ORCID Profile
0000-0003-1700-6962
Current Organisation
Australian Biodiversity Information Services
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Publisher: Pensoft Publishers
Date: 29-08-2023
Abstract: In the fifty years since I became a scientist, there have been many changes in the way we work, in the tools we use, in the software we have available to us and much more. But our motivation to “Do what we do” hasn’t really changed at all. A survey carried out many years ago in Brazil asked biologists why they became biologists. Answers included going to the beach with the family and making shell collections and then trying to identify them being involved in backyard bird surveys catching butterflies in the local meadows, etc. In my own case, it was working on the farm with my father who, at least to me at the time, knew every native plant in the area as well as the grasses and crop plants. I began collecting and pressing grasses at a young age, and those collections are now housed in the herbaria of the University of New England and James Cook University of North Queensland. I am sure that most of you here have similar stories. In this presentation, I talk about the changes that I have seen over those fifty years in the way we work, including working at the Australian Bio ersity Resources Study (ABRS) on the Australian Plant Name Index (APNI) and the Flora of Australia the Environmental Resources Information Network (ERIN) on digitisation projects environmental, species and climate change modelling data quality and decision support systems with the Centro de Referência em Informação Ambiental (CRIA) on data quality and niche modelling and on projects for the Global Bio ersity Information Facility (GBIF), The Global Environment Facility of the World Bank, United Nations Environment Programme (UNEP), Bioversity International, and others on a range of topics including on the conservation of crop wild relatives, reserve selection, rapid bio ersity assessment, data quality, and georeferencing—and, of course, Bio ersity Information Standards (TDWG). I’ll also touch on working with citizen science and tools such as iNaturalist. One key change in the way we work now compared to those early years is in the way we collaborate. A lot of that collaboration has been enhanced and made possible by the Internet: email, the World Wide Web, video conferencing, and chat tools like Zoom. Previously we published some research and then later another person would see that research and add to it, etc. This could take years for advancements to take place. Now, we collaborate: across institutions, countries and across the globe and that has sped up the rate our science can advance. TDWG has been a great ex le of that collaboration, along with agencies such as GBIF. So where will our science take us over the next fifty years? I have no idea, but the advances are likely to be at least as great as the last fifty, so I envy the young scientists in the audience just starting out and getting to grips with artificial intelligence (AI) and all that is likely to bring. We should be wary of AI—but not scared of it—just like computing fifty years ago. In conclusion, what then motivates us to continue to “Do what we do” well past retirement age? In my own case, my motivation throughout my life, has been to add to the bio ersity knowledge base so that those who come after me start at a far better knowledge level than would otherwise have been the case.
Publisher: Pensoft Publishers
Date: 15-08-2023
Abstract: GeoPick is a new web application aimed at providing a simple yet powerful georeferencing tool to the natural history collections community (Fig. 1). Its conceptual foundation is based on the Georeferencing Best Practices by Chapman and Wieczorek (2020), whose guidelines it intends to implement. GeoPick also provides a close and direct relation between the tool’s output and the Darwin Core standard (Wieczorek et al. 2012). In the past two decades, institutions across the world have devoted significant resources to digitise their collections and bring them closer to their final users (Nelson and Ellis 2018), i.e., the research, conservation and education communities, and to the general public. Georeferencing is an important part of the digitisation process (Nelson et al. 2012). It provides the link between the preserved specimen and the natural habitat where it lived (Bloom et al. 2017). However, there is a gap between the data that have already been digitised and made accessible through public repositories such as the Global Bio ersity Information Facility (GBIF) and the expected fully georeferenced information, including crucial information on coordinate uncertainty and precision, which is needed to conduct rigorous studies based upon digitised specimens (Marcer et al. 2022). In order to determine the reasons behind this mismatch between optimal and actual georeferenced data, we conducted a global survey on current georeferencing practices (Marcer et al. 2021a), which was used as the basis for discussion in a workshop organised by the MOBILISE Cost Action that took place in Warsaw (Poland) in 2020 (Marcer et al. 2020, Marcer et al. 2021a). Among other recommendations that came out of the workshop was the need to develop more user-friendly tools that help georeferencers follow standards and guidelines in a more effective way. According to the survey, only 16 percent of respondents used a specific georeferencing tool such as GeoLocate (Rios 2019), while the rest only used general tools (Google Maps/Earth was reported by 47% of the respondents). Moreover, about 75% of the respondents reported only following in-house ad hoc protocols or none at all. This discouraging situation and the need for a tool that is easy to use, yet effective, was discussed informally with attendees at the 2022 Bio ersity Information Standards (TDWG) and Society for the Preservation of Natural History Collections (SPNHC) conferences. The current version of GeoPick is the first step towards a tool that is powerful, accessible and simple enough to help georeferencers follow standards and best practices in order to improve the data that are finally made accessible through repositories such as GBIF. In this first version, GeoPick implements the point-radius (Wieczorek et al. 2004) and shape (Chapman and Wieczorek 2020) georeferencing methods. It calculates the centroid or corrected centre when necessary, the associated coordinate uncertainty, the spatial fits of the point-radius and the footprint, and provides the digitised geometry as Well-Known Text (WKT). The tool offers a map navigator with reference cartography and editing tools to digitise points, lines or polygons representing the locality description of a given location. Data are finally exported via the clipboard in Darwin Core standard format. The tool also offers search functionality for geographic features from OpenStreetMap data through the Nominatim API. The user can search and import a geometry provided by Nominatim and use it as the basis of a georeference. Technical implementation: HTML and JavaScript browser front end that accesses a private API developed in R (R Core Team 2022). Source code availabe at GitHub. License : GeoPick is open source with a AGPLv3 license. Public URL : geopick.gbif.org (available starting October 2023)
Publisher: Pensoft Publishers
Date: 08-2022
DOI: 10.3897/BISS.6.91078
Abstract: Data Quality Task Group 2 was established to create a suite of core tests and associated assertions about the 'quality' of bio ersity informatics data (Chapman et al. 2020). The group has been active since January 2017, about four years longer than its four main members would have anticipated. We all thought “How hard could it be?” The answer was “Harder than we thought!” We have invested well over two years full time into this project. There were multiple times over the past five years where we thought we were 95% done, but we were wrong. Were we dumb? I doubt it! The authors (other than the lead author) are highly experienced in bio ersity data quality, Darwin Core and data testing. Neither were we lazy. Why has it gone so slowly? It is mostly due to the complexity of the task and the inability to meet face-to-face. Zoom just doesn’t cut it for this type of work. We achieved the most at our one face-to-face meeting in Gainesville (Florida) in 2018. Our advances over the past year have come from rounds of feedback between the test specifications, test implementation, development of data for validating the tests and comparison between results from implementations and the expectations of the validation data. There are hopefully useful lessons in this for similar projects. We now have a solid base where future evolution, such as tests for specific environments, will be made relatively easy. The major components of this project are the 99 tests themselves, the parameters for these tests (see dwg/bdq/issues/122), a vocabulary of the terms used in the framework and test data for validating implementations of the tests. We remain focused on what we call core tests: those that provide power in evaluating ‘fitness for use’, are widely applicable and are relatively easy to implement. The test descriptions we have settled on are: A human readable label (split into a test class, a target Darwin Core term and an ‘action’) A Globally Unique Identifier for the test (a GUID) A simple English description Test class from the Fitness-For-Use Framework (Data Quality Task Group 1): Validation, Amendment, Measure or Issue Resource Type (all of the Core tests operate on a single record) Information Elements (specified as the applicable Darwin Core Class and as a list of specific Darwin Core terms required as inputs for the test) Specification (an explanation of how the test works from an implementation perspective) Data quality dimension (from the Fitness-for-Use Framework) Warning type (ambiguous, amended, incomplete, invalid, issue, report, unlikely) Parameters (options that allow implementations to behave differently in clearly defined ways such as the use of a national species list) Source Authority (external references required by the test) An ex le Source (the origin of the test) References Link to reference implementations Link to source code and Notes (explanations of subtle or not so subtle aspects of the test). A human readable label (split into a test class, a target Darwin Core term and an ‘action’) A Globally Unique Identifier for the test (a GUID) A simple English description Test class from the Fitness-For-Use Framework (Data Quality Task Group 1): Validation, Amendment, Measure or Issue Resource Type (all of the Core tests operate on a single record) Information Elements (specified as the applicable Darwin Core Class and as a list of specific Darwin Core terms required as inputs for the test) Specification (an explanation of how the test works from an implementation perspective) Data quality dimension (from the Fitness-for-Use Framework) Warning type (ambiguous, amended, incomplete, invalid, issue, report, unlikely) Parameters (options that allow implementations to behave differently in clearly defined ways such as the use of a national species list) Source Authority (external references required by the test) An ex le Source (the origin of the test) References Link to reference implementations Link to source code and Notes (explanations of subtle or not so subtle aspects of the test). The composition of the core tests has been stable for over a year. We have generated most of the test data using the template: the applicable test, a unique identifier, input data, expected output data, the response status (e.g., “internal prerequisites not met”), the response result (e.g., “not compliant”), and an optional comment. What remains to be done? We need to complete the test data, produce normative and non-normative documentation, and transform our work into a TDWG Technical Specification. While TG2 is over 95% complete, we would still welcome anyone who is interested to learn about bio ersity data quality to contribute.
Publisher: GBIF Secretariat
Date: 2006
Publisher: Zenodo
Date: 2020
Publisher: Pensoft Publishers
Date: 08-2022
DOI: 10.3897/BISS.6.91062
Abstract: Names are essential for communication. In bio ersity we have a nomenclature system that has stood the test of time (around 270 years) and, despite some shortcomings, it works. However, the world has changed. Extinction rates have increased rapidly in recent times and are rising at ever increasing rates due to climate change and human neglect. As a result, we need to do everything we can to protect the species that remain and, to do that, we need to be able to communicate about those species. The publishing process is slow, and there is a dearth of taxonomists, so the formal publication for many of these species, especially in the tropics and the New World, cannot keep up with the ever-increasing known unpublished species. It is estimated (Chapman 2009) that only about 16% of the world’s species have been described. In plants that figure is around 65% described, with 35% still undescribed. Many of these are known, and many are threatened, but unless we give them names, we cannot adequately communicate about them, exchange data on them, or add them into conservation legislation. This includes being able to identify photos, etc. in citizen science apps such as iNaturalist, which can be important in determining ranges, identifying new taxa, and for taxonomists and other researchers. In the 1980s, Australia developed a formulaic naming system for undescribed plant species (Chapman 2005). The formulaic name follows the format: "& Genus& sp. & colloquial name, location or description& (& Voucher& )" e.g., " Prostanthera sp. Somersbey (B.J.Conn 4024)". This was universally adopted in Australia in the 1990s, and allowed, from 1999, the inclusion of these undescribed taxa in the legislated National and State threatened species lists. As of May 2022, there were 41 such taxa listed in the Australian Environment Protection and Bio ersity Conservation Act 1999 (EPBC Act). Such formulaic names can be synonymised as new names are formally described. A more detailed discussion of the formulaic method was given on pages 20-21 in Chapman (2005). Some have said that this is a trivial issue, but it is not trivial, and we will give evidence of this by looking at just one small part of the world – Western Australia – where there are currently 987 currently accepted undescribed taxa out of an estimated flora of about 13,000 that use this formulaic naming system (florabase.dpaw.wa.gov.au/). That is around 7.5% of all plant species in the state. Of these 987 taxa, over 50% are listed as Conservation Priority taxa (calculated from the florabase reference). This is not just an Australian problem, or just a plant problem, and thus we need such a system formalised into a TDWG standard. This would allow for consistency across the globe and across life kingdoms and allow for the transfer of data through data aggregators such as the Global Bio ersity Information Facility (GBIF). We also need citizen science apps such as iNaturalist to allow for the inclusion of such names in their taxonomies, otherwise we lose a lot of important information on some of the most valuable and threatened taxa in the world. While looking at the benefits, we must also look at some of the drawbacks e.g., physical piracy of rare taxa, and taxonomic piracy. It has been suggested that in some taxonomic groups, tying a formula name to a voucher, and especially where there is a link to a photo on iNaturalist, could encourage taxon pirates to describe and publish the taxa as new taxa in self-published journals without having examined any material. These issues need to be discussed, but we believe they are not reasons to deny support of the concept of formulaic names, even if different formats are needed for different taxonomic groups.
Publisher: FapUNIFESP (SciELO)
Date: 2022
Publisher: Pensoft Publishers
Date: 18-05-2018
DOI: 10.3897/BISS.2.25608
Abstract: Task Group 2 of the TDWG Data Quality Interest Group aims to provide a standard suite of tests and resulting assertions that can assist with filtering occurrence records for as many applications as possible. Currently ‘data aggregators’ such as the Global Bio ersity Information Facility (GBIF), the Atlas of Living Australia (ALA) and iDigBio run their own suite of tests over records received and report the results of these tests (the assertions): there is, however, no standard reporting mechanisms. We reasoned that the availability of an internationally agreed set of tests would encourage implementations by the aggregators, and at the data sources (museums, herbaria and others) so that issues could be detected and corrected early in the process. All the tests are limited to Darwin Core terms. The ~95 tests refined from over 250 in use around the world, were classified into four output types: validations, notifications, amendments and measures. Validations test one of more Darwin Core terms, for ex le, that dwc:decimalLatitude is in a valid range (i.e. between -90 and +90 inclusive). Notifications report a status that a user of the record should know about, for ex le, if there is a user-annotation associated with the record. Amendments are made to one or more Darwin Core terms when the information across the record can be improved, for ex le, if there is no value for dwc:scientificName, it can be filled in from a valid dwc:taxonID. Measures report values that may be useful for assessing the overall quality of a record, for ex le, the number of validation tests passed. Evaluation of the tests was complex and time-consuming, but the important parameters of each test have been consistently documented. Each test has a globally unique identifier, a label, an output type, a resource type, the Darwin Core terms used, a description, a dimension (from the Framework on Data Quality from TG1), an ex le, references, implementations (if any), test-prerequisites and notes. For each test, generic code is being written that should be easy for institutions to implement – be they aggregators or data custodians. A valuable product of the work of TG2 has been a set of general principles. One ex le is “Darwin Core terms are either: literal verbatim (e.g., dwc:verbatimLocality) and cannot be assumed capable of validation, open-ended (e.g., dwc:behavior) and cannot be assumed capable of validation, or bounded by an agreed vocabulary or extents, and therefore capable of validation (e.g., dwc:countryCode)”. Another is “criteria for including tests is that they are informative, relatively simple to implement, mandatory for amendments and have power in that they will not likely result in 0% or 100% of all record hits.” A third: “Do not ascribe precision where it is unknown.” GBIF, the ALA and iDigBio have committed to implementing the tests once they have been finalized. We are confident that many museums and herbaria will also implement the tests over time. We anticipate that demonstration code and a test dataset that will validate the code will be available on project completion.
Publisher: Wiley
Date: 02-2018
DOI: 10.1002/APS3.1024
Publisher: GBIF Secretariat
Date: 2008
Publisher: Wiley
Date: 03-12-2020
DOI: 10.1111/DDI.13208
Abstract: Natural history collections constitute an enormous wealth of information of Life on Earth. It is estimated that over 2 billion specimens are preserved at institutions worldwide, of which less than 10% are accessible via bio ersity data aggregators such as GBIF. Moreover, they are a very important resource for eco‐evolutionary research, which greatly depends on knowing the precise location where the specimens were collected in order to characterize the environment in which they lived. Yet, only about 55% of the accessible records are georeferenced and only 31% have coordinate uncertainty information, which is critical for conducting rigorous studies. The awareness of this gap of knowledge which hinders the enormous potential of such data in research led to the organization of a workshop which brought together key players in georeferencing of natural history collections. The discussion and outcomes of this workshop are here presented.
Publisher: Pensoft Publishers
Date: 10-10-2019
DOI: 10.3897/BISS.3.38084
Publisher: GBIF Secretariat
Date: 2020
Publisher: The University of Kansas
Date: 09-05-2005
DOI: 10.17161/BI.V2I0.5
Publisher: Pensoft Publishers
Date: 20-03-2020
DOI: 10.3897/BISS.4.50889
Abstract: The quality of bio ersity data publicly accessible via aggregators such as GBIF (Global Bio ersity Information Facility), the ALA (Atlas of Living Australia), iDigBio (Integrated Digitized Biocollections), and OBIS (Ocean Biogeographic Information System) is often questioned, especially by the research community. The Data Quality Interest Group, established by Bio ersity Information Standards (TDWG) and GBIF, has been engaged in four main activities: developing a framework for the assessment and management of data quality using a fitness for use approach defining a core set of standardised tests and associated assertions based on Darwin Core terms gathering and classifying user stories to form contextual-themed use cases, such as species distribution modelling, agrobio ersity, and invasive species and developing a standardised format for building and managing controlled vocabularies of values. Using the developed framework, data quality profiles have been built from use cases to represent user needs. Quality assertions can then be used to filter data suitable for a purpose. The assertions can also be used to provide feedback to data providers and custodians to assist in improving data quality at the source. A case study, using two different implementations of tests and assertions based around the Darwin Core "Event Date" terms, were also tested against GBIF data, to demonstrate that the tests are implementation agnostic, can be run on large aggregated datasets, and can make bio ersity data more fit for typical research uses.
Publisher: Public Library of Science (PLoS)
Date: 14-11-2006
Publisher: Public Library of Science (PLoS)
Date: 28-06-2017
Publisher: Pensoft Publishers
Date: 13-06-2018
DOI: 10.3897/BISS.2.26084
Abstract: The quality of bio ersity data is an on-going issue. Early efforts to improve quality go back at least 4 decades, but it has never risen to the level of importance that it should have. For far too long the push to database more and more data regardless of its quality has taken priority. So I pose the question - what is the use of having lots of data if 1) we don’t know what its quality is, and 2) if much of it is not fit for use? When data-basing of herbarium and museum collections began in the 1970s many taxonomists saw the only use of the data as being for taxonomic purposes. But as more and more data has become digitally available, so too the uses to which the data can be put. It has also become increasingly important that the data we have in our herbaria and museums be put to more uses to justify on-going support and funding. But whose responsibility is data quality? To answer that I take you to general data quality principles – i.e. that the difficulty and the cost of improving the quality of the data increases the further you move from its source. Responsibility for data quality rests with everyone. Collectors of the specimens Database designers and builders Data entry operators Data curators and managers Those responsible for exchanging/exporting the data Data aggregators Data publishers Data users Collectors of the specimens Database designers and builders Data entry operators Data curators and managers Those responsible for exchanging/exporting the data Data aggregators Data publishers Data users We all have responsibilities. So, what can we each do to play our part? We need to work together at all levels of the data chain. We need to develop systems whereby feedback on quality from wherever it comes can be documented and fed back. It is no use continually making corrections to the data down the line if those corrections never get back to the data curators and data custodians. It is also of little use if the information fed back goes nowhere and nothing is done with it. The TDWG Data Quality Interest Group is working on setting up standards and tools to help make this possible. We have developed a Framework for Data Quality, we have developed a set of core tests for data quality, and assertions for feeding information back to custodians and forward to users and is beginning a process to deal with vocabularies of value for bio ersity data.
Publisher: Pensoft Publishers
Date: 20-03-2020
Publisher: Pensoft Publishers
Date: 16-08-2017
Publisher: Wiley
Date: 14-06-2022
DOI: 10.1111/ECOG.06025
Publisher: Pensoft Publishers
Date: 29-09-2020
DOI: 10.3897/BISS.4.58983
Abstract: To understand biological and geological events and the history of collected s les, it is essential to determine and communicate location information accurately. The accuracy of a georeference depends upon the circumstances of the event. Historical collections depend on having clear verbatim locality descriptions, the correct interpretation of data written on labels, and on the availability of gazetteers and maps of suitable scale and time. Observation and tracking data localities depend on GPS (Global Positiioning System) accuracy, and on presence or absence of nearby obstructions such as buildings, forest cover, cliffs, etc. Marine data depend on the accurate determination of the surface location and the techniques to position a e event from that location and to determine its depth and extent. Many people are using smartphones or maps such as Google Earth and Google Maps to determine their georeferences – but are they suitable and accurate enough to determine locations and elevations? New editions of the Georeferencing Best Practices (Chapman and Wieczorek 2020), the Georeferencing Quick Reference Guide (Zermoglio et al. 2020), and the Georeferencing Calculator Manual (Bloom et al. 2020), were published earlier this year and address all the issues listed above and many more. These documents were based on earlier versions but have been updated and improved considerably – adding information for marine biomes, caves, lithographic stratifications, transects, and the use of elevation, as well as including many more illustrations and ex les. The expansion of an extensive georeferencing glossary adds to consistency in the use of terms The trio of documents now provides consistent guidance about how to georeference erse locality types and detailed instructions on how to calculate uncertainty using many different coordinate reference systems and datums (horizontal and vertical) along with the importance of recording this information. Finally, they provide guidance on how to set up a georeferencing project and how to relate the results to the Darwin Core Standard (Darwin Core Task Group 2009). For the last decade, Darwin Core (Wieczorek et al. 2012) has been one of the preferred standards for sharing bio ersity data, including associated location information. Darwin Core currently has 44 terms in its Location class, allowing sharing from administrative isions, to elevations and depths, coordinates in different formats, and georeference metadata, among others. Although Darwin Core provides definitions for each of its terms, their correct use is sometimes poorly understood, resulting in information being captured incorrectly, or not captured, documented or shared at all. We will re-introduce these documents, discuss their content, importance, and differences from previously published versions. The newly revised documents provide guidance on capturing and documenting georeferences, clarifying the georeferencing process and showing how to capture information using Darwin Core appropriately. They will improve the location data associated with biological events and our understanding of these events.
Publisher: Pensoft Publishers
Date: 10-2020
DOI: 10.3897/BISS.4.58982
Abstract: Motivation Other than data availability, ‘Data Quality’ is probably the most significant issue for users of bio ersity data and this is especially so for the research community. Data Quality Tests and Assertions Task Group (TG-2) from the Bio ersity Information Standards (TDWG) Bio ersity Quality Interest Group is reviewing practical aspects relating to ‘data quality’ with a goal of providing a current best practice at the key interface between data users and data providers: tests and assertions. If an internationally agreed standard suite of core tests and resulting assertions can be used by all data providers and aggregators and hopefully data collectors, then greater and more appropriate use could be made of bio ersity data. Adopting this suite of core tests, data providers and particularly aggregators such as the Global Bio ersity Information Facility (GBIF) and its nodes would have increased credibility with the user communities and could provide more effective information for evaluating ‘fitness for use’. Goals, Outputs and Outcomes A standard core (fundamental) set of tests and associated assertions based around Darwin Core terms A standard suite of descriptive fields for each test Broad deployment of the tests, from collector to aggregator A set of basic principles for the creation of tests/assertions Software that provides an ex le implementation of each test Data that can be used to validate an implementation of the tests A publication that captures the knowledge built during the creation of the tests/assertions A standard core (fundamental) set of tests and associated assertions based around Darwin Core terms A standard suite of descriptive fields for each test Broad deployment of the tests, from collector to aggregator A set of basic principles for the creation of tests/assertions Software that provides an ex le implementation of each test Data that can be used to validate an implementation of the tests A publication that captures the knowledge built during the creation of the tests/assertions Strategy The tests and rules generating assertions at the record-level are more fundamental than the tools or workflows that will be based on them. The priority is to create a fully documented suite of core tests that define a framework for ready extension across terms and domains. Status 2019-2020 The core tests have proven to be far more complex than any of the team had anticipated. Several times over the past three years, we believed we had finalized the tests, only to find new issues that have required a fresh understanding and subsequent edits, e.g., the most recent dropping of the two tests related to dwc:identificationQualifier: TG2-VALIDATION_IDENTIFICATIONQUALIFIER_DETECTED and TG2-AMENDMENT_IDENTIFICATIONQUALIFIER_FROM_TAXON TG2-VALIDATION_IDENTIFICATIONQUALIFIER_DETECTED and TG2-AMENDMENT_IDENTIFICATIONQUALIFIER_FROM_TAXON This decision resulted from a review of dwc:identificationQualifier values in GBIF records and an evaluation of expected values based on the Darwin Core definition of the term. Aside from there being many values, the term expects the qualifier in relation to a given taxonomic name, and rules of open nomenclature are unevenly adopted across data records to reliably parse and detect dwc:identificationQualifier for these tests to be effective. A similar situation occurs for dwc:scientificName, where we have resorted to the term “polynomial” to refer to the non-authorship part of dwc:scientificName. What has occurred during the past year? Months of work on discussions and edits to the GitHub issues (= mainly the tests), using mainly via Zoom and email. We had hoped to have a face-to-face meeting in Bariloche, Argentina early in 2020 but the Corona virus stopped that. This was unfortunate as we needed this meeting to discuss the remaining complex issues as noted above. Attempting to address such issues by Zoom has been far less efficient. We are occasionally re-visiting decisions made years earlier. An indication that we have been doing this work for (too) many years. We have now standardized all the test parameters for the 99 CORE tests. Much work has gone into standardizing the phrasing and terminology within the 'Expected response' field of the tests – the parameter that most clearly defines each test. Two of the test fields that have taken most of our time to resolve have been ‘Parameters’ and what we now call ‘bdq:sourceAuthority’ (Chapman et al. 2020a). These are now complete. The work on ‘Parameters’ has fed in to Task Group 4 on Vocabularies of Values (see Vocabularies needed for Darwin Core terms prepared by TG4). We have published the work from the Data Quality Interest and Task Groups: Chapman et al. 2020b We have extended the vocabulary that has been used for the Tests and Assertions. Development of the datasets that validate the implementation of the tests continues. We recognize the dependence on the work of the Annotations Interest Group for the results from the tests to have maximal impact. It is important that test results stay with the records. Months of work on discussions and edits to the GitHub issues (= mainly the tests), using mainly via Zoom and email. We had hoped to have a face-to-face meeting in Bariloche, Argentina early in 2020 but the Corona virus stopped that. This was unfortunate as we needed this meeting to discuss the remaining complex issues as noted above. Attempting to address such issues by Zoom has been far less efficient. We are occasionally re-visiting decisions made years earlier. An indication that we have been doing this work for (too) many years. We have now standardized all the test parameters for the 99 CORE tests. Much work has gone into standardizing the phrasing and terminology within the 'Expected response' field of the tests – the parameter that most clearly defines each test. Two of the test fields that have taken most of our time to resolve have been ‘Parameters’ and what we now call ‘bdq:sourceAuthority’ (Chapman et al. 2020a). These are now complete. The work on ‘Parameters’ has fed in to Task Group 4 on Vocabularies of Values (see Vocabularies needed for Darwin Core terms prepared by TG4). We have published the work from the Data Quality Interest and Task Groups: Chapman et al. 2020b We have extended the vocabulary that has been used for the Tests and Assertions. Development of the datasets that validate the implementation of the tests continues. We recognize the dependence on the work of the Annotations Interest Group for the results from the tests to have maximal impact. It is important that test results stay with the records. We will provide details of the challenges, the breakdown of the tests and the advances of the project.
Publisher: FapUNIFESP (SciELO)
Date: 2022
DOI: 10.1590/1676-0611-BN-2022-1405
Abstract: Abstract ECO92, a United Nations Conference on Environment and Development, hosted by Brazil in 1992 was a catalyst for much activity on bio ersity in the State of São Paulo and eventually led to the establishment of BIOTA-FAPESP. BIOTA-FAPESP quickly evolved into a world-leading research program that has broadened to cover all aspects of bio ersity in the State from genes through species and to ecosystems and the interactions between them. Through the funding of multi-disciplinary projects, the development of collaborative links within the State, nationally and internationally, and the astute use of databases to link the program’s project outputs it has set a platform for filling the bio ersity knowledge gaps. Having achieved much in the last two decades, it still has some way to go, but the stage is set.
Location: Australia
No related grants have been discovered for Arthur Chapman.