Academic literature on the topic 'Darwin Core mapping'

Since its ratification as a TDWG standard in 2009, data publishers have had to struggle with the essential step of mapping fields in working databases to the terms in Darwin Core Wieczorek et al. 2012 in order to publish and share data using that standard. Doing so requires a good understanding of both the data set and Darwin Core. The accumulated knowledge about these mappings constitutes what we call the "Darwin Cloud." We will explore the nature of data mapping challenges and the potential for semi-automated solutions to them. Specifically, we will look at the "Darwinizer" actor and its usage in related workflows within the Kurator data quality framework and the implications for community-managed vocabularies.

Fundamental to the capacity of Australia's 15,000 biosciences researchers to answer questions in taxonomy, phylogeny, evolution, conservation, and applied fields like crop improvement and biosecurity, is access to trusted genomics (and genetics) datasets. Historically, researchers turned to single points of origin, like GenBank (part of the United States' National Center for Biotechnology Information), to find the reference or comparative data they needed, but the rapidity of data generation using next-gen methods, and the enormous size and diversity of datasets derived from next-gen sequencing methods, mean that single databases no longer contain all data of a specific class, which may be attributable to individual taxa, nor the full breadth of data types relevant for that taxon. Comprehensively searching for taxonomically relevant data, and indeed, data of types germane to the research question, is a significant challenge for researchers. Data are openly available online, but the data may be stored under synonyms or indexed via unconventional taxonomies. Data repositories are largely disconnected and researchers must visit multiple sites to have confidence that their searches have been exhaustive. Databases may focus on single data types and not store or reference other data assets, though they may be relevant for the taxon of interest. Additionally, our survey of the genomics community indicated that researchers are less likely to trust data with inadequately evidenced provenance metadata. This means that genomics data are hard to find and are often untrusted. Moreover, even once found, the data are in formats that do not interoperate with occurrence and ecological datasets, such as those housed in the Atlas of Living Australia. We built the Australian Reference Genome Atlas (ARGA) to overcome the barriers faced by researchers in finding and collating genomics data for Australia's species, and we have built it so that researchers can search for data within taxonomically accepted contexts and defined intersections and conjunctions with verified and expert ecological datasets. Using a series of ingestion scripts, the ARGA data team has implemented new and customised data mappings that effectively integrate genomics data, ecological traits, and occurrence data within an extended Darwin Core Event framework (GBIF 2018). Here, we will demonstrate how the architecture we derived for ARGA application works, and how it can be extended as new data sources emerge. We then demonstrate how our flexible model can be used to:locate genomics data for taxa of interest;explore data within an ecological context; andcalculate metrics for data availability for provincial bioregions.

Biodiversity is increasingly being assessed using omic technologies (e.g. metagenomics or metatranscriptomics); however, the metadata generated by omic investigations is not fully harmonised with that of the broader biodiversity community. There are two major communities developing metadata standards specifications relevant to omic biodiversity data: TDWG, through its Darwin Core (DwC) standard, and the Genomic Standard Consortium (GSC), through its Minimum Information about any (x) Sequence (MIxS) checklists. To prevent these specifications leading to silos between the communities using them (e.g. INSDC: an internationally mandated database collaboration for nucleotide sequencing data [from health, biodiversity, microbiology, etc.] using the MIxS checklists; OBIS and GBIF: global biodiversity data networks using the DwC standard), there is a need to harmonise them at the level of the standards organisations themselves.To this end, we have brought together representatives from these standardisation bodies, along with representatives from established biodiversity data infrastructures, domain experts, data generators, and publishers to develop sustainable interoperability between the two specifications. Together, we have:generated a semantic mapping between the terminology used in each specification, and syntactic mapping of their associated values following the Simple Standard for Sharing Ontology Mappings (SSSOM), andcreated an example MIxS-DwC extension showing the incorporation of unmapped MIxS terms into a DwC-Archive.To sustain these mechanisms of interoperability, we have proposed a Memorandum of Understanding between the GSC and TDWG. During our work, we also noted a number of key challenges that currently preclude interoperation between these two specifications. In this talk, we will outline the major steps we took to get here, as well as the future activities we recommend based on our outputs.

The Taxon Concept Schema (TCS, Biodiversity Information Standards (TDWG) 2005) is the TDWG standard for sharing taxonomic data. TCS has never enjoyed widespread use and most taxonomic data is exchanged using the Darwin Core (Wieczorek et al. 2012) Taxon class or non-standard terms. For the last three years, the TCS 2 Task Group has been working on a major new version, which will take TCS out of its XML Schema and convert it to a vocabulary standard of terms and definitions that does not dictate a data format and can be maintained under the TDWG Vocabulary Maintenance Standard (Vocabulary Maintenance Specification Task Group 2017). This new version (Taxon Concept Schema 2 Task Group 2024) is now ready to go out to public review.With only 50 terms in total, 12 of which are borrowed from Darwin Core and Dublin Core (Dublin Core Metadata Initiative 2012), TCS is a small standard. There are, however, a lot of relationships, both internal and external, which means that all taxonomic data can be exchanged using these relatively few terms. The TCS TaxonConcept is equivalent to the Darwin Core Taxon and in some situations can be used in conjunction with the Taxon class, while in other situations it replaces it. The biggest difference between the TCS TaxonConcept and the Darwin Core Taxon is that in TCS, the Taxon Concept and Taxon Name are separated, while in Darwin Core the taxon name is embedded in the Taxon. This means that, when using TCS to exchange taxonomic data, one does not have to create—and assign identifiers to—the data artefacts that one would have to create when using Darwin Core to exchange the same data.Another important difference is that a TCS TaxonConcept must have a source (accordingTo). This is important because the same name can apply to different taxonomic groups. The missing element when using only names is the definition or circumscription of the taxonomic group. Circumscription is drawing a boundary around a taxonomic group and is by many taxonomists seen as the holy grail of taxonomic data. TCS 1 had the CharacterCircumscription and SpecimenCircumscription elements, but these have not yet been included in TCS 2, because we do not know how to implement them in a useful way and, if we are going to have circumscription in TCS, we want it to be operational.While we are as yet unable to express circumscription in a meaningful way in TCS, we can still do everything we need to do with the mapping properties and the TaxonConceptMapping class that are included in TCS. Because scientific names have types and these types are specimens, some of these mappings can be derived from the synonymy, using the taxon concepts as sets and the taxon names as elements in these sets. This, along with nomenclatural business rules that decide which, of all the names that can be applied to a taxon concept, is the accepted name, is also the reason that name resolution and name matching mostly work. However, the mappings or name resolution thus obtained are only based on the information in the data set and there is no taxonomic data set that includes all the necessary mapping information in its synonymy.The biggest challenge in dealing with taxonomic data is that we are often dealing with incomplete data. Using the right objects, i.e., taxon concepts rather than taxon names, and allowing additional expert knowledge in the form of taxon concept mappings in our data sets and name resolution, will go some way to resolve this problem.

The Australian Biodiversity Information Standard (ABIS) is a data standard that has been developed to represent and exchange biodiversity data expressed using the Resource Description Framework (RDF). ABIS has the TERN ontology at its core, which is a conceptual information model that represents plot-based ecological surveys. The RDF-linked data structure is self-describing, composed of "triples". This format is quite different from tabular data. During the Australian federal government Biodiversity Data Repository pilot project, occurrence data in tabular Darwin Core format was converted into ABIS linked data. This lightning talk will describe the approach taken, the challenges that arose, and the ways in which data using Darwin Core terms can be represented in a different way using linked data technologies.

The standardization of data, encompassing both primary and contextual information (metadata), plays a pivotal role in facilitating data (re-)use, integration, and knowledge generation. However, the biodiversity and omics communities, converging on omics biodiversity data, have historically developed and adopted their own distinct standards, hindering effective (meta)data integration and collaboration.In response to this challenge, the Task Group (TG) for Sustainable DwC-MIxS Interoperability was established. Convening experts from the Biodiversity Information Standards (TDWG) and the Genomic Standards Consortium (GSC) alongside external stakeholders, the TG aimed to promote sustainable interoperability between the Minimum Information about any (x) Sequence (MIxS) and Darwin Core (DwC) specifications.To achieve this goal, the TG utilized the Simple Standard for Sharing Ontology Mappings (SSSOM) to create a comprehensive mapping of DwC keys to MIxS keys. This mapping, combined with the development of the MIxS-DwC extension, enables the incorporation of MIxS core terms into DwC-compliant metadata records, facilitating seamless data exchange between MIxS and DwC user communities.Through the implementation of this translation layer, data produced in either MIxS- or DwC-compliant formats can now be efficiently brokered, breaking down silos and fostering closer collaboration between the biodiversity and omics communities. To ensure its sustainability and lasting impact, TDWG and GSC have both signed a Memorandum of Understanding (MoU) on creating a continuous model to synchronize their standards. These achievements mark a significant step forward in enhancing data sharing and utilization across domains, thereby unlocking new opportunities for scientific discovery and advancement.

The Research Institute for Nature and Forest (INBO) is co-managing three biologging networks as part of a terrestrial and freshwater observatory for LifeWatch Belgium. The networks are a GPS tracking network for large birds, an acoustic receiver network for fish, and a camera trap network for mammals. As part of our mission at the Open science lab for biodiversity, we are publishing the machine observations these networks generate as standardized, open data. One of the challenges however, is finding the appropriate standards and platforms to do so. In this talk, we will present the three networks, the type of biologging data they collect and how we (plan to) standardize these to specific community standards and to Darwin Core (Wieczorek et al. 2012). Data from the bird tracking network have been published in 2014 as one of the first biologging datasets on the Global Biodiversity Information Facility (GBIF) (Stienen et al. 2014). We are now planning to upload the data to Movebank instead and contribute to a generic mapping between the Movebank format and Darwin Core. Data from the acoustic receiver network are being mapped using the Darwin Core guidelines proposed by the Machine Observations Interest Group of Biodiversity Information Standards (TDWG). Images generated by the camera trap network are managed in the annotation system Agouti, for which we plan to export the data in the Camera Trap Metadata Language (Forrester et al. 2016). We also aim to write a software package to deposit camera trap images and data on Zenodo and map the observation data to Darwin Core. We hope that our work will contribute to discussions and guidelines on how to best map biologging data to Darwin Core, which is one of the aims of the Machine Observations Interest Group of Biodiversity Information Standards (TDWG).

For those biologists and biodiversity data managers who are unfamiliar with information science data practices of data standardization, the use of complex software to assist in the creation of standardized datasets can be a barrier to sharing data.Since the ratification of the Darwin Core Standard (DwC) (Darwin Core Task Group 2009) by the Biodiversity Information Standards (TDWG) in 2009, many datasets have been published and shared through a variety of data portals. In the early stages of biodiversity data sharing, the protocol Distributed Generic Information Retrieval (DiGIR), progenitor of DwC, and later the protocols BioCASe and TDWG Access Protocol for Information Retrieval (TAPIR) (De Giovanni et al. 2010) were introduced for discovery, search and retrieval of distributed data, simplifying data exchange between information systems. Although these protocols are still in use, they are known to be inefficient for transferring large amounts of data (GBIF 2017). Because of that, in 2011 the Global Biodiversity Information Facility (GBIF) introduced the Darwin Core Archive (DwC-A), which allows more efficient data transfer, and has become the preferred format for publishing data in the GBIF network. DwC-A is a structured collection of text files, which makes use of the DwC terms to produce a single, self-contained dataset. Many tools for assisting data sharing using DwC-A have been introduced, such as the Integrated Publishing Toolkit (IPT) (Robertson et al. 2014), the Darwin Core Archive Assistant (GBIF 2010) and the Darwin Core Archive Validator. Despite promoting and facilitating data sharing, many users have difficulties using such tools, mainly because of the lack of training in information science in the biodiversity curriculum (Convention on Biological Diversiity 2012, Enke et al. 2012). However, most users are very familiar with spreadsheets to store and organize their data, but the adoption of the available solutions requires data transformation and training in information science and more specifically, biodiversity informatics. For an example of how spreadsheets can simplify data sharing see Stoev et al. (2016).In order to provide a more "familiar" approach to data sharing using DwC-A, we introduce a new tool as a Google Sheet Add-on. The Add-on, called <em>Darwin Core Archive Assistant Add-on</em> can be installed in the user's Google Account from the G Suite MarketPlace and used in conjunction with the Google Sheets application.The Add-on assists the mapping of spreadsheet columns/fields to DwC terms (Fig. 1), similar to IPT, but with the advantage that it does not require the user to export the spreadsheet and import it into another software. Additionally, the Add-on facilitates the creation of a star schema in accordance with DwC-A, by the definition of a "CORE_ID" (e.g. occurrenceID, eventID, taxonID) field between sheets of a document (Fig. 2). The Add-on also provides an Ecological Metadata Language (EML) (Jones et al. 2019) editor (Fig. 3) with minimal fields to be filled in (i.e., mandatory fields required by IPT), and helps users to generate and share DwC-Archives stored in the user's Google Drive, which can be downloaded as a DwC-A or automatically uploaded to another public storage resource like a user's Zenodo Account (Fig. 4).We expect that the Google Sheet Add-on introduced here, in conjunction with IPT, will promote biodiversity data sharing in a standardized format, as it requires minimal training and simplifies the process of data sharing from the user's perspective, mainly for those users not familiar with IPT, but that historically have worked with spreadsheets. Although the DwC-A generated by the add-on still needs to be published using IPT, it does provide a simpler interface (i.e., spreadsheet) for mapping data sets to DwC than IPT. Even though the IPT includes many more features than the Darwin Core Assistant Add-on, we expect that the Add-on can be a "starting point" for users unfamiliar with biodiversity informatics before they move on to more advanced data publishing tools. On the other hand, Zenodo integration allows users to share and cite their standardized data sets without publishing them via IPT, which can be useful for users without access to an IPT installation. Additionally, we are working on new features and future releases will include the automatic generation of Global Unique Identifiers for shared records, the possibility of adding additional data standards and DwC extensions, integration with GBIF REST API and with IPT REST API.

The Global Biodiversity Information Facility (GBIF) is an international network and data infrastructure that provides free and open access to biodiversity data from around the world, enabling scientists, policymakers, and the public to explore and analyze information about the Earth's living organisms. Originally developing at a distance from GBIF, metabarcoding of DNA has become a standard tool for detecting species in bulk samples or environmental samples such as soil, water, and air. Raw sequence data (fastq files) are often shared and deposited in dedicated repositories. Seen from a biodiversity documenting perspective, raw sequences have limited value, as several steps of bioinformatic processing and filtering are needed to arrive at a credible set of sequences that can be interpreted by comparing to a sequence reference database. Most often, such interpretated DNA metabarcoding data come in the shape a table with abundances of so-called Amplicon Sequence Variants (ASV) or Operational Taxonomic Units (OTU) across samples—a so-called ASV/OTU table—and some associated files, e.g., spatiotemporal and other sample metadata, and taxonomic inferences of sequences. In this session we present GBIF state-of-work and plans for developing and improving publishing and standardisation of OTU table-like biodiversity data for easier and broader reuse, including a prototype tool using the OTU table as a publishing model, mapping this research-familiar data format to the Darwin Core standard (Darwin Core Task Group 2009).

Currently, Linked Open Data (LOD) have enabled integrated data sharing across disciplines over the Web. However, for LOD users, in areas such as biodiversity (which massively use the Web to disseminate data), the task of transforming data file contents in CSV (Comma Separated Value) to RDF (Resource Description Framework) is not trivial. We have developed a new approach to map data files in CSV to RDF format based on a domain-specific language (DSL) called BioDSL. Using it, biodiversity data users can write compact programs to map their data to RDF and link them to the LOD. Biodiversity vocabularies and ontologies, such as Darwin Core and OntoBio, can be used with BioDSL to enrich user data. Existing tools are exclusively focused on mapping (CSV to RDF), offering little or no support for linking data to the LOD (interconnecting user entities to LOD entities). They also are more complex to use than BioDSL.&#x0D;

Currently, Linked Open Data (LOD) have enabled integrated data sharing across disciplines over the Web. However, for LOD users, in areas such as biodiversity (which massively use the Web to disseminate data), the task of transforming data file contents in CSV (Comma Separated Value) to RDF (Resource Description Framework) is not trivial. We have developed a new approach to map data files in CSV to RDF format based on a domain-specific language (DSL) called BioDSL. Using it, biodiversity data users can write compact programs to map their data to RDF and link them to the LOD. Biodiversity vocabularies and ontologies, such as Darwin Core and OntoBio, can be used with BioDSL to enrich user data. Existing tools are exclusively focused on mapping (CSV to RDF), offering little or no support for linking data to the LOD (interconnecting user entities to LOD entities). They also are more complex to use than BioDSL.

This study is a reflection on educational reality based on certainty and uncertainty coordinates. Exploring the significance of the binomial reality, generated by the different degrees of certainty, perceived by the actors involved in teaching, the article proposes a few acting options, in order to develop an appropriate orientation of the teacher training process, in a contemporary society marked by the “certainty of uncertainty”. Embracing the unknown, coping with unfamiliar situations, reflecting constructively on one’s own mistakes, as part of a teacher daily activity, are generated by a genuine positioning towards uncertainty in education, raising it from the status of a problem to the hypostasis of an opportunity. Mapping uncertainty through resilience, building confidence in experiencing doubt, reshaping learning by daring to approach dilemmas and stepping out of inaction can be viewed as valid alternatives in developing a professional self in a changing environment. That claims a rethinking of teacher training in terms of developing abilities for sustaining appropriate responses and a proper understanding of the relationship between certainty and uncertainty in education, having the intention of building quality learning experiences. The concepts of choice and change are about to conquer the ideas of standards and stability in educational context as proofs of a renewed approach in order to delineate core drivers of human development in contemporaneity. That is why rethinking teacher training needs to focus on articulating the reflective practicing with experiencing a constant change, integrating the multiplicity of opportunities in a supportive learning environment for developing a global competence, in order to respond effectively to the contemporary challenges.

Introduction There are steadily expanding claims that teacher community contributes to the improvement in the practices of teaching and schooling (cf., Witziers et al., 1999; Little, 2003; Darling-Hammond and Bransford, 2005) as well as individual teacher development and the collective capacity schools (cf., Seashore Louis et al., 1996; Grossman et al., 2001; Imants et al., 2001; Achinstein, 2002; Piazza et al., 2009). In line with Grossman et al. (2001), we are interested in teacher community at the local level, where interaction, dialogue and trust are necessary elements of building cohesion. Based on the definition of community by Bellah et al (1985), we define a teacher community as ‘a group of teachers who are socially interdependent, who participate together in discussion and decision making, and share and build knowledge with a group identity, shared domain and goals, and shared interactional repertoire’. This means that we distinguish three core features of a teacher community: group identity, shared domain and goals, and shared interactional repertoire. These features refers to the nature of a community (group identity), what a community is about (shared domain), and how it functions (shared interactional repertoire). In a literature review of Brouwer et al. (in press), 31 design principles have been retrieved from the literature about the setup of efficient and effective teacher communities in schools. Examples of design principles are the promotion of interdependence, shared responsibility and individual accountability, the development of guidelines for dealing with conflicts and decision-making, and the consideration of group size and heterogeneity of expertise. The use of online workspaces might solve issues in communication and collaboration of school teachers as well as in establishing feelings of cohesion and trust –in addition to face-to-face interaction and collaboration. However, the problem is that we do not know how online workspace should be designed in order to efficiently and effectively communities of teachers in secondary school. Method and results A systematic review will be presented of online workspaces from the perspective of how teacher communities should be designed in order to effectively and efficiently support collaboration and communication of teachers in secondary schools. These tools includes tools for collaborative writing, file sharing, mind mapping, group communication, social networking, wikis and blogs, web presenting, whiteboarding, web and video conferencing, chat and instant messaging, and project management and event scheduling. Subsequently, online collaboration tools are evaluated on the way their functionalities potentially facilitate the design principles that have been worked out. Literature Achinstein, B. (2002), “Conflict amid community: The micropolitics of teacher collaboration”, Teacher College Record, Vol.104 No.3, pp.421-455. Bellah, R. N., Madsen, N., Sullivan, W. M., Swidler, A., &amp; Tipton, S. M. (1985). Habits of the heart; Individualism and commitment in American life. Berkeley, CA: University of Calidofornia Press. Brouwer, P., Brekelmans, M., Nieuwenhuis, L., &amp; Simons, P. R. J. (in press). Fostering teacher community development A review of design principles and a case study of an innovative interdisciplinary team. Learning Environments Research. Darling-Hammond, L. and Bransford, J. (Eds.) (2005), Preparing teachers for a changing world. What teachers should learn and be able to do, Jossey-Bass, San Francisco. Grossman, P., Wineburg, S., &amp; Woolworth, S. (2001). Toward a theory of teacher community. Teacher College Record, 103, 942-1012. Imants, J., Sleegers, P. and Witziers, B. (2001), “The tension between sub-structures in secondary schools and educational reform”, School Leadership &amp; Management, Vol.21, No.3, pp.289-307. Little, J. W. (2003), “Inside teacher community: representations of classroom practice”, Teachers College Record, Vol.105 No.6, pp.913-945. Piazza, P., McNeill, K.L. and Hittinger, J. (2009), “Developing a voluntary teacher community: The role of professional development, collaborative learning and conflict”, Paper presented at the annual meeting of the American Educational Research Association, April, San Diego, CA. Seashore Louis, K., Marks, H. and Kruse, S. (1996), “Teachers’ professional community in restructuring schools.” American Educational Research Journal, Vol.33 No.4, pp.757-798. Witziers, B., Sleegers, P. and Imants, J. (1999), “Departments as teams: functioning, variations and alternatives”, School Leadership &amp; Management, Vol.19 No.3, pp.293- 304.