuard, Pierre ;
- Head, Megan L ;
- Jennions, Michael D.
The data were analysed for the presence of selective reporting of non-significance and/or reverse P-hacking in the field of behavioural ecology.
On 1 February 2018, we searched for articles published between 1990 and 2018 in three leading behavioural ecology journals: Animal Behaviour, Behavioral Ecology, and Behavioral Ecology and Sociobiology, that had ‘experiment* AND control*’ in the ‘topic’ category in the ISI Web of Science database (1081 articles). We then searched these articles for tests of whether the mean value of confounding variables differed between treatment(s) and control groups where subjects were randomly assigned to groups. We only included confounding variables that involved measurements made on test subjects or groups of subjects (e.g. body mass, blood glucose, brood size). To identify papers with suitable data, we first read Abstract, Methods and Results to see if there was any indication that the study was likely to include tests for confounding variables differing between control and treatment groups (i.e. studies that involved experimental manipulation). We recorded: the name of the confounding variable, P-values or statistical significance statements associated with tests for a difference, the sample sizes.
Authors
- Vrtilek, Milan ;
- Chuard, Pierre ;
- Head, Megan L ;
- Jennions, Michael D.
No description available
Authors
- Iglesias-Carrasco, Maider ;
- Fox, Rebecca J ;
- Vega-Trejo, Regina ;
- Jennions, Michael D ;
- Head, Megan L
No description available
Authors
- Marsh, Jason N ;
- Vega-Trejo, Regina ;
- Jennions, Michael Dawson ;
- Head, Megan L