Update README.md

README.md

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 ![image](https://cdn-uploads.huggingface.co/production/uploads/69ed86a08bdc19557f6eda14/U_fIUryMHT6MWwoKAjIgK.png)
 *Figure 1: SIIB physical and control layers*
 
+## 2.2- Positionality
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+This dataset was created by researchers at the Distribution Grids Research and Innovation (DGRI) Lab at the University of Calgary, AB, working at the intersection of ML and power systems engineering. The team brings expertise in power systems modeling, simulation, and ML-based applications, combined with experience in both academic research and industry deployment. This positions us to make informed choices about scenario design, simulation fidelity, and the selection of a canonical test system, but it also means that our framing of what constitutes a meaningful and representative dataset is shaped by the conventions and priorities of the power systems engineering community, which may differ from those of researchers approaching this problem from a pure ML or scientific computing perspective. Our work is motivated by the practical challenge of enabling stable integration of IBRs through scalable simulation tools. This motivation reflects a specific orientation toward the energy transition and the computational needs of grid operators and planners. Researchers with different priorities, for example, those focused on social and economic dimensions of power systems, may find the dataset's scope and framing less directly applicable to their needs.
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+Two distinct field epistemologies are in tension in the design of this dataset. The power systems engineering tradition prioritizes physical fidelity, grounded in first-principles differential-algebraic equation models, validated against known physical phenomena, and evaluated against established test cases. This epistemology is evident in our choice of the SIIB system as the test case, in the use of industry-standard simulation platforms, and in the cross-validation of outputs. The assumption embedded in this tradition is that a well-modeled simplified system is a valid proxy for studying fundamental dynamic phenomena, an assumption that is widely accepted within power systems but deserves explicit acknowledgment. The machine learning tradition, by contrast, prioritizes statistical generalization, benchmark comparability, and scale. This epistemology shaped our decision to generate 4,000 diverse scenarios through stochastic sampling, to provide paired data across two simulation domains to support resolution-invariance studies, and to structure the dataset for straightforward ingestion by standard ML pipelines. Users should be aware that this dataset was designed with ML for surrogate time-domain modeling as the primary downstream task. Design choices that appear neutral, the parameter sampling ranges, the disturbance types included, and the choice of signals logged reflect judgments made from within this dual epistemological framing. Different choices would have produced a different dataset, and those differences would matter for certain downstream applications.
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+## 2.3- Carbon Footprint
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+Each simulation run requires approximately 7 minutes of CPU execution time. Assuming an average computational power draw of 100 W for the host system [7], the energy consumption per simulation run is 7 min × (1 hr / 60 min) × 0.1 kW = 0.0117 kWh per simulation. The data was generated in Alberta; thus, using the Alberta grid emission intensity of 0.47 kg CO₂e/kWh for electricity generation [8], the carbon footprint per simulation run is approximately 0.0117 kWh × 0.47 kg CO₂e/kWh ≈ 5.5 g CO₂e per simulation run. Across all 4,000 simulation runs, the total estimated carbon footprint is 4,000 × 5.5 g ≈ 22 kg CO₂e. For reference, this is roughly equivalent to driving a passenger vehicle approximately 107 kilometers [9], making the dataset generation process environmentally modest relative to the potential research impact of enabling more computationally efficient grid simulation tools.
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+The dataset creation involves a trade-off between simulation fidelity and computational and environmental cost. EMT simulations were necessary to accurately capture fast inverter dynamics, provide reliable ground truth for machine learning models, and enable cross-resolution learning between EMT and RMS domains. At the same time, the dataset size of 4000 scenarios was selected to provide sufficient diversity for machine learning applications, while keeping the overall computational footprint within a manageable range. 
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 [1] https://ieeexplore.ieee.org/abstract/document/9286772/ 
 
 [2] https://ieeexplore.ieee.org/abstract/document/10213230 
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 [6] https://ieeexplore.ieee.org/abstract/document/10226356 
 
+[7] https://www.energy.gov/sites/prod/files/2016/07/f33/2010-05-26%20TIAX%20CMELs%20Final%20Report_0.pdf 
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+[8] https://www.alberta.ca/albertas-greenhouse-gas-emissions-reduction-performance
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+[9] https://natural-resources.canada.ca/energy-efficiency/transportation-energy-efficiency/personal-vehicles 
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