Skip to main content
SpringerLink
Log in

Assessing Legacy Collections for Scientific Data Rescue

  • Conference paper
  • First Online:
Diversity, Divergence, Dialogue (iConference 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12646))

Included in the following conference series:

Abstract

Widespread investments in facilitating reuse and reproducibility of scientific research have spurred an increasing recognition of the potential value of data biding in unpublished records and legacy research materials, such as scientists’ papers, historical publications, and working files. Recovering usable scientific data from legacy collections constitutes one kind of data rescue: the usually urgent application of selected data curation processes to data at imminent risk of loss. Given growing interest in data-intensive research, and a concomitant movement toward computationally amenable collections in memory institutions, scientific data repositories and collecting institutions would benefit from systematic approaches to assessing and processing legacy collections with the specific goal of retrieving reusable or historically valuable scientific data. This paper suggests a preliminary framework for assessing legacy collections of research materials for the purpose of data rescue. Developed through three case studies of agricultural research collections held by the United States Department of Agriculture’s National Agricultural Library, this framework aims to guide data rescue initiatives in agricultural research centers, and to provide conceptual framing for emerging conversations around data rescue across disciplines.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Downs, R.R., Chen, R.S.: Curation of scientific data at risk of loss: data rescue and dissemination. In: Curating Research Data Volume One: Practical Strategies for Your Digital Repository, pp. 263–277. Association of College and Research Libraries (2017)

    Google Scholar 

  2. Wippich, C.: Preserving science for the ages—USGS data rescue. In: Preserving science for the ages–USGS data rescue (USGS Numbered Series No. 2012–3078; Fact Sheet, Vols. 2012–3078). U.S. Geological Survey (2012). https://doi.org/10.3133/fs20123078

  3. Rountree, R.A., Perkins, P.J., Kenney, R.D., Hinga, K.R.: Sounds of western north Atlantic fishes—data rescue. Bioacoustics 12(2–3), 242–244 (2002). https://doi.org/10.1080/09524622.2002.9753710

    Article  Google Scholar 

  4. Fisher, M.: Making the case for evidence-based agriculture. CSA News 59(5), 4–11 (2014). https://doi.org/10.2134/csa2014-59-5-1

    Article  Google Scholar 

  5. Oden, J.T., et al.: National Science Foundation Advisory Committee for Cyberinfrastructure Task Force on Grand Challenges (2011). https://www.nsf.gov/cise/oac/taskforces/TaskForceReport_GrandChallenges.pdf

  6. Cragin, M.H., Palmer, C.L., Carlson, J.R., Witt, M.: Data sharing, small science and institutional repositories. Philos. Trans. Royal Soc. A Math. Phys. Eng. Sci. 368(1926), 4023–4038 (2010). https://doi.org/10.1098/rsta.2010.0165

    Article  Google Scholar 

  7. Padilla, T., Allen, L., Frost, H., Potvin, S., Elizabeth, R.R., Varner, S.: Final Report – Always Already Computational: Collections as Data (Version 1), Zenodo, 22 May 2019. https://doi.org/10.5281/zenodo.3152935

  8. Brunet, M., Jones, P.: Data rescue initiatives: bringing historical climate data into the 21st century. Climate Res. 47(1), 29–40 (2011). https://doi.org/10.3354/cr00960

    Article  Google Scholar 

  9. Wyborn, L., Hsu, L., Lehnert, K., Parsons, M.A.: Guest editorial: special issue: rescuing legacy data for future science. GeoResJ 6, 106–107 (2015). https://doi.org/10.1016/j.grj.2015.02.017

    Article  Google Scholar 

  10. Vearncombe, J., Riganti, A., Isles, D., Bright, S.: Data upcycling. Ore Geol. Rev. 89, 887–893 (2017). https://doi.org/10.1016/j.oregeorev.2017.07.009

    Article  Google Scholar 

  11. Janz, M.M.: Maintaining access to public data: Lessons from Data Refuge [Preprint]. LIS Scholarship Archive (2018). https://doi.org/10.31229/osf.io/yavzh

  12. Griffin, E.R.: When are old data new data? GeoResJ 6, 92–97 (2015). https://doi.org/10.1016/j.grj.2015.02.004

    Article  Google Scholar 

  13. Fallas, K.M., MacNaughton, R.B., Sommers, M.J.: Maximizing the value of historical bedrock field observations: an example from northwest Canada. GeoResJ 6, 30–43 (2015). https://doi.org/10.1016/j.grj.2015.01.004

    Article  Google Scholar 

  14. Specht, A., Bolton, M., Kingsford, B., Specht, R., Belbin, L.: A story of data won, data lost and data re-found: the realities of ecological data preservation. Biodivers. Data J. 6, e28073 (2018). https://doi.org/10.3897/BDJ.6.e28073

    Article  Google Scholar 

  15. Lafferty-Hess, S., Christian, T.-M.: More data, less process? The applicability of MPLP to research data. IASSIST Q. 40(4), 6 (2017). https://doi.org/10.29173/iq907

  16. Greene, M., Meissner, D.: More product, less process: revamping traditional archival processing. Am. Archivist, 68(2), 208–263 (2005). https://doi.org/10.17723/aarc.68.2.c741823776k65863

  17. Waugh, D., Russey Roke, E., Farr, E.: Flexible processing and diverse collections: a tiered approach to delivering born digital archives. Arch. Rec. 37(1), 3–19 (2016). https://doi.org/10.1080/23257962.2016.1139493

    Article  Google Scholar 

  18. Faulder, E., et al.: Digital Processing Framework [Report] (2018). https://ecommons.cornell.edu/handle/1813/57659

  19. Cooper, D., et al.: Supporting the Changing Research Practices of Agriculture Scholars. Ithaka S+R (2017). https://doi.org/10.18665/sr.303663

  20. Brouder, S., et al.: Enabling open-source data networks in public agricultural research (CAST Commentary No. QTA2019–1). Search Results Web Result with Site Links Council for Agricultural Science and Technology (2019). https://www.cast-science.org/wp-content/uploads/2019/05/QTA2019-1-Data-Sharing.pdf

  21. Carlson, J.: The Data Curation Profile Toolkit: The Profile Template. Purdue University Libraries/Distributed Data Curation Center (2010).https://doi.org/10.5703/1288284315653

  22. Faundeen, J.L., Oleson, L.R.: Scientific Data appraisals: the value driver for preservation efforts, vol. 6 (2007). https://www.pv2007.dlr.de/Papers/Faundeen_AppraisalsValue_for_Preservation.pdf

  23. Palmer, C.L., Weber, N.M., Cragin, M.H.: The analytic potential of scientific data: Understanding re-use value. Proc. Am. Soc. Inf. Sci. Technol. 48(1), 1 (2011). https://doi.org/10.1002/meet.2011.14504801174

    Article  Google Scholar 

  24. Hoffman, K.M., Clarke, C.T., Shiue, H.S.Y., Nicholas, P., Shaw, M., Fenlon, K.: Data rescue: an assessment framework for legacy research collections (2020). https://doi.org/10.13016/1zmx-ghhq

Download references

Acknowledgements

This research was conducted by Fellows in the Digital Curation Fellowship program, supported by Non-Assistance Cooperative Agreement #58–8260-6–003 between the University of Maryland and the United States Department of Agriculture (USDA), Agricultural Research Service (ARS), National Agricultural Library with funding provided by the USDA, ARS, Office of National Programs.

Author information

Authors and Affiliations

  1. College of Information Studies, University of Maryland, College Park, USA

    Hilary Szu Yin Shiue, Cooper T. Clarke, Miranda Shaw, Kelly M. Hoffman & Katrina Fenlon

Authors
  1. Hilary Szu Yin Shiue
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cooper T. Clarke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miranda Shaw
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kelly M. Hoffman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Katrina Fenlon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Katrina Fenlon .

Editor information

Editors and Affiliations

  1. Humboldt University of Berlin, Berlin, Germany

    Katharina Toeppe

  2. Renmin University of China, Beijing, China

    Hui Yan

  3. The University of Hong Kong, Hong Kong, Hong Kong

    Samuel Kai Wah Chu

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shiue, H.S.Y., Clarke, C.T., Shaw, M., Hoffman, K.M., Fenlon, K. (2021). Assessing Legacy Collections for Scientific Data Rescue. In: Toeppe, K., Yan, H., Chu, S.K.W. (eds) Diversity, Divergence, Dialogue. iConference 2021. Lecture Notes in Computer Science(), vol 12646. Springer, Cham. https://doi.org/10.1007/978-3-030-71305-8_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-71305-8_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71304-1

  • Online ISBN: 978-3-030-71305-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation