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Title: Collections do not have to Remain Ambiguous Forever: Seven steps to getting the correct people into your data
People are involved with the collection and curation of all biodiversity data, whether they are researchers, members of the public, taxonomists, conservationists, collection managers or wildlife managers. Knowing who those people are and connecting their biographical information to the biodiversity data they collect helps us contextualise their scientific work. We are particularly concerned with those people and communities involved in the collection and identification of biological specimens. People from herbaria and natural science museums have been collecting and preserving specimens from all over the world for more than 200 years. The problem is that many of these people are only known by unstandardized names written on specimen labels, often with only initials and without any biographical information. The process of identifying and linking individuals to their biographies enables us to improve the quality of the data held by collections while also quantifying the contributions of the often underappreciated people who collected and identified these specimens. This process improves our understanding of the history of collecting, and addresses current and future needs for maintaining the provenance of specimens so as to comply with national and international practices and regulations. In this talk we will outline the steps that collection managers, data scientists, curators, software engineers, and collectors can take to work towards fully disambiguated collections. With examples, we can show how they can use these data to help them in their work, in the evaluation of their collections, and in measuring the impact of individuals and organisations, local to global.  more » « less
Award ID(s):
2033973
NSF-PAR ID:
10377060
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Biodiversity Information Science and Standards
Volume:
6
ISSN:
2535-0897
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ; ; ; ; et al ( , Biodiversity Data Journal)
    Scientific collections have been built by people. For hundreds of years, people have collected, studied, identified, preserved, documented and curated collection specimens. Understanding who those people are is of interest to historians, but much more can be made of these data by other stakeholders once they have been linked to the people’s identities and their biographies. Knowing who people are helps us attribute work correctly, validate data and understand the scientific contribution of people and institutions. We can evaluate the work they have done, the interests they have, the places they have worked and what they have created from the specimens they have collected. The problem is that all we know about most of the people associated with collections are their names written on specimens. Disambiguating these people is the challenge that this paper addresses. Disambiguation of people often proves difficult in isolation and can result in staff or researchers independently trying to determine the identity of specific individuals over and over again. By sharing biographical data and building an open, collectively maintained dataset with shared knowledge, expertise and resources, it is possible to collectively deduce the identities of individuals, aggregate biographical information for each person, reduce duplication of effort and share the information locally and globally. The authors of this paper aspire to disambiguate all person names efficiently and fully in all their variations across the entirety of the biological sciences, starting with collections. Towards that vision, this paper has three key aims: to improve the linking, validation, enhancement and valorisation of person-related information within and between collections, databases and publications; to suggest good practice for identifying people involved in biological collections; and to promote coordination amongst all stakeholders, including individuals, natural history collections, institutions, learned societies, government agencies and data aggregators. 
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  2. ; ; ; ; ; ; ; ; ; ; et al ( , Zenodo)

    PLEASE CONTACT AUTHORS IF YOU CONTRIBUTE AND WOULD LIKE TO BE LISTED AS A CO-AUTHOR. (this message will be removed some time weeks/months after the first publication)

    Terrestrial Parasite Tracker indexed biotic interactions and review summary.

    The Terrestrial Parasite Tracker (TPT) project began in 2019 and is funded by the National Science foundation to mobilize data from vector and ectoparasite collections to data aggregators (e.g., iDigBio, GBIF) to help build a comprehensive picture of arthropod host-association evolution, distributions, and the ecological interactions of disease vectors which will assist scientists, educators, land managers, and policy makers. Arthropod parasites often are important to human and wildlife health and safety as vectors of pathogens, and it is critical to digitize these specimens so that they, and their biotic interaction data, will be available to help understand and predict the spread of human and wildlife disease.

    This data publication contains versioned TPT associated datasets and related data products that were tracked, reviewed and indexed by Global Biotic Interactions (GloBI) and associated tools. GloBI provides open access to finding species interaction data (e.g., predator-prey, pollinator-plant, pathogen-host, parasite-host) by combining existing open datasets using open source software.

    If you have questions or comments about this publication, please open an issue at https://github.com/ParasiteTracker/tpt-reporting or contact the authors by email.

    Funding:
    The creation of this archive was made possible by the National Science Foundation award "Collaborative Research: Digitization TCN: Digitizing collections to trace parasite-host associations and predict the spread of vector-borne disease," Award numbers DBI:1901932 and DBI:1901926

    References:
    Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005.

    GloBI Data Review Report

    Datasets under review:
     - University of Michigan Museum of Zoology Insect Division. Full Database Export 2020-11-20 provided by Erika Tucker and Barry Oconner. accessed via https://github.com/EMTuckerLabUMMZ/ummzi/archive/6731357a377e9c2748fc931faa2ff3dc0ce3ea7a.zip on 2022-06-24T14:02:48.801Z
     - Academy of Natural Sciences Entomology Collection for the Parasite Tracker Project accessed via https://github.com/globalbioticinteractions/ansp-para/archive/5e6592ad09ec89ba7958266ad71ec9d5d21d1a44.zip on 2022-06-24T14:04:22.091Z
     - Bernice Pauahi Bishop Museum, J. Linsley Gressitt Center for Research in Entomology accessed via https://github.com/globalbioticinteractions/bpbm-ent/archive/c085398dddd36f8a1169b9cf57de2a572229341b.zip on 2022-06-24T14:04:37.692Z
     - Texas A&M University, Biodiversity Teaching and Research Collections accessed via https://github.com/globalbioticinteractions/brtc-para/archive/f0a718145b05ed484c4d88947ff712d5f6395446.zip on 2022-06-24T14:06:40.154Z
     - Brigham Young University Arthropod Museum accessed via https://github.com/globalbioticinteractions/byu-byuc/archive/4a609ac6a9a03425e2720b6cdebca6438488f029.zip on 2022-06-24T14:06:51.420Z
     - California Academy of Sciences Entomology accessed via https://github.com/globalbioticinteractions/cas-ent/archive/562aea232ec74ab615f771239451e57b057dc7c0.zip on 2022-06-24T14:07:16.371Z
     - Clemson University Arthropod Collection accessed via https://github.com/globalbioticinteractions/cu-cuac/archive/6cdcbbaa4f7cec8e1eac705be3a999bc5259e00f.zip on 2022-06-24T14:07:40.925Z
     - Denver Museum of Nature and Science (DMNS) Parasite specimens (DMNS:Para) accessed via https://github.com/globalbioticinteractions/dmns-para/archive/a037beb816226eb8196533489ee5f98a6dfda452.zip on 2022-06-24T14:08:00.730Z
     - Field Museum of Natural History IPT accessed via https://github.com/globalbioticinteractions/fmnh/archive/6bfc1b7e46140e93f5561c4e837826204adb3c2f.zip on 2022-06-24T14:18:51.995Z
     - Illinois Natural History Survey Insect Collection accessed via https://github.com/globalbioticinteractions/inhs-insects/archive/38692496f590577074c7cecf8ea37f85d0594ae1.zip on 2022-06-24T14:19:37.563Z
     - UMSP / University of Minnesota / University of Minnesota Insect Collection accessed via https://github.com/globalbioticinteractions/min-umsp/archive/3f1b9d32f947dcb80b9aaab50523e097f0e8776e.zip on 2022-06-24T14:20:27.232Z
     - Milwaukee Public Museum Biological Collections Data Portal accessed via https://github.com/globalbioticinteractions/mpm/archive/9f44e99c49ec5aba3f8592cfced07c38d3223dcd.zip on 2022-06-24T14:20:46.185Z
     - Museum for Southern Biology (MSB) Parasite Collection accessed via https://github.com/globalbioticinteractions/msb-para/archive/178a0b7aa0a8e14b3fe953e770703fe331eadacc.zip on 2022-06-24T15:16:07.223Z
     - The Albert J. Cook Arthropod Research Collection accessed via https://github.com/globalbioticinteractions/msu-msuc/archive/38960906380443bd8108c9e44aeff4590d8d0b50.zip on 2022-06-24T16:09:40.702Z
     - Ohio State University Acarology Laboratory accessed via https://github.com/globalbioticinteractions/osal-ar/archive/876269d66a6a94175dbb6b9a604897f8032b93dd.zip on 2022-06-24T16:10:00.281Z
     - Frost Entomological Museum, Pennsylvania State University accessed via https://github.com/globalbioticinteractions/psuc-ento/archive/30b1f96619a6e9f10da18b42fb93ff22cc4f72e2.zip on 2022-06-24T16:10:07.741Z
     - Purdue Entomological Research Collection accessed via https://github.com/globalbioticinteractions/pu-perc/archive/e0909a7ca0a8df5effccb288ba64b28141e388ba.zip on 2022-06-24T16:10:26.654Z
     - Texas A&M University Insect Collection accessed via https://github.com/globalbioticinteractions/tamuic-ent/archive/f261a8c192021408da67c39626a4aac56e3bac41.zip on 2022-06-24T16:10:58.496Z
     - University of California Santa Barbara Invertebrate Zoology Collection accessed via https://github.com/globalbioticinteractions/ucsb-izc/archive/825678ad02df93f6d4469f9d8b7cc30151b9aa45.zip on 2022-06-24T16:12:29.854Z
     - University of Hawaii Insect Museum accessed via https://github.com/globalbioticinteractions/uhim/archive/53fa790309e48f25685e41ded78ce6a51bafde76.zip on 2022-06-24T16:12:41.408Z
     - University of New Hampshire Collection of Insects and other Arthropods UNHC-UNHC accessed via https://github.com/globalbioticinteractions/unhc/archive/f72575a72edda8a4e6126de79b4681b25593d434.zip on 2022-06-24T16:12:59.500Z
     - Scott L. Gardner and Gabor R. Racz (2021). University of Nebraska State Museum - Parasitology. Harold W. Manter Laboratory of Parasitology. University of Nebraska State Museum. accessed via https://github.com/globalbioticinteractions/unl-nsm/archive/6bcd8aec22e4309b7f4e8be1afe8191d391e73c6.zip on 2022-06-24T16:13:06.914Z
     - Data were obtained from specimens belonging to the United States National Museum of Natural History (USNM), Smithsonian Institution, Washington DC and digitized by the Walter Reed Biosystematics Unit (WRBU). accessed via https://github.com/globalbioticinteractions/usnmentflea/archive/ce5cb1ed2bbc13ee10062b6f75a158fd465ce9bb.zip on 2022-06-24T16:13:38.013Z
     - US National Museum of Natural History Ixodes Records accessed via https://github.com/globalbioticinteractions/usnm-ixodes/archive/c5fcd5f34ce412002783544afb628a33db7f47a6.zip on 2022-06-24T16:13:45.666Z
     - Price Institute of Parasite Research, School of Biological Sciences, University of Utah accessed via https://github.com/globalbioticinteractions/utah-piper/archive/43da8db550b5776c1e3d17803831c696fe9b8285.zip on 2022-06-24T16:13:54.724Z
     - University of Wisconsin Stevens Point, Stephen J. Taft Parasitological Collection accessed via https://github.com/globalbioticinteractions/uwsp-para/archive/f9d0d52cd671731c7f002325e84187979bca4a5b.zip on 2022-06-24T16:14:04.745Z
     - Giraldo-Calderón, G. I., Emrich, S. J., MacCallum, R. M., Maslen, G., Dialynas, E., Topalis, P., … Lawson, D. (2015). VectorBase: an updated bioinformatics resource for invertebrate vectors and other organisms related with human diseases. Nucleic acids research, 43(Database issue), D707–D713. doi:10.1093/nar/gku1117. accessed via https://github.com/globalbioticinteractions/vectorbase/archive/00d6285cd4e9f4edd18cb2778624ab31b34b23b8.zip on 2022-06-24T16:14:11.965Z
     - WIRC / University of Wisconsin Madison WIS-IH / Wisconsin Insect Research Collection accessed via https://github.com/globalbioticinteractions/wis-ih-wirc/archive/34162b86c0ade4b493471543231ae017cc84816e.zip on 2022-06-24T16:14:29.743Z
     - Yale University Peabody Museum Collections Data Portal accessed via https://github.com/globalbioticinteractions/yale-peabody/archive/43be869f17749d71d26fc820c8bd931d6149fe8e.zip on 2022-06-24T16:23:29.289Z

    Generated on:
    2022-06-24

    by:
    GloBI's Elton 0.12.4 
    (see https://github.com/globalbioticinteractions/elton).

    Note that all files ending with .tsv are files formatted 
    as UTF8 encoded tab-separated values files.

    https://www.iana.org/assignments/media-types/text/tab-separated-values


    Included in this review archive are:

    README:
      This file.

    review_summary.tsv:
      Summary across all reviewed collections of total number of distinct review comments.

    review_summary_by_collection.tsv:
      Summary by reviewed collection of total number of distinct review comments.

    indexed_interactions_by_collection.tsv: 
      Summary of number of indexed interaction records by institutionCode and collectionCode.

    review_comments.tsv.gz:
      All review comments by collection.

    indexed_interactions_full.tsv.gz:
      All indexed interactions for all reviewed collections.

    indexed_interactions_simple.tsv.gz:
      All indexed interactions for all reviewed collections selecting only sourceInstitutionCode, sourceCollectionCode, sourceCatalogNumber, sourceTaxonName, interactionTypeName and targetTaxonName.

    datasets_under_review.tsv:
      Details on the datasets under review.

    elton.jar: 
      Program used to update datasets and generate the review reports and associated indexed interactions.

    datasets.zip:
      Source datasets used by elton.jar in process of executing the generate_report.sh script.

    generate_report.sh:
      Program used to generate the report

    generate_report.log:
      Log file generated as part of running the generate_report.sh script
     

     
    more » « less
  3. ; ; ; ( , Biodiversity Information Science and Standards)
    null (Ed.)
    A wealth of information about how parasites interact with their hosts already exists in collections, scientific publications, specialized databases, and grey literature. The US National Science Foundation-funded Terrestrial Parasite Tracker Thematic Collection Network (TPT) project began in 2019 to help build a comprehensive picture of arthropod ectoparasites including the evolution of these parasite-host biotic associations, distributions, and the ecological interactions of disease vectors. TPT is a network of biodiversity collections whose data can assist scientists, educators, land managers, and policymakers to better understand the complex relationship between hosts and parasites including emergent properties that may explain the causes and frequency of human and wildlife pathogens. TPT member collections make their association information easier to access via Global Biotic Interactions (GloBI, Poelen et al. 2014), which is periodically archived through Zenodo to track progress in the TPT project. TPT leverages GloBI's ability to index biotic associations from specimen occurrence records that come from existing management systems (e.g., Arctos, Symbiota, EMu, Excel, MS Access) to avoid having to completely rework existing, or build new, cyber-infrastructures before collections can share data. TPT-affiliated collection managers use collection-specific translation tables to connect their verbatim (or original) terms used to describe associations (e.g., "ex", "found on", "host") to their interpreted, machine-readable terms in the OBO Relations Ontology (RO). These interpreted terms enable searches across previously siloed association record sets, while the original verbatim values remain accessible to help retain provenance and allow for interpretation improvements. TPT is an ambitious project, with the goal to database label data from over 1.2 million specimens of arthropod parasites of vertebrates coming from 22 collections across North America. In the first year of the project, the TPT collections created over 73,700 new records and 41,984 images. In addition, 17 TPT data providers and three other collaborators shared datasets that are now indexed by GloBI, visible on the TPT GloBI project page. These datasets came from collection specimen occurrence records and literature sources. Two TPT data archives that capture and preserve the changes in the data coming from TPT to GloBI were published through Zenodo (Poelen et al. 2020a, Poelen et al. 2020b). The archives document the changes in how data are shared by collections including the biotic association data format and quantity of data captured. The Poelen et al. 2020b report included all TPT collections and biotic interactions from Arctos collections in VertNet and the Symbiota Collection of Arthropods Network (SCAN). The total number of interactions included in this report was 376,671 records (500,000 interactions is the overall goal for TPT). In addition, close coordination with TPT collection data managers including many one-on-one conversations, a workshop, and a webinar (Sullivan et al. 2020) was conducted to help guide the data capture of biotic associations. GloBI is an effective tool to help integrate biotic association data coming from occurrence records into an openly accessible, global, linked view of existing species interaction records. The results gleaned from the TPT workshop and Zenodo data archives demonstrate that minimizing changes to existing workflows allow for custom interpretation of collection-specific interaction terms. In addition, including collection data managers in the development of the interaction term vocabularies is an important part of the process that may improve data sharing and the overall downstream data quality. 
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  4. ; ; ; ( , Biodiversity Information Science and Standards)
    Elmer Ottis Wooton (1865–1945) was one of the most important early botanists to work in the Southwestern United States, contributing a great deal of natural history knowledge and botanical research on the flora of New Mexico that shaped many naturalists and scientists for generations. The extensive Wooton legacy includes herbarium collections that he and his famous student Paul Carpenter Standley (1884–1963), prolific botanist and explorer, used for the first Flora of New Mexi co by Wooten and Standley 1915 , along with resources covering botany and range management strategies for the northern Chihuahuan Desert, and an extensive, yet to be digitized, historical archive of correspondence, field notes, vegetation sketches, photographs, and lantern slides, all from his travels and field work in the region. Starting in 1890, the most complete set of Wooton’s herbarium collections were deposited in the NMC herbarium at New Mexico State University (NMSU), and his archives, now stored in a Campus library, have together been underutilized, offline resources. The goals of this ongoing project are to secure, preserve, and promote Wooton’s important historical resources, by fleshing out the botanical history of the region, raising appreciation of herbarium collections within the community, and emphasizing their unique role in facilitating contemporary research aimed at addressing pressing scientific questions such as vegetation responses to global climate change. Students and the general public involved in this project are engaged through hands-on activities including cataloging, databasing and digitization of nearly 10,000 herbarium specimens and Wooton’s archives. These outputs, combined with contemporary data collection and computational biology techniques from an ecological perspective, are being used to document vegetation changes in iconic, climate-sensitive, high-elevation mountainous ecosystems present in southwestern New Mexico. In a later phase of the project, a variety of public audiences will participate through interactive online story maps and citizen science programs such as iNaturalist , Notes from Nature , and BioBlitz . Images of herbarium specimens will be shared via an online database and other relevant biodiversity portals ( Symbiota , iDigBio , JStor ) Community members reached through this project will be better-informed citizens, who may go on to become new stewards of natural history collections, with the potential to influence policies safeguarding the future of our planet’s biodiversity. More locally, the project will support the management of Organ Mountains Desert Peaks National Monument, which was established in 2014 to protect the area's human and environmental resources, and for which knowledge and data are currently limited. 
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  5. ; ; ; ; ; ; ( , International Journal on Digital Libraries)
    Adam, N. ; Neuhold, E. ; Furuta, R. (Ed.)
    Metadata is a key data source for researchers seeking to apply machine learning (ML) to the vast collections of digitized biological specimens that can be found online. Unfortunately, the associated metadata is often sparse and, at times, erroneous. This paper extends previous research conducted with the Illinois Natural History Survey (INHS) collection (7244 specimen images) that uses computational approaches to analyze image quality, and then automatically generates 22 metadata properties representing the image quality and morphological features of the specimens. In the research reported here, we demonstrate the extension of our initial work to University of the Wisconsin Zoological Museum (UWZM) collection (4155 specimen images). Further, we enhance our computational methods in four ways: (1) augmenting the training set, (2) applying contrast enhancement, (3) upscaling small objects, and (4) refining our processing logic. Together these new methods improved our overall error rates from 4.6 to 1.1%. These enhancements also allowed us to compute an additional set of 17 image-based metadata properties. The new metadata properties provide supplemental features and information that may also be used to analyze and classify the fish specimens. Examples of these new features include convex area, eccentricity, perimeter, skew, etc. The newly refined process further outperforms humans in terms of time and labor cost, as well as accuracy, providing a novel solution for leveraging digitized specimens with ML. This research demonstrates the ability of computational methods to enhance the digital library services associated with the tens of thousands of digitized specimens stored in open-access repositories world-wide by generating accurate and valuable metadata for those repositories. 
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