Automated Author Profile
Kulich, Clara
University of Geneva
0000-0002-9483-2128

Current S-Index

0.8

Sum of Dataset Indices for all datasets

Average Dataset Index per Dataset

0.4

Average Dataset Index per dataset

Total Datasets

Total datasets for this author

Average FAIR Score

15.4%

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: 0.8 (sum of 2 datasets Dataset Index scores)

More information here.

S-Index Over Time

Cumulative Citations Over Time

Cumulative Mentions Over Time

Datasets

Replication Data for: "Women and Ethnic Minority Candidates Face Dynamic Party Divergent Glass Cliff Conditions in French Elections" (Version: 1.1)

Compiled French National Assembly election data for 2002, 2007, and 2012 from the Centre de Données Socio-Politiques (CDSP,2020) and for 2017 from the Plateforme ouverte desdonnées publiques françaises (2020). Includes immigration data for 2012 and derived variables for research on party differences in the Glass Cliff effect for women and ethnic, racial, or immigrant minority candidates.

Authors

Robinson, Sarah ;
Clara, Kulich

0 Citations0 Mentions15% FAIR0.2 Dataset Index

10.7910/dvn/ohsqkeJanuary 2024

Replication Data for: Political Ideology Modifies the Effect of Glass Cliff Candidacies on Election Outcomes for Women in American State Legislative Races (2011 - 2016) (Version: 1.2)

Compilation of American state legislative election outcomes by district for both upper and lower chambers in each state (2011-2016), includes candidate gender and party, seat type, incumbency, and other variables. Additional data for state level legislative variables: proportion of women serving in each legislature in each chamber by party and state (2017), women's institutional resources, term limits, professionalism, and derived study measures (district seat winnability). Associated dataset codebooks and replication scripts (R) are catalogued for analysis provided in 'Political Ideology Modifies the Effect of Glass Cliff Candidacies on Election Outcomes for Women in American State Legislative Races (2011 - 2016)'. Citations for original data sources are provided in the README.txt file associated to each replication folder. Data users are asked to cite the original sources in link with this compilation where applicable.