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Is itself a file that is human-readable to document the narrative, data sources, and data processing used to develop the quantities (and uncertainty) for the input/output flows of a unit process or is capable of generating that document. It is meant as a DS/DF replacement.1
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Displayed documentation directly in a web browser.1
Implement scenarios directly with openLCA or a json-ld.
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Read the input/output flow quantities of a unit process in openLCA/json-LD. Thinking of this in terms of a QA step to ensure both internal and external users that the provided json-ld input/output quantities match those in documentation.
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Write input/output flows to openLCA
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Write parameters and parameter values to openLCA
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Include FEDEFL info in input/output flows.
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Include pointers to spreadsheets, other notebooks, or alternate forms of documentation. The narrative of the calculations in those other sources needs to be explained within the report.
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Include graphs and graphics, etc in report.
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Show EPA flow-level DQI in report
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Write flow-level DQI to openLCA
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EPA process-level DQI – there’s a method to determine the process completeness that includes some equations. Ideally, I think there would be a way to automate this based on the available inventory.
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Write process-level EPA DQI to openLCA
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List independent vs dependent parameters in report2
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Show formulas for flows (could be a reference to a single parameter or a formula as allowed by openLCA)
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Write formulas to flow values in openLCA
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Generate text formulas for openLCA given calculations in python. From an end-user QA perspective, might be nice to confirm “text formula results” match values.
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Suggested citation
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Automatically generate suggested citation
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List references
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Interface with reference management software (e.g., Zotero) to generate bibliography. Pyzotero!
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Option to hide code blocks – for example, HTML version doesn’t need to see in-line commands to write to openLCA. Might also be nice to “hide” code blocks by default but allow them to be expanded in the HTML
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Footnotes
Jupyter notebooks were originally envisioned for this because they combine human-readable text (markdown) with blocks available for python to transparently show how the data is processed. We also spent time investigating how that notebook could be converted to HTML to be displayed directly in the browser rather than having to load an environment to navigate the actual notebook. ↩
It was spot in the DF (word) file of old unit processes. ↩
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Updated: 2025-09-08
Footnotes
Jupyter notebooks were originally envisioned for this because they combine human-readable text (markdown) with blocks available for python to transparently show how the data is processed. We also spent time investigating how that notebook could be converted to HTML to be displayed directly in the browser rather than having to load an environment to navigate the actual notebook. ↩
It was spot in the DF (word) file of old unit processes. ↩
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