Digitization and the Future of Natural History Collections
Abstract
Natural history collections (NHCs) are the foundation of historical baselines for assessing anthropogenic impacts on biodiversity. Along these lines, the online mobilization of specimens via digitization—the conversion of specimen data into accessible digital content—has greatly expanded the use of NHC collections across a diversity of disciplines. We broaden the current vision of digitization (Digitization 1.0)—whereby specimens are digitized within NHCs—to include new approaches that rely on digitized products rather than the physical specimen (Digitization 2.0). Digitization 2.0 builds on the data, workflows, and infrastructure produced by Digitization 1.0 to create digital-only workflows that facilitate digitization, curation, and data links, thus returning value to physical specimens by creating new layers of annotation, empowering a global community, and developing automated approaches to advance biodiversity discovery and conservation. These efforts will transform large-scale biodiversity assessments to address fundamental questions including those pertaining to critical issues of global change.
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X-ray computed microtomography (micro-CT) can non-destructively digitize valuable collection material in 3D.
“X-ray computed microtomography (micro-CT) can non-destructively digitize valuable collection material in 3D 10 , 11”
From High-throughput phenomics of global ant biodiversity (2026) · cites this paper as [11]
Cites
Lewis, S. L., & Maslin, M. A. (2015) · Nature · doi:10.1038/nature14258 · Cited by 3,441 · see citing works
Impact of a Century of Climate Change on Small-Mammal Communities in Yosemite National Park, USA
Moritz, C. C., Patton, J. L., Conroy, C. J., Parra, J. L., White, G. C., & Beissinger, S. R. (2008) · Science · doi:10.1126/science.1163428 · Cited by 1,122 · see citing works
Taxonomic bias in biodiversity data and societal preferences
Troudet, J., Grandcolas, P., Blin, A., Vignes‐Lebbe, R., & Legendre, F. (2017) · Scientific Reports · doi:10.1038/s41598-017-09084-6 · Cited by 902 · see citing works
The Value of Museum Collections for Research and Society
Suarez, A. V., & Tsutsui, N. D. (2004) · BioScience · doi:10.1641/0006-3568(2004)054[0066:tvomcf]2.0.co;2 · Cited by 820 · see citing works
Spatial bias in the GBIF database and its effect on modeling species' geographic distributions
Beck, J., Böller, M., Erhardt, A., & Schwanghart, W. (2014) · Ecological Informatics · doi:10.1016/j.ecoinf.2013.11.002 · Cited by 708 · see citing works
Multidimensional biases, gaps and uncertainties in global plant occurrence information
Meyer, C., Weigelt, P., & Kreft, H. (2016) · Ecology Letters · doi:10.1111/ele.12624 · Cited by 580 · see citing works
Amazon plant diversity revealed by a taxonomically verified species list
Cardoso, D., Särkinen, T., Alexander, S. N., Amorim, A. M., Bittrich, V., Celis, M., Daly, D. C., Fiaschi, P., Funk, V. A., Giacomin, L. L., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1706756114 · Cited by 470 · see citing works
Widespread sampling biases in herbaria revealed from large‐scale digitization
Daru, B. H., Park, D., Primack, R. B., et al. (2017) · New Phytologist · doi:10.1111/nph.14855 · Cited by 431 · see citing works
Self-citationPyke, G. H., & Ehrlich, P. R. (2009) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/j.1469-185x.2009.00098.x · Cited by 405 · see citing works
Declines in the numbers of amateur and professional taxonomists: implications for conservation
Hopkins, G. W., & Freckleton, R. (2002) · Animal Conservation · doi:10.1017/s1367943002002299 · Cited by 383 · see citing works
Herbarium specimens demonstrate earlier flowering times in response to warming in Boston
Primack, D., Imbres, C., Primack, R. B., et al. (2004) · American Journal of Botany · doi:10.3732/ajb.91.8.1260 · Cited by 371 · see citing works
Mega-evolutionary dynamics of the adaptive radiation of birds
Cooney, C. R., Bright, J. A., Capp, E. J. R., Chira, A. M., Hughes, E. C., Moody, C. J. A., Nouri, L. O., Varley, Z. K., & Thomas, G. H. (2017) · Nature · doi:10.1038/nature21074 · Cited by 356 · see citing works
Automated plant species identification—Trends and future directions
Wäldchen, J., Rzanny, M., Seeland, M., & Mäder, P. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005993 · Cited by 342 · see citing works
Biological collections for understanding biodiversity in the Anthropocene
Meineke, E. K., Davies, T. J., Daru, B. H., et al. (2018) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2017.0386 · Cited by 327 · see citing works
Self-citationThe history and impact of digitization and digital data mobilization on biodiversity research
Nelson, G., & Ellis, S. (2019) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2017.0391 · Cited by 304 · see citing works
Widespread mistaken identity in tropical plant collections
Goodwin, Z. A., Harris, D. J., Filer, D. L., Wood, J. R., & Scotland, R. W. (2015) · Current Biology · doi:10.1016/j.cub.2015.10.002 · Cited by 266 · see citing works
Natural history collections as sources of long-term datasets
Lister, A. M. (2011) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2010.12.009 · Cited by 257 · see citing works
Lendemer, J. C., Thiers, B. M., Monfils, A., Zaspel, J. M., Ellwood, E. R., Bentley, A. C., LeVan, K. E., Bates, J. M., Jennings, D., Contreras, D. L., et al. (2019) · BioScience · doi:10.1093/biosci/biz140 · Cited by 240 · see citing works
Collections‐based science in the 21st Century
Funk, V. A. (2018) · Journal of Systematics and Evolution · doi:10.1111/jse.12315 · Cited by 217 · see citing works
Towards global data products of Essential Biodiversity Variables on species traits
Kissling, W. D., Walls, R., Bowser, A., Jones, M., Kattge, J., Agosti, D., Amengual, J., Basset, A., van Bodegom, P. M., Cornelissen, J. H. C., et al. (2018) · Nature Ecology & Evolution · doi:10.1038/s41559-018-0667-3 · Cited by 217 · see citing works
The unrealized potential of herbaria for global change biology
Meineke, E. K., Davis, C. “., & Davies, T. J. (2018) · Ecological Monographs · doi:10.1002/ecm.1307 · Cited by 200 · see citing works
Self-citationValan, M., Makonyi, K., Maki, A., Vondráček, D., & Ronquist, F. (2019) · Systematic Biology · doi:10.1093/sysbio/syz014 · Cited by 198 · see citing works
Beck, J., Ballesteros‐Mejia, L., Nagel, P., & Kitching, I. J. (2013) · Diversity and Distributions · doi:10.1111/ddi.12083 · Cited by 157 · see citing works
Ballard, H., Robinson, L. D., Young, A. N., Pauly, G., Higgins, L. M., Johnson, R. F., & Tweddle, J. (2017) · Biological Conservation · doi:10.1016/j.biocon.2016.08.040 · Cited by 153 · see citing works
Climate Change and Biosphere Response: Unlocking the Collections Vault
Johnson, K. G., Brooks, S. J., Fenberg, P. B., et al. (2011) · BioScience · doi:10.1525/bio.2011.61.2.10 · Cited by 135 · see citing works
Park, D., Breckheimer, I., Williams, A. C., Law, E., Ellison, A. M., & Davis, C. “. (2018) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2017.0394 · Cited by 131 · see citing works
Self-citationComputer vision cracks the leaf code
Wilf, P., Zhang, S., Chikkerur, S. S., Little, S. A., Wing, S. L., & Serre, T. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1524473113 · Cited by 130 · see citing works
Assessing the Relevance of Herbarium Collections as Tools for Conservation Biology
Nualart, N., Cortina, N. I., Tomàs, I. S. I., et al. (2017) · The Botanical Review · doi:10.1007/s12229-017-9188-z · Cited by 130 · see citing works
Research and Societal Benefits of the Global Biodiversity Information Facility
EDWARDS, J. L. (2004) · BioScience · doi:10.1641/0006-3568(2004)054[0486:rasbot]2.0.co;2 · Cited by 124 · see citing works
Hill, A., Guralnick, R., Smith, A. M., Sallans, A., Gillespie, R. G., Denslow, M., Gross, J., Murrell, Z. E., Conyers, T., Oboyski, P. T., et al. (2012) · ZooKeys · doi:10.3897/zookeys.209.3472 · Cited by 110 · see citing works
Citizen science and conservation: Recommendations for a rapidly moving field
Ellwood, E. R., Crimmins, T., & Miller-Rushing, A. (2017) · Biological Conservation · doi:10.1016/j.biocon.2016.10.014 · Cited by 110 · see citing works
The French Muséum national d’histoire naturelle vascular plant herbarium collection dataset
Bras, G. L., Pignal, M., Jeanson, M. L., et al. (2017) · Scientific Data · doi:10.1038/sdata.2017.16 · Cited by 91 · see citing works
Automated plant identification using artificial neural network and support vector machine
Kho, S. J., Sugumaran, M., Malek, S., et al. (2017) · Frontiers in Life Science · doi:10.1080/21553769.2017.1412361 · Cited by 86 · see citing works
Digitization of museum collections holds the potential to enhance researcher diversity
Drew, J., Moreau, C. S., & Stiassny, M. L. J. (2017) · Nature Ecology & Evolution · doi:10.1038/s41559-017-0401-6 · Cited by 83 · see citing works
iN aturalist as a tool to expand the research value of museum specimens
Heberling, J. M., & Isaac, B. L. (2018) · Applications in Plant Sciences · doi:10.1002/aps3.1193 · Cited by 83 · see citing works
Self-citationAccelerating the Digitization of Biodiversity Research Specimens through Online Public Participation
Ellwood, E. R., Dunckel, B. A., Flemons, P. K. J., Guralnick, R., Nelson, G., Newman, G., Newman, S., Paul, D. L., Riccardi, G., Rios, N., et al. (2015) · BioScience · doi:10.1093/biosci/biv005 · Cited by 81 · see citing works
Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning
Zhou, N., Siegel, Z. D., Zarecor, S., Lee, N., Campbell, D. A., Andorf, C. M., Nettleton, D. S., Lawrence‐Dill, C. J., Ganapathysubramanian, B., Kelly, J. W., et al. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006337 · Cited by 76 · see citing works
Deep Feature Embedding for Accurate Recognition and Retrieval of Handwritten Text
Krishnan, P., Dutta, K., & Jawahar, C. V. (2016) · International Conference on Frontiers in Handwriting Recognition · doi:10.1109/icfhr.2016.0062 · Cited by 75 · see citing works
Unger, J., Merhof, D., & Renner, S. S. (2016) · BMC Evolutionary Biology · doi:10.1186/s12862-016-0827-5 · Cited by 74 · see citing works
Sweeney, P. W., Starly, B., Morris, P. J., et al. (2018) · Taxon · doi:10.12705/671.10 · Cited by 72 · see citing works
No general relationship between mass and temperature in endothermic species
Riemer, K., Guralnick, R., & White, E. P. (2018) · eLife · doi:10.7554/elife.27166 · Cited by 71 · see citing works
Lorieul, T., Pearson, K. D., Ellwood, E. R., et al. (2019) · Applications in Plant Sciences · doi:10.1002/aps3.1233 · Cited by 71 · see citing works
Willis, C. G., Law, E., Williams, A. C., Franzone, B. F., Bernardos, R. L., Bruno, L. G., Hopkins, C., Schorn, C., Weber, E., Park, D., et al. (2017) · New Phytologist · doi:10.1111/nph.14535 · Cited by 65 · see citing works
Self-citationApplications of deep convolutional neural networks to digitized natural history collections
Schuettpelz, E., Frandsen, P. B., Dikow, R. B., et al. (2017) · Biodiversity Data Journal · doi:10.3897/bdj.5.e21139 · Cited by 58 · see citing works
Digitization protocol for scoring reproductive phenology from herbarium specimens of seed plants
Yost, J., Sweeney, P. W., Gilbert, E., et al. (2018) · Applications in Plant Sciences · doi:10.1002/aps3.1022 · Cited by 58 · see citing works
Contribution of Natural History Collection Data to Biodiversity Assessment in National Parks
O’Connell, A. F., GILBERT, A. T., & Hatfield, J. S. (2004) · Conservation Biology · doi:10.1111/j.1523-1739.2004.00034.x-i1 · Cited by 54 · see citing works
MacDougall, A. S., Loo, J. A., Clayden, S. R., et al. (1998) · Biological Conservation · doi:10.1016/s0006-3207(98)00031-7 · Cited by 53 · see citing works
Tegelberg, R., Mononen, T., & Saarenmaa, H. (2014) · Taxon · doi:10.12705/636.13 · Cited by 52 · see citing works
Cobb, N. S., Gall, L. F., Zaspel, J. M., Dowdy, N. J., McCabe, L. M., & Kawahara, A. Y. (2019) · PeerJ · doi:10.7717/peerj.8086 · Cited by 51 · see citing works
Gutiérrez‐Larruscain, D., Vicente, M. S., Anderberg, A. A., et al. (2018) · Taxon · doi:10.12705/671.9 · Cited by 42 · see citing works
Park, D., & Potter, D. (2015) · Molecular Ecology · doi:10.1111/mec.13227 · Cited by 40 · see citing works
Self-citationThe importance of digitized biocollections as a source of trait data and a new VertNet resource
Guralnick, R., Zermoglio, P., Wieczorek, J. R., LaFrance, R., Bloom, D., & Russell, L. (2016) · Database · doi:10.1093/database/baw158 · Cited by 39 · see citing works
A software system for automated identification and retrieval of moth images based on wing attributes
Feng, L., Bhanu, B., & Heraty, J. M. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.09.012 · Cited by 38 · see citing works
Chang, J., & Alfaro, M. E. (2015) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12508 · Cited by 32 · see citing works
Kurator: A Kepler Package for Data Curation Workflows
Dou, L., Cao, G. T. A. N., Morris, P. J., et al. (2012) · Procedia Computer Science · doi:10.1016/j.procs.2012.04.177 · Cited by 27 · see citing works
The time has come for Natural History Collections to claim co‐authorship of research articles
Rouhan, G., Dorr, L. J., Gautier, L., et al. (2017) · Taxon · doi:10.12705/665.2 · Cited by 21 · see citing works
Rogers, N. (2016) · Science · doi:10.1126/science.352.6287.762 · Cited by 18 · see citing works
Schermer, M., & Hogeweg, L. (2018) · Biodiversity Information Science and Standards · doi:10.3897/biss.2.25268 · Cited by 17 · see citing works
The geometry of taking flight: Limb morphometrics in Mesozoic theropods
Hedrick, B. P., Manning, P. L., Lynch, E., Cordero, S. A., & Dodson, P. (2014) · Journal of Morphology · doi:10.1002/jmor.20329 · Cited by 12 · see citing works
Self-citationBayesian Inference from Count Data Using Discrete Uniform Priors
Comoglio, F., Fracchia, L., & Rinaldi, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0074388 · Cited by 8 · see citing works
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Data integration enables global biodiversity synthesis
Heberling, J. M., Miller, J. T., Noesgaard, D., Weingart, S. B., & Schigel, D. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2018093118 · Cited by 380 · see citing works
Self-citationHigh-throughput phenomics of global ant biodiversity
Katzke, J., Hita Garcia, F., Lösel, P. D., Azuma, F., Faragó, T., Aibekova, L., Casadei-Ferreira, A., Gautam, S., Richter, A., Toulkeridou, E., et al. (2026) · Nature Methods · doi:10.1038/s41592-026-03005-0 · Cited by 6 · see citing works
“X-ray computed microtomography (micro-CT) can non-destructively digitize valuable collection material in 3D 10,11 , but substantial challenges must be overcome to achieve a phenomic big data revolution across the millions of animal species 9,10,12–14 .”
- Authors
- Hedrick, B. P., Heberling, J. M., Meineke, E. K., Turner, K. G., Grassa, C. J., Park, D. S., Kennedy, J., Clarke, J. A., Cook, J. A., Blackburn, D. C., Edwards, S. V., & Davis, C. C.
- Affiliations
- Harvard University (US)
- Carnegie Museum of Natural History (US)
- Idaho State University (US)
- The University of Texas at Austin (US)
- University of New Mexico (US)
- Florida Museum of Natural History (US)
- Corresponding author
- Hedrick, Brandon P
- Davis, Charles C
- ORCID
- Venue
- BioScience
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-02-07
- Volume
- 70
- Issue
- 3
- Pages
- 243-251
- ISSN
- 0006-3568 (ISSN-L)
- 1525-3244
- OpenAlex
- W2959883904
- Cited by
- 306
- References
- 61
- Citations by year
- 2026: 46 · 2025: 57 · 2024: 52 · 2023: 43 · 2022: 49 · 2021: 40 · 2020: 19
- FWCI
- 35.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- BioScience (publishedVersion)
- Funders
- National Science Foundation (1441785, 1746177, EF1208835, 1754584)
- Topics
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
- Physical Sciences › Environmental Science › Ecology › Environmental DNA in Biodiversity Studies
- Keywords
- natural history collections, digitization, specimen records, biodiversity assessment, biodiversity discovery, conservation, global change, digital curation, annotation, biodiversity data infrastructure
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