Automated Author ProfilePalomar, Rafa
Palomar, Rafa
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: 0.9 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
his dataset contains the raw responses collected for the study “Pseudoscientific and unwarranted beliefs among prospective primary and secondary science teachers.” The research examines the level of acceptance of pseudosciences and unwarranted beliefs among future science teachers.The dataset includes anonymized responses from 616 participants:381 university science graduates enrolled in a Master’s Degree in Secondary Teacher Training (future Secondary Teachers – ST)235 second-year Primary Education student teachers (PT).Participants completed a two-part questionnaire administered as part of regular training activities.The first part asks respondents to assess the scientific status of various disciplines (from basic sciences to pseudosciences) using a 1–5 Likert scale.The second part includes 13 pseudoscientific statements across four domains (paranormal, health-related, denialist, and legitimizing), where participants indicate their level of agreement using a four-option Likert scale (disagree, partly disagree, partly agree, agree).The dataset contains all item-level responses in raw form, fully anonymized, and suitable for replication, secondary analyses, and meta-research in science education and belief studies.
Authors
- Palomar, Rafa
his dataset contains the raw responses collected for the study “Pseudoscientific and unwarranted beliefs among prospective primary and secondary science teachers.” The research examines the level of acceptance of pseudosciences and unwarranted beliefs among future science teachers.The dataset includes anonymized responses from 616 participants:381 university science graduates enrolled in a Master’s Degree in Secondary Teacher Training (future Secondary Teachers – ST)235 second-year Primary Education student teachers (PT).Participants completed a two-part questionnaire administered as part of regular training activities.The first part asks respondents to assess the scientific status of various disciplines (from basic sciences to pseudosciences) using a 1–5 Likert scale.The second part includes 13 pseudoscientific statements across four domains (paranormal, health-related, denialist, and legitimizing), where participants indicate their level of agreement using a four-option Likert scale (disagree, partly disagree, partly agree, agree).The dataset contains all item-level responses in raw form, fully anonymized, and suitable for replication, secondary analyses, and meta-research in science education and belief studies.
Authors
- Palomar, Rafa