Automated Organization ProfileAeronautics Institute of Technology and Delft University of Technologyand Delft
Aeronautics Institute of Technology and Delft University of Technologyand Delft
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets in this organization
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the organization's datasets
Total Mentions
Total mentions of the organization'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: 2.5 (sum of 4 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Installation and running the modelIt is necessary to install Calliope to run the model. Instructions for installation and running the model are available at:https://calliope.readthedocs.io/.Temporal resolutionThe temporal resolution of the model is 8 hours by default. You can set the model with another resolution in the "overrides" file: time_resampling: model.time: {function: resample, function_options: {'resolution': '8H'}} Please be aware that running the model might be computationally expensive. The model contains data from one year. If you wish to test the model, you can indicate a shorter time range in the "overrides" file > weather years. For example, you can select a subset of ten days of data: year_2010: model.subset_time: ['2010-01-01', '2010-01-10'] Weather yearWeather years include data from 2000 to 2019.ScenariosThe scenario names are structured as follows: route + policy + year:Routes: 1) Baseline2) Limited electrification (elec. stage 1)3) Intensive electrification (elec. stage 2)4) Net zeroPolicy:1) Default (status quo)2) Land constraints (exclusion of relevant ecological lands) (LC)3) 100% renewable (RE) - phase-out fossil fuels4) Land constraints (LC) + 100% REExample:scenario_netzero_banned_2019 (route=netzero, policy= phase-out fossil fuels)
Authors
- Paula Conde Santos Borba ;
- Stefan Pfenninger ;
- Wilson Sousa Junior
Installation and running the modelIt is necessary to install Calliope to run the model. Instructions for installation and running the model are available at:https://calliope.readthedocs.io/.Temporal resolutionThe temporal resolution of the model is 8 hours by default. You can set the model with another resolution in the "overrides" file: time_resampling: model.time: {function: resample, function_options: {'resolution': '8H'}} Please be aware that running the model might be computationally expensive. The model contains data from one year. If you wish to test the model, you can indicate a shorter time range in the "overrides" file > weather years. For example, you can select a subset of ten days of data: year_2010: model.subset_time: ['2010-01-01', '2010-01-10'] Weather yearWeather years include data from 2000 to 2019.ScenariosThe scenario names are structured as follows: route + policy + year:Routes: 1) Baseline2) Limited electrification (elec. stage 1)3) Intensive electrification (elec. stage 2)4) Net zeroPolicy:1) Default (status quo)2) Land constraints (exclusion of relevant ecological lands) (LC)3) 100% renewable (RE) - phase-out fossil fuels4) Land constraints (LC) + 100% REExample:scenario_netzero_banned_2019 (route=netzero, policy= phase-out fossil fuels)
Authors
- Paula Conde Santos Borba ;
- Stefan Pfenninger ;
- Wilson Sousa Junior
This repository contains the datasets for the publication "Enhancing drought resilience and energy security through complementing hydro by offshore wind power - the case of Brazil". Bias correction Technical data of existing farms (ABBEólica) Bias correction factors at the farm level Demand Simulated wind and solar power Biomass, nuclear, and small hydropower generation in 2019 Raw demand data Updated demand Hydropower time series Affluent Natural energy of run-of-rivers (fio d'água, in Portuguese) and reservoirs (reservatórios, in Portuguese), installed capacity, and maximal storage Offshore wind farms Locations, coordinates, water depth, available areas, water depth, distance to shore, technology,and maximal capacity; Code to estimate offshore wind farm capex and opex. Results of Calliope model capacity carrier_prod (power generation) storage costs emissions
Authors
- Borba, Paula
This repository contains the datasets for the publication "Enhancing drought resilience and energy security through complementing hydro by offshore wind power - the case of Brazil". Bias correction Technical data of existing farms (ABBEólica) Bias correction factors at the farm level Demand Simulated wind and solar power Biomass, nuclear, and small hydropower generation in 2019 Raw demand data Updated demand Hydropower time series Affluent Natural energy of run-of-rivers (fio d'água, in Portuguese) and reservoirs (reservatórios, in Portuguese), installed capacity, and maximal storage Offshore wind farms Locations, coordinates, water depth, available areas, water depth, distance to shore, technology,and maximal capacity; Code to estimate offshore wind farm capex and opex. Results of Calliope model capacity carrier_prod (power generation) storage costs emissions
Authors
- Borba, Paula