Scientific Data Management for the ATP 3. Edward J. Wolfrum, Eric Knoshaug, Lieve Laurens, Valerie Harmon, John A. McGowen
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1 Scientific Data Management for the ATP 3 Edward J. Wolfrum, Eric Knoshaug, Lieve Laurens, Valerie Harmon, John A. McGowen
2 The ATP 3 UFS Scientific Data Management in 1 slide The ATP 3 team uses commercial and open-source tools to collect, store, audit, correct, & retrieve data about samples Existence & Location (what & where they are) Composition (what they look like) History (how were they produced) Standardizing how we collect and store these data across all ATP 3 sites is absolutely necessary for success Each site uses standardized spreadsheets to collect defined primary data about samples & experiments A data quality team reviews these spreadsheets, and works with site personnel to correct errors; these spreadsheets hold the raw data The UFS data are available to the public Curated datasets are available for download at OpenEI.org A manuscript describing these data is in preparation for Nature Scientific Data
3 Outline The Scientific Data Management Workflow Data repository - OpenEi.org What are the data? Data collection & curation Using the data Conclusions
4 Scientific Data Management (n.) Scientific Data Management (SDM) is a series of discrete activities we performed to curate and store the data from ATP 3 experiments The goal is to ensure that robust and complete datasets are collected and archived to allow useful review and analysis by internal and external stakeholders SDM does not include generating the data, which is a big challenge itself!
5 The SDM Workflow (1/2) 1. DATA COLLECTION Each site records pond operation, weather, and algal compositional analysis data into standardized Excel spreadsheets 2. DATA VALIDATION Spreadsheets (1 for each site-experiment combination) are stored in Dropbox and reviewed by QA/QC team. Data include pond operational, weather, instrumental, and harvest data.
6 The SDM Workflow (2/2) 3. DATA EXTRACTION R scripts extract the data and create ASCII files for pond operational data, instrumental & weather data, and summary harvest data the flat files 4. DATA QA/QC These flat files are then examined visually by the ATP 3 data analysis team. Errors are corrected in the original spreadsheet 5. SHARING The datasets and experimental protocols are publicly available on the Open Energy Information network:
7 OpenEI.org Using the existing infrastructure of OpenEI.org provides a rapid, robust, and low-cost solution for making the ATP 3 datasets public
8 OpenEI.org -
9 Exactly what data are available? Pond Operational Data Over 15,800 unique time points Instrumentation Data Over 1,084,000 time points & daily averaged data Weather Data Over 217,500 unique time points & daily averaged data Harvest Yield Data 2150 discrete harvests
10 Spreadsheet Configuration Production data are collected in a spreadsheet - this is the primary unit of experimental information for the ATP 3 Consortium CONUNDRUM FLEXIBILITY vs. RAPID DEVELOPMENT The form of the algae pond production spreadsheet was adapted from experience at ASU Compositional analysis data are collected in a separate spreadsheet, adapted from experience at NREL All spreadsheets are under version/change control The spreadsheets have evolved over the course of the project; our SDM applications have had to evolve as well
11 Structuring the spreadsheets simply Primary Data Validation We locked down the spreadsheets and added data validation functionality to the Pond Data Discrete (PDD) page in the Excel spreadsheets to ensure proper data types and values (e.g., no ph values of really high or -4 ). Not completely robust, but solved some problems Real-time Visualization We added graphs in the collection spreadsheets and placed active spreadsheets in a shared Dropbox folder to allow each site (and the ATP 3 leadership team) to monitor the results of experiments on a daily basis
12 Spreadsheet PDD Page (1/3)
13 Spreadsheet PDD Page (2/3)
14 Spreadsheet PDD Page (3/3)
15 The challenges of data curation All primary data from the UFS spreadsheets were plotted Obviously bad or missing data removed What is an outlier?
16 Using ATP3 UFS Data (1/2) Once the primary data are curated and stored, they can be examined directly, or used to calculate things like productivity & algal growth rates 16
17 Using ATP 3 UFS Data (2/2) Cultivation Data from the ATP 3 are used in NREL s Algae State of Technology (SOT) Assessments Productivity and Growth Rate calculations can be done in multiple ways Slope of AFDW vs. time Intermediate Harvests Across Multiple Harvests We calculated the multiple harvest yield productivity using 3 different time frames to simulate different production strategies (Reference: Knoshaug, et. al. NREL/TP ) 17
18 Conclusions The Scientific Data Management process used by the ATP3 Consortium evolved over the course of the project We used open-source tools to review and QA/QC data from multiple sites and multiple experiments over multiple years Datasets that have been QA/QC d are available on OpenEI.org, a DOEfunded public-facing website We (again) emphasize that Scientific Data Management is a set of processes, not a set of software tools. If you want to manage scientific data, you need a manager; this is a discrete and critical task, particularly when the data are being generated from multiple sources
19 Questions?
20 TRUTHS OF SCIENTIFIC DATA MANAGEMENT 1. The primary unit of data collection for most researchers & projects is the spreadsheet 2. The data types being collected will likely change over the course of a project 3. It is possible to minimize these changes by careful consideration at the beginning of the project, but they cannot be eliminated 4. Whatever system and workflow used for scientific data management must be able to respond to these changes 5. The difficulty in coordinating these changes among research partners goes up as the exponential of the number of partners 6. Data curation (QA/QC) to identify and correct data entry errors is a separate activity from data collection/storage and is equally important 7. Scientific Data Management is not a collection of software tools, it is a process that can use software tools
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