Automated Author ProfileCan, Zehra Semra
Marmara Universitesi0000-0003-4447-3400
Can, Zehra Semra
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: 3.2 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This dataset contains n = 209 observations of equilibrium adsorption capacity for metal ions adsorbed by metal–organic frameworks (MOFs) in aqueous systems, obtained from 10 peer-reviewed studies. Each row corresponds to a unique experimental condition (MOF, metal ion, solution pH, initial concentration C₀, adsorbent dose). Input parameters span three descriptor groups used for machine-learning modeling and interpretability:MOF structural: BET (m² g⁻¹), PoreSize (nm), PoreVolume (cm³ g⁻¹), pHpzcExperimental: pH, C₀ (mg L⁻¹), AdsorbentDose(g L⁻¹)Ion properties: OxidationState, AtomicMass (g mol⁻¹), Electronegativity, IonizationEnergy (eV), AtomicRadius (Å)Intended use. Interpretable ML (e.g., XGBoost + SHAP) to predict metal ion adsorption capacity of MOFs and to identify governing factors across materials and operating conditions. Extrapolation beyond observed ranges is discouraged.Files in this deposit:dataset.xlsx (human-readable) and dataset.csv (UTF-8, machine-readable)README.md (context, usage)data_dictionary.md (definitions/units/ranges)references.docx and references.csv (StudyID ↔ full citation ↔ DOI mapping)code/train_ML_shap.py and requirements.txtmof-adsorption-dataset-v1.zip (all the files zipped together for one-click download)
Authors
- Kahraman, Elif Nagihan ;
- Can, Zehra Semra
This dataset contains n = 209 observations of equilibrium adsorption capacity for metal ions adsorbed by metal–organic frameworks (MOFs) in aqueous systems, obtained from 10 peer-reviewed studies. Each row corresponds to a unique experimental condition (MOF, metal ion, solution pH, initial concentration C₀, adsorbent dose). Input parameters span three descriptor groups used for machine-learning modeling and interpretability:MOF structural: BET (m² g⁻¹), PoreSize (nm), PoreVolume (cm³ g⁻¹), pHpzcExperimental: pH, C₀ (mg L⁻¹), AdsorbentDose(g L⁻¹)Ion properties: OxidationState, AtomicMass (g mol⁻¹), Electronegativity, IonizationEnergy (eV), AtomicRadius (Å)Intended use. Interpretable ML (e.g., XGBoost + SHAP) to predict metal ion adsorption capacity of MOFs and to identify governing factors across materials and operating conditions. Extrapolation beyond observed ranges is discouraged.Files in this deposit:dataset.xlsx (human-readable) and dataset.csv (UTF-8, machine-readable)README.md (context, usage)data_dictionary.md (definitions/units/ranges)references.docx and references.csv (StudyID ↔ full citation ↔ DOI mapping)code/train_ML_shap.py and requirements.txtmof-adsorption-dataset-v1.zip (all the files zipped together for one-click download)
Authors
- Kahraman, Elif Nagihan ;
- Can, Zehra Semra