Suchergebnisse

This presentation addresses cultural heritage data-sharing practices through the use of Republic of Korea open government data for data-curation and data integration. Data curation enables data-sharing throughout the data management life cycle to create new value for new user needs. Our research employed a visualization phase, in which we used domain analytical techniques to better understand the contents of the population of 375 library-related open government cultural heritage data available at the Korean Open Government Website (http://data.go.kr/). Researchers translated all records from Korean to English. Data were in unstructured and in heterogeneous formats such as file formats, data formats and or web addresses. For data curation and integration, we employed the meta-level ontology known as the CIDOC-CRM, which we applied qualitatively to small sets of carefully selected records. To map instantiation of records, which is required for data integration, we used FRBRoo (Functional Requirements for Bibliographic Records – object oriented), an extension of the CIDOC CRM, to map the instantiation of data records in a typical data-sharing scenario. Then, equivalent mapping processes were comparatively tested with visualizations to demonstrate the effective harmonization between the CIDOC CRM and FRBRoo, which enables the integration of metadata and data curation from unstructured and heterogeneous formats. This presentation may contribute to the cross- or meta-institutional integration of curation across institutional boundaries in cultural heritage.

The amount of data in our world today is substantially mammoth. Many of the personal and non-personal aspects of our day to day activities are aggregated and stored as data by both businesses and governments. The increasing data captured through multimedia, social media, and the Internet of Things is a phenomenon that needs to be properly examined. In this article, we explore this topic, and analyse the term data ownership. We aim to raise awareness and trigger debate for policy makers around data ownership and the need to improve existing data protection and privacy laws and legislation at both national and international levels.

"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 ...

"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 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.

AbstractThe article discusses different examples of data-driven policing, its legal provisions and effects on a society's understanding of public security. It distinguishes between (a) the collection of classical data such as fingerprints or DNA, which serve to identify suspects and to collect evidence, (b) the processes and the impetus of big data, and (c) the networking of files from different security authorities. Discussing systematic forecasting tools, the article works out a significant difference between the prediction of incidents such as home burglary in the case of predictive policing, and the identification of individuals deemed to be at risk of involvement in various forms of crime in the case of risk control programs. Data and personality protection are interrelated issues.

The letter to the Editor is in response to the manuscript by Hertzog et al. (2023) titled "Data management instruments to Protect the personal information of Children and Adolescents in sub-Saharan Africa." The letter elaborates on personal data protection, particularly the POPI Act's data management requirements; the DNA Act mandates specific measures to ensure the data integrity and security of the NFDD's information. In addition, it criminalises the misuse or compromise of the data's integrity within the NFDD. In addition, the DNA Act established the National Forensic Oversight and Ethical Board (NFOEB), which is responsible for overseeing ethical compliance, implementing the Act, and preserving data integrity within the NFDD. The NFOEB is also responsible for investigating any complaints regarding DNA forensics and the management of the NFDD.

Science functions best within a liberal democracy. Every hypothesis test is an expression of doubt, as it carries with it the implication that a particular presumption may be incorrect (Kruschke 1998), whereas authoritarianism punishes challenges to prescribed beliefs. Consequently, science can lead to true innovation and improvements in knowledge only when laws and social norms permit dissent.

ObjectivesGovernments acquire extensive data holdings and face increasing pressure to make these available as record-level microdata for research. However, turning data into research-ready data (RRD) is not a straightforward exercise. We demonstrate how even in simple cases researcher involvement can bring substantial rewards for effective RRD development.
MethodsThis paper reports on an ADRUK-funded project to take a dataset originally collected by the Office for National Statistics for official statistics (the UK Annual Survey of Hours and Earnings, ASHE), formally review its microanalytical characteristics, link it to Census 2011 data, and prepare a new 'research ready dataset' with appropriate documentation and coding. This should have been straightforward as the datasets had already been widely used as research microdata. However, the involvement of academic researchers in the production of research-ready data led to many important new insights.
ResultsThe research programme had 3 aims: testing assumptions about the data; reviewing data quality; and adding value.
Because of its sampling model, ASHE is assumed to have random non-response both longitudinally and in cross section. The research team showed that was untrue: there was higher attrition than expected, and both longitudinal and cross-sectional non-response appeared non-random..
The data quality review showed further concerns about the accuracy of some geographical indicators, and some variables of opaque provenance; in contrast, we confirmed the accuracy of administrative variables created by ONS.
As well as being important for researchers, these findings have the potential for significant effects on official statistics produced from the source data, enhancing the value of the source data.
Finally, value was added from new variables which reflected the team's wide research interests
ConclusionOften in government the assumption is that creating RRDs is a matter of creatign files and giving access to the researchers. Insights from our work show that the deep involvement of the research community can bring rewards for both data holders and researchers. For RRDs, researcher-led construction is vital.