Emergent Challenges for Science sUAS Data Management: Fairness through Community Engagement and Best Practices Development
1.1. Current Use of sUAS in Research
1.2. Current sUAS Data Management
1.3. Opportunities for sUAS Data Management
1.3.1. The Push for Open Science and FAIRness
1.3.2. The Corresponding Maturation of Data Technologies
1.3.3. The Lack of Norms or Legacy sUAS Data
2. Materials and Methods
2.1. Community Engagement
2.1.1. Earth Science Information Partners Federation
2.1.2. Research Data Alliance
2.2. Additional Key Events and Communities
2.2.1. Oceanographic Sciences
2.2.2. Atmospheric Science
2.2.3. Agricultural Sciences
2.2.4. Traditional Remote Sensing
2.2.5. Earth & Space Science Informatics (ESSI)
3.1. sUAS Data Are Unique and in Need of Unique Management Infrastructure
3.2. Eight Community Distilled sUAS Data Management Challenges to Be Addressed
- Sensor use procedures: Sensor specific, tested and qualified use procedural best practices and standards are urgently needed in common human and machine readable languages. These best practice methodology and procedural guidelines should be developed and provided either by the manufacturer or the research community and include: mounting requirements on various platforms, calibration, ground truthing, and maintenance procedures, sample rates, flight patterns, and required metadata for data use and publication. The need for these and the aforementioned emphasis on machine and human readability is both for user ease and so as to enable greater automation in the capture of data provenance. As mentioned existing initial work on this issue has already appeared within the atmospheric community [48,49] and the Agricultural Sciences . While these procedures are largely currently not instantiated in open machine readable forms, they represent a direction for others to follow and contribute further to.
- Operational practices: Having best practices regarding operational protocols for scientific research will lower the barrier to entry for new users, allow training materials to be standardized for the many new training courses being created, and reduce the burden on operators which can only lead to safer operations. Further, while many countries have now begun to settle on regulations, many research organizations are still grappling with their own internal policies and protocols. Researcher operational best practices, created based on the experience of those who have been operating for longer, could serve to accelerate organizational protocol deployment in a country agnostic manner. One examples of such that is readily accessible comes from University of Exeter’s Remote Sensing Laboratory , and another is the University of California’s risk assessment and operating policy .
- Analytics and Error correction procedures: Best practices and acceptable error tolerances for primary sensor taxonomy branches and the associated processes need to be defined so as to avoid unintentional—but easy to introduce—errors . These are needed equally by tool providers (commercial and open source) so as to allow them to build to a standard, and by user community so as ensure correct data interpretation. Defining such will additionally contribute to efforts to define sensor use best practices and metadata creation, capture, and archive tooling.
- Data and metadata data formats: Guidelines regarding best practice metadata and data formats would serve the community, not as any form of restriction, but rather as a simple means of reducing workloads for both research sUAS operators and technical developers of: sensors, sUAS platforms, and the many components necessary in a data management tool stack. Having published recommended open formats based on community experience would similarly lower the barrier to novel experiments and enable both open source and commercial developers to create reusable tools.
- Data and metadata provenance practices: Given that a typical sUAS data capture project involves multiple: sensors, mechanical and electrical platforms, complex data transformations, and stakeholders, and that information regarding each of these commonly has a bearing on how a dataset should be interpreted. The provenance and workflow metadata—the record of the processes that created the data—are particularly important. Definitions of what parameters are required to make a data value, set, or product reusable—in potentially other scenarios than that for which it was originally captured or created—is necessary as both a practical guideline for operations and to facilitate the creation of tools to support the automated capture of this provenance.
- Data product levels: Defining suggested data product levels for various data types would facilitate both data archives and single researchers in determining what data should be archived, at what quality levels, at what resolutions, and with what associated metadata as required for likely reuse. This could be done for various primary parameter taxonomy branches, such for spectral data captured for Agricultural Sciences, and for atmospheric time series for Atmospheric Sciences.A crucial and complex sub-component to data product level definitions is the potential ethics driven policies that will govern sharing sUAS data. FAIR does not require open access, and others are exploring the ethical implications of both FAIR and open data in general [73,74]. Not least because of their historical military associations of sUAS but also due to the potential to easily violate important privacy restrictions with sUAS mounted sensors, the community needs to discuss both locally and internationally, what best practices might be for governing sUAS data’s desirable degree and form of openness.
- Data management and analytics tools: As shown in Figure 3, many of the relevant organizations already have some portion of a sUAS data analytics and management tool stack. However, the tools these bodies offer are only sUAS specific in a minority of cases. Rather, the majority were developed for other data types and are now being adapted for sUAS. More resources and effort are therefore necessary to accelerate these adaptations; and it is noteworthy that by addressing the above challenges, it would becomes significantly easier for resource pooling across development efforts.
- Data management education: As the domain grows there is an increasing demand for introductory information that properly addresses the multitude of new expertise needed to effectively use sUAS. In response many universities and other institutions are beginning to formally train research sUAS operators. An acknowledged but core missing component of these training curricula is any information on comprehensive consideration for science data good practices. Bringing together data management training and sUAS training offers a convenient opportunity, but one that depends heavily on investment being made first in the above challenges.
Conflicts of Interest
|sUAS||Small Unmanned Aircraft Systems|
|NASA||The National Aeronautics and Space Administration|
|USGS||United States Geological Survey|
|USDA||United States Department of Agriculture|
- Stöcker, C.; Bennett, R.; Nex, F.; Gerke, M.; Zevenbergen, J. Review of the current state of UAV regulations. Remote Sens. 2017, 9, 459. [Google Scholar] [CrossRef]
- Anweiler, S.; Piwowarski, D. Multicopter platform prototype for environmental monitoring. J. Clean. Prod. 2017, 155, 204–211. [Google Scholar] [CrossRef]
- Cho, R. How Drones Are Advancing Scientific Research|Lamont-Doherty Earth Observatory; Technical Report; Columbia University: New York, NY, USA, 2017. [Google Scholar]
- Colin, C. The Potential of Drones in Research; Technical Report; Environmental Biophysics. 2015. Available online: http://www.environmentalbiophysics.org/the-potential-of-drones-in-research (accessed on 14 November 2017).
- DRONES Reporting for Work. Available online: www.goldmansachs.com/insights/technology-driving-innovation/drones/ (accessed on 19 March 2019).
- Levick, R. Drone Industry Just Beginning To Take Off. Forbes Magazine, 15 May 2018. [Google Scholar]
- Commercial Drones Are Here: The Future of Unmanned Aerial Systems. Available online: https://www.mckinsey.com/industries/capital-projects-and-infrastructure/our-insights/commercial-drones-are-here-the-future-of-unmanned-aerial-systems (accessed on 25 November 2018).
- Shacklett, M.E. How the Drone Market Is Picking Up Speed. 2018. Available online: https://www.pobonline.com/blogs/23-geodatapoint-blog/post/101409-how-the-drone-market-is-picking-up-speed (accessed on 25 November 2018).
- Wilkinson, M.D.; Dumontier, M.; Aalbersberg, I.J.; Appleton, G.; Axton, M.; Baak, A.; Blomberg, N.; Boiten, J.W.; da Silva Santos, L.B.; Bourne, P.E.; et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 2016, 3, 160018. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Realizing the Power of Enterprise Data. Available online: https://www.veritas.com/form/whitepaper/realizing-the-power-of-enterprise-data (accessed on 19 March 2019).
- Cragin, M.H.; Palmer, C.L.; Carlson, J.R.; Witt, M. Data sharing, small science and institutional repositories. Philos. Trans. A Math. Phys. Eng. Sci. 2010, 368, 4023–4038. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Kowalczyk, S.; Shankar, K. Data sharing in the sciences. Ann. Rev. Inf. Sci. Technol. 2011, 45, 247–294. [Google Scholar] [CrossRef]
- Wallis, J.C.; Rolando, E.; Borgman, C.L. If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology. PLoS ONE 2013, 8, e67332. [Google Scholar] [CrossRef] [PubMed]
- Borgman, C.L. The conundrum of sharing research data. J. Am. Soc. Inf. 2012, 63, 1059–1078. [Google Scholar] [CrossRef]
- Nosek, B.A.; Spies, J.R.; Motyl, M. Scientific Utopia: II. Restructuring Incentives and Practices to Promote Truth Over Publishability. Perspect. Psychol. Sci. 2012, 7, 615–631. [Google Scholar] [CrossRef] [PubMed]
- Peng, R.D. Reproducible research in computational science. Science 2011, 334, 1226–1227. [Google Scholar] [CrossRef] [PubMed]
- Martone, M.E. FORCE11: Building the Future for Research Communications and e-Scholarship. Bioscience 2015, 65, 635. [Google Scholar] [CrossRef]
- Lecarpentier, D.; Wittenburg, P.; Elbers, W.; Michelini, A.; Kanso, R.; Coveney, P.; Baxter, R. EUDAT: A New Cross-Disciplinary Data Infrastructure for Science. Int. J. Digit. Curation 2013, 8, 279–287. [Google Scholar] [CrossRef]
- Orszag, P. Open government directive. Memorandum for the Heads of Executive Departments and Agencies. 2009. Available online: https://obamawhitehouse.archives.gov/open/documents/open-government-directive (accessed on 19 May 2019).
- Commission Decision of 12 December 2011 on the Reuse of Commission Documents. Off. J. Eur. Union 2011, 3, 229–232.
- The World Bank. Open Data Gaining Momentum in Africa; The World Bank: Washington, DC, USA, 2015. [Google Scholar]
- Stall, S.; Parsons, M.A.; Lehnert, K.; Cutcher-Gershenfeld, J.; Hanson, B. Enabling FAIR Data across the Earth and Space Sciences—Eos. 2017. Available online: https://eos.org/agu-news/enabling-fair-data-across-the-earth-and-space-sciences (accessed on 11 Decemeber 2018).
- Wilkinson, M.D.; Verborgh, R.; da Silva Santos, L.O.B.; Clark, T.; Swertz, M.A.; Kelpin, F.D.L.; Gray, A.J.G.; Schultes, E.A.; van Mulligen, E.M.; Ciccarese, P.; et al. Interoperability and FAIRness through a novel combination of Web technologies. PeerJ Comput. Sci. 2017, 3, e110. [Google Scholar] [CrossRef][Green Version]
- Mons, B.; Neylon, C.; Velterop, J.; Dumontier, M.; Da Silva Santos, L.O.B.; Wilkinson, M.D. Cloudy, increasingly FAIR; Revisiting the FAIR Data guiding principles for the European Open Science Cloud. Inf. Serv. Use 2017, 37, 49–56. [Google Scholar] [CrossRef]
- Elankavi, R.; Kalaiprasath, R.; Udayakumar, R. Data Mining with Big Data Revolution Hybrid. Int. J. Smart Sens. Intell. Syst. 2017, 10, 560–573. [Google Scholar] [CrossRef][Green Version]
- Lv, Z.; Song, H.; Basanta-Val, P.; Steed, A.; Jo, M. Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics. IEEE Trans. Ind. Inf. 2017, 13, 1891–1899. [Google Scholar] [CrossRef][Green Version]
- Gökalp, M.O.; Kayabay, K.; Zaki, M.; Koçyiğit, A.; Eren, P.E.; Neely, A. Big-Data Analytics Architecture for Businesses: A Comprehensive Review on New Open-Source Big-Data Tools; Cambridge Service Alliance: Cambridge, UK, 2017. [Google Scholar]
- Stein, L.D.; Knoppers, B.M.; Campbell, P.; Getz, G.; Korbel, J.O. Data analysis: Create a cloud commons. Nature 2015, 523, 149–151. [Google Scholar] [CrossRef][Green Version]
- Hashem, I.A.T.; Yaqoob, I.; Anuar, N.B.; Mokhtar, S.; Gani, A.; Ullah Khan, S. The rise of “big data” on cloud computing: Review and open research issues. Inf. Syst. 2015, 47, 98–115. [Google Scholar] [CrossRef]
- Chaudhary, S.; Somani, G.; Buyya, R. Research Advances in Cloud Computing; Springer: Berlin, Germany, 2017. [Google Scholar]
- Von Oehsen, B.; Fox, G.; Ziolkowski, M.; Bedrossian, A.; Fay, D.; Bottum, J.; Atkins, D.; Blatecky, A.; Mcmullen, R.; Tannenbaum, T.; et al. The Future of Cloud for Academic Research Computing; Technical Report; National Science Foundation: Alexandria, VA, USA, 2017. [Google Scholar]
- Dataset Search. Available online: https://toolbox.google.com/datasetsearch (accessed on 25 November 2018).
- Stall, S. Enabling Findable, Accessible, Interoperable, and Reusable Data-Eos. 2017. Available online: https://eos.org/editors-vox/enabling-findable-accessible-interoperable-and-reusable-data (accessed on 25 November 2018).
- Enabling FAIR Data Project. Available online: http://www.copdess.org/enabling-fair-data-project/ (accessed on 25 November 2018).
- L’heureux, A.; Grolinger, K.; Elyamany, H.F.; Capretz, M.A.M. Machine learning with big data: Challenges and approaches. IEEE Access 2017, 5, 777–797. [Google Scholar] [CrossRef]
- Karpatne, A.; Ebert-Uphoff, I.; Ravela, S.; Babaie, H.A.; Kumar, V. Machine Learning for the Geosciences: Challenges and Opportunities. IEEE Trans. Knowl. Data Eng. 2018, 31, 1544–1554. [Google Scholar] [CrossRef][Green Version]
- Barbieri, L.; Wyngaard, J. Drone Cluster—Federation of Earth Science Information Partners. Available online: http://wiki.esipfed.org/index.php/Drone_Cluster (accessed on 14 November 2018).
- Burgess, A. ESIP|Connecting Science, Data and Users. Available online: http://www.esipfed.org/ (accessed on 14 April 2018).
- Barbieri, L.; Wyngaard, J.; Bhakta, K.; Teng, B. Drone Cluster ESIP. 2016. Available online: https://osf.io/nuvem/ (accessed on 14 November 2018).
- ESIP Summer Meeting 2017|25–28 July 2017—Bloomington, IN|ESIP. Available online: http://www.esipfed.org/meetings/upcoming-meetings/esip-summer-meeting-2017 (accessed on 14 April 2018).
- Barbieri, L.; Wyngaard, J. Small Unmanned Aircraft Systems’ Data IG. 2016. Available online: https://www.rd-alliance.org/groups/small-unmanned-aircraft-systems%E2%80%99-data-ig (accessed on 18 February 2018).
- Research Data Alliance (RDA). 2013. Available online: http://rd-alliance.org/ (accessed on 8 May 2012).
- IOOS Glider DAC V2. Available online: https://github.com/ioos/ioosngdac (accessed on 14 November 2018).
- NMF Vehicles. Available online: https://mars.noc.ac.uk/ (accessed on 26 November 2018).
- Buck, J.; Phillips, A.; Lorenzo, A.; Kokkinaki, A.; Hearn, M.; Gardner, T.; Thorne, K. Oceanids command and control (C2) data system-Marine autonomous systems data for vehicle piloting, scientific data users, operational data assimilation, and big data. In Proceedings of the American Geophysical Union, Fall Meeting, New Orleans, LA, USA, 11–15 December 2017. [Google Scholar]
- Argrow, B.M.; Axisa, D.; Chilson, P.; Ellis, S.; Fladeland, M.; Frew, E.W.; Jacob, J.; Lord, M.; Moore, J.; Oncley, S.; et al. Final Report NCAR/EOL Community Workshop on Unmanned Aircraft Systems for Atmospheric Research; NCAR: Boulder, CO, USA, 2017. [Google Scholar]
- International Society for Atmospheric Research Using Remotely Piloted Aircraft. Available online: http://www.isarra.org/ (accessed on 19 March 2019).
- Rossi, M.; Brunelli, D. Gas Sensing on Unmanned Vehicles: Challenges and Opportunities. In Proceedings of the 2017 New Generation of CAS (NGCAS), Genoa, Italy, 6–9 September 2017; pp. 117–120. [Google Scholar]
- Villa, T.; Gonzalez, F.; Miljievic, B.; Ristovski, Z.; Morawska, L. An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives. Sensors 2016, 16, 1072. [Google Scholar] [CrossRef] [PubMed]
- Greene, B.R.; Segales, A.R.; Waugh, S.; Duthoit, S.; Chilson, P.B. Considerations for temperature sensor placement on rotary-wing unmanned aircraft systems. Atmos. Meas. Tech. 2018, 11, 5519–5530. [Google Scholar] [CrossRef][Green Version]
- Greene, B.R.; Segales, A.R.; Bell, T.M.; Pillar-Little, E.A.; Chilson, P.B. Environmental and Sensor Integration Influences on Temperature Measurements by Rotary-Wing Unmanned Aircraft Systems. Sensors 2019, 19, 1470. [Google Scholar] [CrossRef] [PubMed]
- EXIF and XMP Tag Information Read by Pix4D Desktop. Available online: https://support.pix4d.com/hc/en-us/articles/205732309-EXIF-and-XMP-tag-information-read-by-Pix4D-Desktop (accessed on 17 November 2019).
- LTAR-NEON Collaboration to Quantify Rangeland Vegetation Production. Available online: https://www.ars.usda.gov/research/project/?accnNo=429391 (accessed on 19 March 2019).
- U.S. Department of Agriculture. Ag Data Commons. Available online: https://data.nal.usda.gov (accessed on 19 May 2019).
- ESSEM COST Action ES1309. Available online: http://optimise.dcs.aber.ac.uk/ (accessed on 19 March 2019).
- Aasen, H. State-of-the-art in UAV remote sensing survey-First insights into applications of UAV sensing systems. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2017, 42, 1–4. [Google Scholar] [CrossRef]
- Cynthia, F.; Benjamin, T.; Kim, R. USGS Unmanned Aircraft Systems Data Delivery Specification; Technical Report; United States Geological Survey: Denver, CO, USA, 2018. [Google Scholar]
- Open Access at the European Space Agency. Available online: http://open.esa.int/open-access-at-esa/ (accessed on 19 March 2019).
- Data and Information Policy. Available online: https://science.nasa.gov/earth-science/earth-science-data/data-information-policy (accessed on 19 March 2019).
- GSFC Open Source Software. Available online: https://opensource.gsfc.nasa.gov/ (accessed on 19 March 2019).
- Mattmann, C.A. Computing: A vision for data science. Nature 2013, 493, 473. [Google Scholar] [CrossRef] [PubMed]
- Aviation Domain Working Group. Available online: https://www.opengeospatial.org/projects/groups/aviationdwg (accessed on 19 March 2019).
- OpenAerialMap. Available online: http://openaerialmap.org (accessed on 17 November 2018).
- About|OpenTopography. Available online: http://opentopo.sdsc.edu/dataspace/datasets (accessed on 10 April 2019).
- Radiant Earth Foundation—Earth Imagery for Impact. Available online: https://www.radiant.earth/ (accessed on 4 October 2019).
- UASPSE-UPSiE Unmanning Data Mondays. Available online: https://digitalag.org/unmanning_data.php (accessed on 17 November 2018).
- Foundation, L. DroneCode. Available online: https://www.dronecode.org/ (accessed on 13 March 2016).
- Black Swift Technologies. Available online: https://bst.aero/ (accessed on 13 December 2018).
- Hitzler, P.; Janowicz, K. Linked Data, Big Data, and the 4th Paradigm. Semant. Web 2013, 4, 233–235. [Google Scholar][Green Version]
- Cunliffe, A.M.; Anderson, K.; DeBell, L.; Duffy, J. A UK Civil Aviation Authority (CAA)-approved operations manual for safe deployment of lightweight drones in research. Int. J. Remote Sens. 2017, 38, 1–8. [Google Scholar] [CrossRef]
- Unmanned Aircraft System (Drone) Policy. Available online: https://policy.ucop.edu/doc/3500671/Drone (accessed on 19 March 2019).
- Congalton, R.G. Remote sensing and geographic information system data integration: error sources and. Photogramm. Eng. Remote Sens. 1991, 57, 677–687. [Google Scholar]
- Boeckhout, M.; Zielhuis, G.A.; Bredenoord, A.L. The FAIR guiding principles for data stewardship: Fair enough? Eur. J. Hum. Genet. 2018, 26, 931. [Google Scholar] [CrossRef]
- Serwadda, D.; Ndebele, P.; Grabowski, M.K.; Bajunirwe, F.; Wanyenze, R.K. Open data sharing and the Global South—Who benefits? Science 2018, 359, 642–643. [Google Scholar] [CrossRef]
- Weber, N.M.; Baker, K.S.; Thomer, A.K.; Chao, T.C.; Palmer, C.L. Value and Context in data use: Domain analysis revisited. Proc. Am. Soc. Inf. Sci. Technol. 2013, 49, 1–10. [Google Scholar] [CrossRef]
- Chao, T.C.; Cragin, M.H.; Palmer, C.L. Data Practices and Curation Vocabulary (DPCVocab): An empirically derived framework of scientific data practices and curatorial processes. J. Assoc. Inf. Sci. Technol. 2015, 66, 616–633. [Google Scholar] [CrossRef]
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Wyngaard, J.; Barbieri, L.; Thomer, A.; Adams, J.; Sullivan, D.; Crosby, C.; Parr, C.; Klump, J.; Raj Shrestha, S.; Bell, T. Emergent Challenges for Science sUAS Data Management: Fairness through Community Engagement and Best Practices Development. Remote Sens. 2019, 11, 1797. https://doi.org/10.3390/rs11151797
Wyngaard J, Barbieri L, Thomer A, Adams J, Sullivan D, Crosby C, Parr C, Klump J, Raj Shrestha S, Bell T. Emergent Challenges for Science sUAS Data Management: Fairness through Community Engagement and Best Practices Development. Remote Sensing. 2019; 11(15):1797. https://doi.org/10.3390/rs11151797Chicago/Turabian Style
Wyngaard, Jane, Lindsay Barbieri, Andrea Thomer, Josip Adams, Don Sullivan, Christopher Crosby, Cynthia Parr, Jens Klump, Sudhir Raj Shrestha, and Tom Bell. 2019. "Emergent Challenges for Science sUAS Data Management: Fairness through Community Engagement and Best Practices Development" Remote Sensing 11, no. 15: 1797. https://doi.org/10.3390/rs11151797