scholarly journals The Pensoft Data Publishing Workflow: The FAIRway from articles to Linked Open Data

2019 ◽  
Vol 3 ◽  
Author(s):  
Lyubomir Penev ◽  
Teodor Georgiev ◽  
Viktor Senderov ◽  
Mariya Dimitrova ◽  
Pavel Stoev
Keyword(s):  
Open Data ◽  
Structured Data ◽  
Linked Open Data ◽  
Data Publishing ◽  
Knowledge Graph ◽  
Supplementary File ◽  
Biodiversity Data ◽  
Text Format ◽  
Biodiversity Knowledge ◽  
Data Elements

As one of the first advocates of open access and open data in the field of biodiversity publishiing, Pensoft has adopted a multiple data publishing model, resulting in the ARPHA-BioDiv toolbox (Penev et al. 2017). ARPHA-BioDiv consists of several data publishing workflows and tools described in the Strategies and Guidelines for Publishing of Biodiversity Data and elsewhere: Data underlying research results are deposited in an external repository and/or published as supplementary file(s) to the article and then linked/cited in the article text; supplementary files are published under their own DOIs and bear their own citation details. Data deposited in trusted repositories and/or supplementary files and described in data papers; data papers may be submitted in text format or converted into manuscripts from Ecological Metadata Language (EML) metadata. Integrated narrative and data publishing realised by the Biodiversity Data Journal, where structured data are imported into the article text from tables or via web services and downloaded/distributed from the published article. Data published in structured, semanticaly enriched, full-text XMLs, so that several data elements can thereafter easily be harvested by machines. Linked Open Data (LOD) extracted from literature, converted into interoperable RDF triples in accordance with the OpenBiodiv-O ontology (Senderov et al. 2018) and stored in the OpenBiodiv Biodiversity Knowledge Graph. Data underlying research results are deposited in an external repository and/or published as supplementary file(s) to the article and then linked/cited in the article text; supplementary files are published under their own DOIs and bear their own citation details. Data deposited in trusted repositories and/or supplementary files and described in data papers; data papers may be submitted in text format or converted into manuscripts from Ecological Metadata Language (EML) metadata. Integrated narrative and data publishing realised by the Biodiversity Data Journal, where structured data are imported into the article text from tables or via web services and downloaded/distributed from the published article. Data published in structured, semanticaly enriched, full-text XMLs, so that several data elements can thereafter easily be harvested by machines. Linked Open Data (LOD) extracted from literature, converted into interoperable RDF triples in accordance with the OpenBiodiv-O ontology (Senderov et al. 2018) and stored in the OpenBiodiv Biodiversity Knowledge Graph. The above mentioned approaches are supported by a whole ecosystem of additional workflows and tools, for example: (1) pre-publication data auditing, involving both human and machine data quality checks (workflow 2); (2) web-service integration with data repositories and data centres, such as Global Biodiversity Information Facility (GBIF), Barcode of Life Data Systems (BOLD), Integrated Digitized Biocollections (iDigBio), Data Observation Network for Earth (DataONE), Long Term Ecological Research (LTER), PlutoF, Dryad, and others (workflows 1,2); (3) semantic markup of the article texts in the TaxPub format facilitating further extraction, distribution and re-use of sub-article elements and data (workflows 3,4); (4) server-to-server import of specimen data from GBIF, BOLD, iDigBio and PlutoR into manuscript text (workflow 3); (5) automated conversion of EML metadata into data paper manuscripts (workflow 2); (6) export of Darwin Core Archive and automated deposition in GBIF (workflow 3); (7) submission of individual images and supplementary data under own DOIs to the Biodiversity Literature Repository, BLR (workflows 1-3); (8) conversion of key data elements from TaxPub articles and taxonomic treatments extracted by Plazi into RDF handled by OpenBiodiv (workflow 5). These approaches represent different aspects of the prospective scholarly publishing of biodiversity data, which in a combination with text and data mining (TDM) technologies for legacy literature (PDF) developed by Plazi, lay the ground of an entire data publishing ecosystem for biodiversity, supplying FAIR (Findable, Accessible, Interoperable and Reusable data to several interoperable overarching infrastructures, such as GBIF, BLR, Plazi TreatmentBank, OpenBiodiv and various end users.

scholarly journals Data ownership and data publishing

2019 ◽  
Vol 2 ◽  
Author(s):  
Lyubomir Penev
Keyword(s):  
Data Protection ◽  
Open Data ◽  
Data Publishing ◽  
Supplementary File ◽  
Biodiversity Data ◽  
Biodiversity Knowledge ◽  
Data Ownership ◽  
Data Hoarding ◽  
Data Elements ◽  
Access To Data

"Data ownership" is actually an oxymoron, because there could not be a copyright (ownership) on facts or ideas, hence no data onwership rights and law exist. The term refers to various kinds of data protection instruments: Intellectual Property Rights (IPR) (mostly copyright) asserted to indicate some kind of data ownership, confidentiality clauses/rules, database right protection (in the European Union only), or personal data protection (GDPR) (Scassa 2018). Data protection is often realised via different mechanisms of "data hoarding", that is witholding access to data for various reasons (Sieber 1989). Data hoarding, however, does not put the data into someone's ownership. Nonetheless, the access to and the re-use of data, and biodiversuty data in particular, is hampered by technical, economic, sociological, legal and other factors, although there should be no formal legal provisions related to copyright that may prevent anyone who needs to use them (Egloff et al. 2014, Egloff et al. 2017, see also the Bouchout Declaration). One of the best ways to provide access to data is to publish these so that the data creators and holders are credited for their efforts. As one of the pioneers in biodiversity data publishing, Pensoft has adopted a multiple-approach data publishing model, resulting in the ARPHA-BioDiv toolbox and in extensive Strategies and Guidelines for Publishing of Biodiversity Data (Penev et al. 2017a, Penev et al. 2017b). ARPHA-BioDiv consists of several data publishing workflows: Deposition of underlying data in an external repository and/or its publication as supplementary file(s) to the related article which are then linked and/or cited in-tex. Supplementary files are published under their own DOIs to increase citability). Description of data in data papers after they have been deposited in trusted repositories and/or as supplementary files; the systme allows for data papers to be submitted both as plain text or converted into manuscripts from Ecological Metadata Language (EML) metadata. Import of structured data into the article text from tables or via web services and their susequent download/distribution from the published article as part of the integrated narrative and data publishing workflow realised by the Biodiversity Data Journal. Publication of data in structured, semanticaly enriched, full-text XMLs where data elements are machine-readable and easy-to-harvest. Extraction of Linked Open Data (LOD) from literature, which is then converted into interoperable RDF triples (in accordance with the OpenBiodiv-O ontology) (Senderov et al. 2018) and stored in the OpenBiodiv Biodiversity Knowledge Graph Deposition of underlying data in an external repository and/or its publication as supplementary file(s) to the related article which are then linked and/or cited in-tex. Supplementary files are published under their own DOIs to increase citability). Description of data in data papers after they have been deposited in trusted repositories and/or as supplementary files; the systme allows for data papers to be submitted both as plain text or converted into manuscripts from Ecological Metadata Language (EML) metadata. Import of structured data into the article text from tables or via web services and their susequent download/distribution from the published article as part of the integrated narrative and data publishing workflow realised by the Biodiversity Data Journal. Publication of data in structured, semanticaly enriched, full-text XMLs where data elements are machine-readable and easy-to-harvest. Extraction of Linked Open Data (LOD) from literature, which is then converted into interoperable RDF triples (in accordance with the OpenBiodiv-O ontology) (Senderov et al. 2018) and stored in the OpenBiodiv Biodiversity Knowledge Graph In combination with text and data mining (TDM) technologies for legacy literature (PDF) developed by Plazi, these approaches show different angles to the future of biodiversity data publishing and, lay the foundations of an entire data publishing ecosystem in the field, while also supplying FAIR (Findable, Accessible, Interoperable and Reusable) data to several interoperable overarching infrastructures, such as Global Biodiversity Information Facility (GBIF), Biodiversity Literature Repository (BLR), Plazi TreatmentBank, OpenBiodiv, as well as to various end users.

scholarly journals Options to streamline and enrich biodiversity data aggregation

2018 ◽  
Vol 2 ◽  
pp. e26808
Author(s):  
Donald Hobern ◽  
Andrea Hahn ◽  
Tim Robertson
Keyword(s):  
Data Management ◽  
Open Data ◽  
Linked Open Data ◽  
Biodiversity Data ◽  
Rigorous Approach ◽  
Governance Model ◽  
Natural History Collection ◽  
Wide Range ◽  
Essential Biodiversity Variables ◽  
Data Elements

The success of Darwin Core and ABCD Schema as flexible standards for sharing specimen data and species occurrence records has enabled GBIF to aggregate around one billion data records. At the same time, other thematic, national or regional aggregators have developed a wide range of other data indexes and portals, many of which enrich the data by interpreting and normalising elements not currently handled by GBIF or by linking other data from geospatial layers, trait databases, etc. Unfortunately, although each of these aggregators has specific strengths and supports particular audiences, this diversification produces many weaknesses and deficiencies for data publishers and for data users, including: incomplete and inconsistent inclusion of relevant datasets; proliferation of record identifiers; inconsistent and bespoke workflows to interpret and standardise data; absence of any shared basis for linked open data and annotations; divergent data formats and APIs; lack of clarity around provenance and impact; etc. The time is ripe for the global community to review these processes. From a technical standpoint, it would be feasible to develop a shared, integrated pipeline which harvested, validated and normalised all relevant biodiversity data records on behalf of all stakeholders. Such a system could build on TDWG expertise to standardise data checks and all stages in data transformation. It could incorporate a modular structure that allowed thematic, national or regional networks to generate additional data elements appropriate to the needs of their users, but for all of these elements to remain part of a single record with a single identifier, facilitating a much more rigorous approach to linked open data. Most of the other issues we currently face around fitness-for-use, predictability and repeatability, transparency and provenance could be supported much more readily under such a model. The key challenges that would need to be overcome would be around social factors, particularly to deliver a flexible and appropriate governance model and to allow research networks, national agencies, etc. to embed modular components within a shared workflow. Given the urgent need to improve data management to support Essential Biodiversity Variables and to deliver an effective global virtual natural history collection, we should review these challenges and seek to establish a data management and aggregation architecture that will support us for the coming decades.

scholarly journals Robustifying Scholia: paving the way for knowledge discovery and research assessment through Wikidata

2019 ◽  
Vol 5 ◽  
Author(s):  
Lane Rasberry ◽  
Egon Willighagen ◽  
Finn Nielsen ◽  
Daniel Mietchen
Keyword(s):  
Knowledge Discovery ◽  
Large Scale ◽  
Knowledge Workers ◽  
Open Data ◽  
Linked Open Data ◽  
Data Curation ◽  
Research Assessment ◽  
Knowledge Graph ◽  
Incomplete Datasets ◽  
Active Contributor

Knowledge workers like researchers, students, journalists, research evaluators or funders need tools to explore what is known, how it was discovered, who made which contributions, and where the scholarly record has gaps. Existing tools and services of this kind are not available as Linked Open Data, but Wikidata is. It has the technology, active contributor base, and content to build a large-scale knowledge graph for scholarship, also known as WikiCite. Scholia visualizes this graph in an exploratory interface with profiles and links to the literature. However, it is just a working prototype. This project aims to "robustify Scholia" with back-end development and testing based on pilot corpora. The main objective at this stage is to attain stability in challenging cases such as server throttling and handling of large or incomplete datasets. Further goals include integrating Scholia with data curation and manuscript writing workflows, serving more languages, generating usage stats, and documentation.

scholarly journals Liberating links between datasets using lightweight data publishing: an example using plant names and the taxonomic literature

2018 ◽  
Author(s):  
Roderic D. M. Page
Keyword(s):  
Web Server ◽  
Data Publishing ◽  
Knowledge Graph ◽  
Biodiversity Knowledge ◽  
Plant Names ◽  
Cross Links ◽  
Minimal Effort

AbstractConstructing a biodiversity knowledge graph will require making millions of cross links between diversity entities in different datasets. Researchers trying to bootstrap the growth of the biodiversity knowledge graph by constructing databases of links between these entities lack obvious ways to publish these sets of links. One appealing and lightweight approach is to create a “datasette”, a database that is wrapped together with a simple web server that enables users to query the data. Datasettes can be packaged into Docker containers and hosted online with minimal effort. This approach is illustrated using a dataset of links between globally unique identifiers for plant taxonomic names, and identifiers for the taxonomic articles that published those names.

scholarly journals Liberating links between datasets using lightweight data publishing: an example using plant names and the taxonomic literature

2018 ◽  
Vol 6 ◽  
pp. e27539 ◽  
Author(s):  
Roderic Page
Keyword(s):  
Web Server ◽  
Data Publishing ◽  
Knowledge Graph ◽  
Biodiversity Knowledge ◽  
Plant Names ◽  
Cross Links ◽  
Minimal Effort

Constructing a biodiversity knowledge graph will require making millions of cross links between diversity entities in different datasets. Researchers trying to bootstrap the growth of the biodiversity knowledge graph by constructing databases of links between these entities lack obvious ways to publish these sets of links. One appealing and lightweight approach is to create a "datasette", a database that is wrapped together with a simple web server that enables users to query the data. Datasettes can be packaged into Docker containers and hosted online with minimal effort. This approach is illustrated using a dataset of links between globally unique identifiers for plant taxonomic namesand identifiers for the taxonomic articles that published those names.

Link maintenance for integrity in linked open data evolution: Literature survey and open challenges

Semantic Web ◽  
2020 ◽  
pp. 1-25
Author(s):  
Andre Gomes Regino ◽  
Julio Cesar dos Reis ◽  
Rodrigo Bonacin ◽  
Ahsan Morshed ◽  
Timos Sellis
Keyword(s):  
Semantic Web ◽  
Linked Data ◽  
Open Data ◽  
Linked Open Data ◽  
Evolutionary Characteristic ◽  
Rdf Data ◽  
Link Maintenance ◽  
Data Elements ◽  
Data Evolution ◽  
Over Time

RDF data has been extensively deployed describing various types of resources in a structured way. Links between data elements described by RDF models stand for the core of Semantic Web. The rising amount of structured data published in public RDF repositories, also known as Linked Open Data, elucidates the success of the global and unified dataset proposed by the vision of the Semantic Web. Nowadays, semi-automatic algorithms build connections among these datasets by exploring a variety of methods. Interconnected open data demands automatic methods and tools to maintain their consistency over time. The update of linked data is considered as key process due to the evolutionary characteristic of such structured datasets. However, data changing operations might influence well-formed links, which turns difficult to maintain the consistencies of connections over time. In this article, we propose a thorough survey that provides a systematic review of the state of the art in link maintenance in linked open data evolution scenario. We conduct a detailed analysis of the literature for characterising and understanding methods and algorithms responsible for detecting, fixing and updating links between RDF data. Our investigation provides a categorisation of existing approaches as well as describes and discusses existing studies. The results reveal an absence of comprehensive solutions suited to fully detect, warn and automatically maintain the consistency of linked data over time.

scholarly journals Ozymandias: A biodiversity knowledge graph

2018 ◽  
Author(s):  
Roderic D. M. Page
Keyword(s):  
Shared Knowledge ◽  
Knowledge Graph ◽  
Web Interface ◽  
Biodiversity Data ◽  
Knowledge Space ◽  
Link Type ◽  
Biodiversity Knowledge

AbstractEnormous quantities of biodiversity data are being made available online, but much of this data remains isolated in their own silos. One approach to breaking these silos is to map local, often database-specific identifiers to shared global identifiers. This mapping can then be used to construct a knowledge graph, where entities such as taxa, publications, people, places, specimens, sequences, and institutions are all part of a single, shared knowledge space. Motivated by the 2018 GBIF Ebbe Nielsen Challenge I explore the feasibility of constructing a “biodiversity knowledge graph” for the Australian fauna. These steps involved in constructing the graph are described, and examples its application are discussed. A web interface to the knowledge graph (called “Ozymandias”) is available at https://ozymandias-demo.herokuapp.com.

scholarly journals Training and hackathon on building biodiversity knowledge graphs

2019 ◽  
Vol 5 ◽  
Author(s):  
Joel Sachs ◽  
Roderic Page ◽  
Steven J Baskauf ◽  
Jocelyn Pender ◽  
Beatriz Lujan-Toro ◽  
...  
Keyword(s):  
Knowledge Graph ◽  
Graph Database ◽  
Biodiversity Informatics ◽  
Biodiversity Data ◽  
Specific Knowledge ◽  
Biodiversity Knowledge ◽  
Training Event ◽  
Web Infrastructure ◽  
Ongoing Development ◽  
Knowledge Graphs

Knowledge graphs have the potential to unite disconnected digitized biodiversity data, and there are a number of efforts underway to build biodiversity knowledge graphs. More generally, the recent popularity of knowledge graphs, driven in part by the advent and success of the Google Knowledge Graph, has breathed life into the ongoing development of semantic web infrastructure and prototypes in the biodiversity informatics community. We describe a one week training event and hackathon that focused on applying three specific knowledge graph technologies – the Neptune graph database; Metaphactory; and Wikidata - to a diverse set of biodiversity use cases.We give an overview of the training, the projects that were advanced throughout the week, and the critical discussions that emerged. We believe that the main barriers towards adoption of biodiversity knowledge graphs are the lack of understanding of knowledge graphs and the lack of adoption of shared unique identifiers. Furthermore, we believe an important advancement in the outlook of knowledge graph development is the emergence of Wikidata as an identifier broker and as a scoping tool. To remedy the current barriers towards biodiversity knowledge graph development, we recommend continued discussions at workshops and at conferences, which we expect to increase awareness and adoption of knowledge graph technologies.

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