ORCID Profile
0000-0001-8900-6203
Current Organisation
Blatant Fabrications Pty Ltd
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Publisher: Springer Science and Business Media LLC
Date: 12-1995
DOI: 10.1007/BF00058206
Publisher: Wiley
Date: 09-2012
DOI: 10.1111/EMR.12000
Publisher: Springer Science and Business Media LLC
Date: 11-1981
DOI: 10.1007/BF00118398
Publisher: Springer Netherlands
Date: 1981
Publisher: Springer Netherlands
Date: 1981
Publisher: SAGE Publications
Date: 03-1985
DOI: 10.1068/A170397
Abstract: A numerical method for classifying geographic data is presented which incorporates geographic location as an external constraint. The method was implemented by making minimal changes to an existing agglomerative hierarchical algorithm. This was seen as the simplest solution, both computationally and operationally. Given a matrix of similarities or distances calculated from the usual intrinsic variables, the classification proceeds normally with the constraint that only adjacent objects are allowed to form groups. The method has been implemented previously, but here the examination of it is extended to cover the effects of a range of different fusion strategies, and to consider changes in within-group heterogeneity as a result of imposing an adjacency constraint. Three other matters arising are discussed: the presence of regional as opposed to global outliers of a classification the occurrence of reversals in similarity values and a measure of the stress imposed on a classification with an adjacency constraint. The method is seen as suggesting a possible general solution to the problem of constraints in numerical classification. Some ex les of other constraints and the appropriate fusion strategies are given.
Publisher: Wiley
Date: 03-1982
Publisher: Informa UK Limited
Date: 1993
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: Pensoft Publishers
Date: 31-05-2013
Publisher: Springer Science and Business Media LLC
Date: 04-1987
DOI: 10.1007/BF00038687
Publisher: Wiley
Date: 06-1993
DOI: 10.2307/3235592
Publisher: Informa UK Limited
Date: 07-1992
DOI: 10.1207/S15327906MBR2703_6
Abstract: We introduce a UPGMA based counterpart to the Lance and Williams (1966) p-flexible clustering strategy. This new method is compared with the original p-flexible technique based on WPGMA for a range of fl values from -0.8 to +0.1. Recovery of the known cluster structure from simulated data was evaluated using the Hubert and Arabie (1985) version of the Rand statistic. The algorithm used to generate the simulated clusters in the study included error-free data as well as three other error conditions. The simulated data also varied the number of clusters, the underlying dimensionality, and the density distribution of points to the clusters. Results showed that the flexible UPGMA gives the best recovery for all generated data configurations. Further, the flexible UPGMA method with a small negative β value performed better than the standard UPGMA method where β is set equal to zero. The flexible UPGMA strategy, employed with a narrow β range, is recommended based upon its ability to recover cluster structure over all error conditions.
Publisher: Elsevier BV
Date: 1989
Publisher: Elsevier BV
Date: 03-1980
Publisher: Elsevier BV
Date: 03-1980
Publisher: Informa UK Limited
Date: 03-1978
Publisher: Elsevier BV
Date: 2019
Publisher: Elsevier BV
Date: 1976
Publisher: Springer Science and Business Media LLC
Date: 11-1981
DOI: 10.1007/BF00118381
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: Zenodo
Date: 2020
Publisher: IOP Publishing
Date: 02-2019
Publisher: Informa UK Limited
Date: 26-08-2015
Publisher: Pensoft Publishers
Date: 26-06-2019
DOI: 10.3897/BISS.3.37441
Abstract: Effective management of our natural world under current and future conditions requires efficient, collaborative and complementary planning and decision-making processes with clear lines of accountability. While there has been significant progress in establishing national databases for the management of species observation data, these only represent s les of a species' total distribution. The need and challenge therefore is to model these point-based observation data to obtain estimates or projections of the total range and distribution of the species. Such Species Distribution Models (SDMs), also known as Environmental Niche Models (ENMs), and the geographic data (or “maps”) they generate, provide vital information needed by governments at all levels to meet various policy and statutory responsibilities and obligations. SDMs quantify the response of species occurrence to environmental conditions described by variables such as climate, substrate, productivity and vegetation. The outcomes of an SDM can be used to identify locations and regions with potentially suitable environmental conditions for a species, as well as assess how species may respond to projected future climate changes or habitat loss. While SDMs are widely used in many decision- and policy-making programs, investment in species distribution information has been fragmented and limited. In Australia, three different government departments joined forces with the Atlas of Living Australia and the Bio ersity and Climate Change Virtual Laboratory to develop a standard framework for modelling threatened species distributions for use in policy and environmental decision-making. The pilot program that will be conducted throughout 2019 includes three complementary pillars: An expert panel with both researchers and government practitioners who will review current SDM practices used in government and develop a set of best-practice methods. A technology program that includes the development of a new modelling platform that implements the best-practice methods for transparent and reproducible SDMs for decision making as established by the expert panel. Additionally, there will be an online portal for publishing ecological model outputs in a searchable catalogue to enhance cross-jurisdiction collaborations. Establishment of a training and skill development program to upskill decision makers using the new tools and methodology in practice. An expert panel with both researchers and government practitioners who will review current SDM practices used in government and develop a set of best-practice methods. A technology program that includes the development of a new modelling platform that implements the best-practice methods for transparent and reproducible SDMs for decision making as established by the expert panel. Additionally, there will be an online portal for publishing ecological model outputs in a searchable catalogue to enhance cross-jurisdiction collaborations. Establishment of a training and skill development program to upskill decision makers using the new tools and methodology in practice. This presentation will showcase the outcomes of this program and highlight how digital infrastructure can enhance decision making. In this case specifically, the collaboration across government departments ensures a) a consistent approach across jurisdictions, b) an increase in model quality, thereby leading to a decrease in unnecessary survey or consultation efforts, c) an increase in suitability, robustness and reproducibility of SDMs, and d) increased advocacy and coordination in national programs and resources.
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11677437_2
Publisher: Pensoft Publishers
Date: 10-07-2019
DOI: 10.3897/BISS.3.35626
Abstract: ‘Data Quality Test and Assertions’ Task Group 2 (ommunity/bdq/tg-2/) has taken another year to clarify the 102 tests (dwg/bdq/issues?q=is%3Aissue+is%3Aopen+label%3ATest). The original mandate to develop a core suite of tests that could be widely applied from data collection to user evaluation of aggregated data seemed straight-forward. Two years down the track, we have proven that to be incorrect. Among the final tests are complexities that none of the core group anticipated. For ex le, the need for a definition of ‘empty’ or the ‘Expected response’ from the test under various scenarios. The record-based tests apply to Darwin Core terms (erms/) and have been classified as of type validation (66), amendment (29), notification (3) or measure (5). Validations test one or more Darwin Core terms against known characteristics, for ex le, VALIDATION_MONTH_NOTSTANDARD. Amendments may be applied to Darwin Core terms where we can unambiguously offer an improvement to the record, for ex le, AMENDMENT_MONTH_STANDARDIZED. Notifications are made where we believe a flag will help alert users to an issue that needs evaluation, for ex le, NOTIFICATION_DATAGENERALIZATIONS_NOTEMPTY. Measures are summaries of test outcomes at the record level, for ex le, MEASURE_AMENDMENTS_PROPOSED. We note that 41 require some parameters to be established at the time of test implementation, 20 tests require access to a currently accepted vocabulary and 3 tests rely on ISO/DCMI standards. The dependency on vocabularies to circumscribe permissible values for Darwin Core terms led to the establishment by Paula Zermoglio of DQ Task Group 4 (dwg/bdq/tree/master/Vocabularies). A vocabulary of 154 terms that are associated with the tests and assertions have been developed. As at the time of writing this abstract, test data and demonstration code implementation of each test are yet to be completed. We hope these will be finalized by the time of this presentation.
Publisher: Informa UK Limited
Date: 13-09-2019
Publisher: Elsevier BV
Date: 11-2006
Publisher: Wiley
Date: 14-01-2009
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: CSIRO Publishing
Date: 1987
DOI: 10.1071/SR9870337
Abstract: Numerical classification techniques were used to examine the chemical composition of 169 shallow groundwater s les in relation to their geographical distribution within the Murrumbidgee Irrigation Area. The ionic proportions are very similar throughout the whole area, but the total soluble salt concentrations are very variable. The classification was more coherent when primary variables were used rather than derived variables such as Saturation Indices.
Publisher: Wiley
Date: 09-1992
Publisher: Pensoft Publishers
Date: 07-11-2018
DOI: 10.3897/BDJ.6.E28073
Abstract: This paper discusses the process of retrieval and updating legacy data to allow on-line discovery and delivery. There are many pitfalls of institutional and non-institutional ecological data conservation over the long term. Interruptions to custodianship, old media, lost knowledge and the continuous evolution of species names makes resurrection of old data challenging. We caution against technological arrogance and emphasise the importance of international standards. We use a case study of a compiled set of continent-wide vegetation survey data for which, although the analyses had been published, the raw data had not. In the original study, publications containing plot data collected from the 1880s onwards had been collected, interpreted, digitised and integrated for the classification of vegetation and analysis of its conservation status across Australia. These compiled data are an extremely valuable national collection that demanded publishing in open, readily accessible online repositories, such as the Terrestrial Ecosystem Research Network (www.tern.org.au) and the Atlas of Living Australia (ALA: www.ala.org.au), the Australian node of the Global Bio ersity Information Facility (GBIF: www.gbif.org). It is hoped that the lessons learnt from this project may trigger a sober review of the value of endangered data, the cost of retrieval and the importance of suitable and timely archiving through the vicissitudes of technological change, so the initial unique collection investment enables multiple re-use in perpetuity.
Publisher: Elsevier BV
Date: 1993
Publisher: Inter-Research Science Center
Date: 2002
DOI: 10.3354/AME027159
Publisher: Elsevier BV
Date: 1984
Publisher: Zenodo
Date: 2020
Publisher: Pensoft Publishers
Date: 04-07-2019
DOI: 10.3897/BISS.3.37791
Abstract: Essential Bio ersity Variables (EBVs) are the latest push toward supporting state of the environment indicators (Pereira et al. 2013). The European Union Funded Creative-B Project (see cordis.europa.eu roject/rcn/100345/brief/en) outlined the status and strategy for interoperability between what they termed Bio ersity Research Infrastructures (BRIs: such as the Global Bio ersity Information Infrastructure (GBIF), the Atlas of Living Australia (ALA) and the Integrated Digitized Biocollections (iDigBio)). Toward the end of that project, the group decided that a logical follow-on project should position BRIs to support the production of Essential Bio ersity Variables (EBVs). This idea became the GLOBal Infrastructures for Supporting Bio ersity research (GLOBIS-B) project (www.globis-b.eu) and this presentation provides a summary of a case study on generating EBVs (Hardisty et al. 2019). As a part of GLOBIS-B, I suggested that a small team of GLOBIS members should document in detail, each step in the production of an EBV from GBIF and the ALA data for a few invasive species. We wanted address the rarity of detailed recording and justification for each step in the production of a dataset for environmental evaluation. I anticipated that the team would encounter many practical issues, but this case study raised far more significant issues that any of us had anticipated. The EBV chosen for this study was Area of Occupancy (IUCN Standards and Petitions Subcommittee 2017) and the species selected represented various invasion scenarios: Acacia longifolia Vespula germanica and Bubulcus ibis . The workflow included 20 steps between locating data and publishing an EBV, and these steps were radically different between GBIF and the ALA. The workflow required manual steps such as resolving invasive status of Acacia longifolia subspecies only one of which was ‘invasive’. Datasets of occurrence records had to be exported from the ALA and GBIF to enable filtering for purpose, for ex le, not all Darwin Core terms are exposed in the current public interface of the ALA. After the record filtering, the ALA and GBIF datasets then required merging and deduplication, for which one-off code had to be written. A few of the 15 significant messages from this study included: a lack of consistency of data between BRIs (e.g., GBIF records should be a superset of ALA records) consistency and adequacy of filtering tools between BRIs exported data structures massively differed between BRIs that automation of the workflows may be possible but many manual intervention steps were required. By my figuring, the case study took approximately 10 times longer than anticipated, but the messages to BRIs was clear – consistency and adequacy of data and tools require urgent work.
Publisher: Springer Science and Business Media LLC
Date: 09-1986
DOI: 10.1007/BF01894190
Publisher: Wiley
Date: 10-2009
Publisher: Cambridge University Press (CUP)
Date: 06-2000
DOI: 10.1017/S095410200000016X
Abstract: A key element in Antarctic conservation over the past forty years has been area protection. In pursuit of this and the protection of continuing scientific investigations 59 protected areas have so far been agreed by the Antarctic Treaty Consultative Meetings. Although both flora and fauna have been protected under this regime there has only been the habitat matrix devised by SCAR as a guide to selection of areas. What could we do to improve the process of identification and selection?
Publisher: Elsevier BV
Date: 09-1996
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: Pensoft Publishers
Date: 20-07-2016
DOI: 10.3897/RIO.2.E9933
Publisher: Pensoft Publishers
Date: 21-05-2018
DOI: 10.3897/BISS.2.25664
Abstract: To usefully describe sensor deployments on animals is a major challenge for advocates of data standards. Bio-logging studies also need to be documented in a standard manner to facilitate discovery and determine relevance? For systems aggregating bio ersity occurrence records, the use of the Darwin Core standard (Wieczorek et al. 2012) to express species occurrences is near ubiquitous. Bio-logging studies are universally multiple instances of species occurrences that output high quality spatial and temporal data recorded by specialists. There are a lot of benefits to summarising these studies by means of a single, flat file record. Simple Darwin Core offers the ability to do this by representing the multiple occurrences as a date range in dwc:eventDate and a footprint polygon using dwc:footprintWKT for the area covered by the track. By also uniformly describing the species, the dwc:basisOfRecord as Machine Observation, and a controlled vocabulary to describe the type of bio-logging data, systems could offer an effective means of querying tracking data. It’s important to look to other data standards initiatives relevant to bio-logging to ensure common usage of Darwin Core terms. The Atlas of Living Australia is using an implementation of Simple Darwin Core to represent data from the bio-logging platform ZoaTrack as occurrence data to make it discoverable via location or species-based searches. Other initiatives, for ex le Swedish LifeWatch follow a similar approach to represent data from the Wireless Remote Animal Monitoring (WRAM) Scandinavian bio-logging infrastructure. With endorsement from the community, the implementation could be useful as a type of metadata catalogue record, opening it for usage in application programmer interface (API) development and thus enabling machine interoperability between systems and users. In short, bio-logging systems and practitioners would be able to easily discover relevant studies by searching by location and/or species.
Publisher: Pensoft Publishers
Date: 21-04-2021
DOI: 10.3897/BDJ.9.E65023
Abstract: The Atlas of Living Australia (ALA) is Australia’s national bio ersity database, delivering data and related services to more than 80,000 Australian and international users annually. Established under the Australian Government’s National Collaborative Research Infrastructure Strategy to provide trusted bio ersity data to support the research sector, its utility now extends to government, higher education, non-government organisations and community groups. These partners provide data to the ALA and leverage its data and related services. The ALA has also played an important leadership role internationally in the bio ersity informatics and infrastructure space, both through its partnership with the Global Bio ersity Information Facility and through support for the international Living Atlases programmes which has now delivered 24 instances of ALA software to deliver sovereign bio ersity data capability around the world. This paper begins with a historical overview of the genesis of the ALA from the collections, museums and herbaria community in Australia. It details the bio ersity and related data and services delivered to users with a primary focus on species occurrence records which represent the ALA's primary data type. Finally, the paper explores the ALA's future directions by referencing results from a recently completed national consultation process.
Publisher: Springer Netherlands
Date: 1987
Publisher: Wiley
Date: 08-1991
DOI: 10.2307/3236031
Publisher: Pensoft Publishers
Date: 05-07-2018
DOI: 10.3897/BISS.2.28131
Abstract: Taxonomic work is slow and time consuming. Alarm bells have rung for years about the need to go faster, the need to attract and train new taxonomic workers, and the need to convince other branches of science that taxonomic work is vital. Morphological taxonomy is either being overrun or augmented – depending on your perspective – by genomics, artificial intelligence, new imaging methods and species-related data from other branches of science. Ecology is one such branch of science, where defining, documenting and managing information about species traits has emerged as one of the most significant problems in the discipline. Traits have been recorded for aeons, but the resulting data has largely been insulated within cliques. How do we integrate these data and make them available in a form that will help to address significant issues about our environment? The ‘speed bumps’ on the route to a useful solution may be more social than technical. Cross-disciplinary collaboration is required to address the big questions in bio ersity research today, and it will need to extend beyond taxonomy and ecology to other disciplines, such as pharmacology and material science. As Harry Truman said, and John LaSalle often quoted, “It is amazing what you can accomplish if you do not care who gets the credit”. We are challenged to understand and answer the key questions about the world on which we all depend. What are the challenges and the opportunities to accelerate bio ersity discovery and documentation?
No related grants have been discovered for Lee Belbin.