Automated Author ProfileTejada, Julian
Tejada, Julian
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: 2.9 (sum of 5 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Data summary = this is the raw data in the study:Marmolejo-Ramos, F, Marrone, R., Korolkiewicz, M., Gabriel, F., Siemens, G., Joksimovic, S., Yamada, Y., Mori, M., Rahwan T., Sahakyan, M., Sonna, B., Samekin, A., Som, B., Ndukaihe, I., Arinze, N. C., Kundrat, J., Ngo, G., Nguyen, G., Lacia, M., Kung, C., Irmayanti, M., Muktadir, A., Timoria Samosir. F., Liuzza, M, Omid, Hassan, Ozdogru, A., Ariyabuddhiphongs, K., Rakchai, W., Trujillo, N., Maris Valencia S., Janyan, A., Kostov, K., Montoro, P., Hinojosa, J., Medeiros, K., Hunt, T., Freitag, R., Posada, J., Tejada, J. Trust in algorithms. An experimental approach
"ID": D for each participant"Country""e" : factor variable identifying trials with or without explainability"S" : Factor variable identifying conditions of low and high stake"Item": Factor variable identifying each of the six scenarios. "Probability": Mean probability answered for the question 1 and 2 on the condition of stake described in the column S and e"Age""Gender""ADA": Numeric variable whihc represents the participant's level of familiarity with algorithms"BLISS": avearega number of correct answers of the fourteen items selected from Literacy In Statistics (BLIS)
Authors
- Marmolejo-Ramos, Fernando ;
- Tejada, Julian
Data summary = this is the raw data in the study:Marmolejo-Ramos, F, Marrone, R., Korolkiewicz, M., Gabriel, F., Siemens, G., Joksimovic, S., Yamada, Y., Mori, M., Rahwan T., Sahakyan, M., Sonna, B., Samekin, A., Som, B., Ndukaihe, I., Arinze, N. C., Kundrat, J., Ngo, G., Nguyen, G., Lacia, M., Kung, C., Irmayanti, M., Muktadir, A., Timoria Samosir. F., Liuzza, M, Omid, Hassan, Ozdogru, A., Ariyabuddhiphongs, K., Rakchai, W., Trujillo, N., Maris Valencia S., Janyan, A., Kostov, K., Montoro, P., Hinojosa, J., Medeiros, K., Hunt, T., Freitag, R., Posada, J., Tejada, J. Trust in algorithms. An experimental approach
"ID": D for each participant"Country""e" : factor variable identifying trials with or without explainability"S" : Factor variable identifying conditions of low and high stake"Item": Factor variable identifying each of the six scenarios. "Probability": Mean probability answered for the question 1 and 2 on the condition of stake described in the column S and e"Age""Gender""ADA": Numeric variable whihc represents the participant's level of familiarity with algorithms"BLISS": avearega number of correct answers of the fourteen items selected from Literacy In Statistics (BLIS)
Authors
- Marmolejo-Ramos, Fernando ;
- Tejada, Julian
Data summary = this is the raw data in the study:Marmolejo-Ramos, F, Marrone, R., Korolkiewicz, M., Gabriel, F., Siemens, G., Joksimovic, S., Yamada, Y., Mori, M., Rahwan T., Sahakyan, M., Sonna, B., Samekin, A., Som, B., Ndukaihe, I., Arinze, N. C., Kundrat, J., Ngo, G., Nguyen, G., Lacia, M., Kung, C., Irmayanti, M., Muktadir, A., Timoria Samosir. F., Liuzza, M, Omid, Hassan, Ozdogru, A., Ariyabuddhiphongs, K., Rakchai, W., Trujillo, N., Maris Valencia S., Janyan, A., Kostov, K., Montoro, P., Hinojosa, J., Medeiros, K., Hunt, T., Freitag, R., Posada, J., Tejada, J. Trust in algorithms. An experimental approach
"ID": D for each participant"Country""e" : factor variable identifying trials with or without explainability"S" : Factor variable identifying conditions of low and high stake"Item": Factor variable identifying each of the six scenarios. "Probability": Mean probability answered for the question 1 and 2 on the condition of stake described in the column S and e"Age""Gender""ADA": Numeric variable whihc represents the participant's level of familiarity with algorithms"BLISS": avearega number of correct answers of the fourteen items selected from Literacy In Statistics (BLIS)
Authors
- Marmolejo-Ramos, Fernando ;
- Tejada, Julian
NetLogo Iterated Prisoner's Dilemma dataset generated for the manuscript New memory-one strategies of the Iterated Prisoner’s Dilemma: a new framework to programmed human-AI interaction
Authors
- Tejada, Julian
NetLogo Iterated Prisoner's Dilemma dataset generated for the manuscript New memory-one strategies of the Iterated Prisoner’s Dilemma: a new framework to programmed human-AI interaction
Authors
- Tejada, Julian