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Animals : An Open Access Journal From... Jun 2023The illegal wildlife trade is a significant threat to global biodiversity, often targeting already threatened species. In combating the trade, it is critical to know the...
The illegal wildlife trade is a significant threat to global biodiversity, often targeting already threatened species. In combating the trade, it is critical to know the provenance of the traded animal or part to facilitate targeted conservation actions, such as education and enforcement. Here, we present and compare two methods, portable X-ray fluorescence (pXRF) and stable isotope analysis (SIA), to determine both the geographic and source provenance (captive or wild) of traded animals and their parts. Using three critically endangered, frequently illegally traded Philippine species, the Palawan forest turtle (), the Philippine cockatoo (), and the Philippine pangolin (), we demonstrate that using these methods, we can more accurately assign provenance using pXRF data (x¯ = 83%) than SIA data (x¯ = 47%). Our results indicate that these methods provide a valuable forensic tool that can be used in combating the illegal wildlife trade.
PubMed: 37443963
DOI: 10.3390/ani13132165 -
Annals of Anatomy = Anatomischer... Oct 2022The use of 21st Century technology in anatomy teaching and the recent crisis caused by the Coronavirus pandemic has stimulated anatomists to ponder the ethics...
BACKGROUND
The use of 21st Century technology in anatomy teaching and the recent crisis caused by the Coronavirus pandemic has stimulated anatomists to ponder the ethics surrounding the utilisation of digital images from human bodies of known and unknown provenance in teaching.
AIM
This novel study explores the awareness of southern African anatomy educators regarding the provenance and ethical use of human material in digital resources for E-learning purposes.
MATERIALS AND METHOD
Anatomy educators (both members and non-members of the Anatomical Society of Southern Africa including postgraduate students in anatomy) located in 15 health sciences facilities in southern Africa were asked to participate in the survey which consisted of an anonymous, cross-sectional, questionnaire conducted on an online research data system, REDCap.
RESULTS
While 52% of respondents used E-learning resources sourced from their own departments for teaching, only 58% of these had knowledge of the provenance of the human material used. Of the 72% of respondents using images from external E-learning resources, 64% did not know the provenance of the human material in these resources. Some southern African anatomists considered anonymity as equivalent to informed consent. Regarding the acceptability of unclaimed bodies for online images, 37% of respondents were against the use of these bodies, while 20% indicated that it was acceptable. Personal internal moral conflict was acknowledged regarding the use of material from unclaimed bodies, particularly during crises such as the Coronavirus pandemic when digital resources were limited.
DISCUSSION AND CONCLUSIONS
Factors such as lack of awareness of provenance, the law in South Africa and using anonymity for consent, influence the ethical behaviour of southern African anatomists. Clear guiding principles would be of value for anatomists globally with respect to consent to the taking and distribution of images, and transparency on the source of the digital images provided in digital texts and online platforms. The establishment of both an oversight and ethics committee at institutions where digital imaging will be used is recommended.
Topics: Humans; Cadaver; Digital Technology; Cross-Sectional Studies; Anatomists; Morals; Anatomy; Teaching
PubMed: 35987425
DOI: 10.1016/j.aanat.2022.151990 -
Journal of Personalized Medicine Jun 2023This article aims to perform a Systematic Literature Review (SLR) to better understand the structures of different methods, techniques, models, methodologies, and... (Review)
Review
AIMS
This article aims to perform a Systematic Literature Review (SLR) to better understand the structures of different methods, techniques, models, methodologies, and technologies related to provenance data management in health information systems (HISs). The SLR developed here seeks to answer the questions that contribute to describing the results.
METHOD
An SLR was performed on six databases using a search string. The backward and forward snowballing technique was also used. Eligible studies were all articles in English that presented on the use of different methods, techniques, models, methodologies, and technologies related to provenance data management in HISs. The quality of the included articles was assessed to obtain a better connection to the topic studied.
RESULTS
Of the 239 studies retrieved, 14 met the inclusion criteria described in this SLR. In order to complement the retrieved studies, 3 studies were included using the backward and forward snowballing technique, totaling 17 studies dedicated to the construction of this research. Most of the selected studies were published as conference papers, which is common when involving computer science in HISs. There was a more frequent use of data provenance models from the PROV family in different HISs combined with different technologies, among which blockchain and middleware stand out. Despite the advantages found, the lack of technological structure, data interoperability problems, and the technical unpreparedness of working professionals are still challenges encountered in the management of provenance data in HISs.
CONCLUSION
It was possible to conclude the existence of different methods, techniques, models, and combined technologies, which are presented in the proposal of a taxonomy that provides researchers with a new understanding about the management of provenance data in HISs.
PubMed: 37373980
DOI: 10.3390/jpm13060991 -
Heliyon Feb 2023Open Educational Resources (OER) can be adapted and combined to create new resources that better meet the specific needs of different kinds of users and scenarios. In... (Review)
Review
Open Educational Resources (OER) can be adapted and combined to create new resources that better meet the specific needs of different kinds of users and scenarios. In this sense, OER strongly contributes to generating and sharing educational knowledge. Due to the possibility of creating a new OER through the revision and remix activities, the original OER and the transformation process should be adequately identified. This way, the user of the OER has enough information about the history of the resource and, thus, can use it with confidence and security. In this context, determining data provenance, which describes the history of a data from its origin to its current state, becomes very relevant. For OER, there are examples of metadata standards and digital repositories that help to obtain the data provenance. However, the information collected is insufficient to identify the entire history of the provenance of OER. This article proposes a Provenance Model for OER called the ProvOER Model, which allows the documentation and identification of the provenance of OER. For this purpose, a minimum set of metadata was defined that reflects the OER intrinsic properties and the activities that created a new OER. The experiments showed that the ProvOER Model produced a suitable representation of the provenance of OER. In addition, the ProvOER Model allowed identifying the original OER used in a revise or remix activity and the continuous stretch used to create a new resource.
PubMed: 36755614
DOI: 10.1016/j.heliyon.2023.e13311 -
Journal of Biomedical Semantics Jan 2022The advancement of science and technologies play an immense role in the way scientific experiments are being conducted. Understanding how experiments are performed and...
BACKGROUND
The advancement of science and technologies play an immense role in the way scientific experiments are being conducted. Understanding how experiments are performed and how results are derived has become significantly more complex with the recent explosive growth of heterogeneous research data and methods. Therefore, it is important that the provenance of results is tracked, described, and managed throughout the research lifecycle starting from the beginning of an experiment to its end to ensure reproducibility of results described in publications. However, there is a lack of interoperable representation of end-to-end provenance of scientific experiments that interlinks data, processing steps, and results from an experiment's computational and non-computational processes.
RESULTS
We present the "REPRODUCE-ME" data model and ontology to describe the end-to-end provenance of scientific experiments by extending existing standards in the semantic web. The ontology brings together different aspects of the provenance of scientific studies by interlinking non-computational data and steps with computational data and steps to achieve understandability and reproducibility. We explain the important classes and properties of the ontology and how they are mapped to existing ontologies like PROV-O and P-Plan. The ontology is evaluated by answering competency questions over the knowledge base of scientific experiments consisting of computational and non-computational data and steps.
CONCLUSION
We have designed and developed an interoperable way to represent the complete path of a scientific experiment consisting of computational and non-computational steps. We have applied and evaluated our approach to a set of scientific experiments in different subject domains like computational science, biological imaging, and microscopy.
Topics: Knowledge Bases; Reproducibility of Results; Semantic Web; Semantics
PubMed: 34991705
DOI: 10.1186/s13326-021-00253-1 -
AMIA ... Annual Symposium Proceedings.... 2017Scientific reproducibility is critical for biomedical research as it enables us to advance science by building on previous results, helps ensure the success of...
Scientific reproducibility is critical for biomedical research as it enables us to advance science by building on previous results, helps ensure the success of increasingly expensive drug trials, and allows funding agencies to make informed decisions. However, there is a growing "crisis" of reproducibility as evidenced by a recent Nature journal survey of more than 1500 researchers that found that 70% of researchers were not able to replicate results from other research groups and more than 50% of researchers were not able reproduce their own research results. In 2016, the National Institutes of Health (NIH) announced the "Rigor and Reproducibility" guidelines to support reproducibility in biomedical research. A key component of the NIH Rigor and Reproducibility guidelines is the recording and analysis of "provenance" information, which describes the origin or history of data and plays a central role in ensuring scientific reproducibility. As part of the NIH Big Data to Knowledge (BD2K)-funded data provenance project, we have developed a new informatics framework called Provenance for Clinical and Healthcare Research (ProvCaRe) to extract, model, and analyze provenance information from published literature describing research studies. Using sleep medicine research studies that have made their data available through the National Sleep Research Resource (NSRR), we have developed an automated pipeline to identify and extract provenance metadata from published literature that is made available for analysis in the ProvCaRe knowledgebase. NSRR is the largest repository of sleep data from over 40,000 studies involving 36,000 participants and we used 75 published articles describing 6 research studies to populate the ProvCaRe knowledgebase. We evaluated the ProvCaRe knowledgebase with 28,474 "provenance triples" using hypothesis-driven queries to identify and rank research studies based on the provenance information extracted from published articles.
Topics: Algorithms; Biological Ontologies; Biomedical Research; Guidelines as Topic; Health Services Research; Humans; Knowledge Bases; Metadata; National Institutes of Health (U.S.); Reproducibility of Results; Semantics; Sleep; United States
PubMed: 29854241
DOI: No ID Found -
Neuroinformatics Jul 2022Sharing various neuroimaging digital resources have received widespread attention in FAIR (Findable, Accessible, Interoperable and Reusable) neuroscience. In order to...
Sharing various neuroimaging digital resources have received widespread attention in FAIR (Findable, Accessible, Interoperable and Reusable) neuroscience. In order to support a comprehensive understanding of brain cognition, neuroimaging provenance should be constructed to characterize both research processes and results, and integrates various digital resources for quick replication and open cooperation. This brings new challenges to neuroimaging text mining, including fragmented information, lack of labelled corpora, and vague topics. This paper proposes a text mining pipeline for enabling the FAIR neuroimaging study. In order to avoid fragmented information, the Brain Informatics provenance model is redesigned based on NIDM (Neuroimaging Data Model) and FAIR facets. It can systematically capture the provenance requests from the FAIR neuroimaging study and then transform them into a group of text mining tasks. A neuroimaging text mining pipeline combining deep adversarial learning with interaction based topic modeling, called neuroimaging interaction topic model (Neuroimaging-ITM), is proposed to automatically extract neuroimaging provenance and identify research topics in the few-shot scenario. Finally, a group of experiments is completed by using real data from the journal PloS One. The experimental results show that Neuroimaging-ITM can systematically and accurately extract provenance information and obtain high-quality research topics from the full text of neuroimaging articles. Most of the mean F1 values of provenance extraction exceed 0.9. The topic coherence and KL (Kullback-Leibler) divergence reach 9.95 and 0.96 respectively. The results are obviously better than baseline methods.
Topics: Data Mining; Neuroimaging; Neurosciences
PubMed: 35235184
DOI: 10.1007/s12021-022-09571-w -
Studies in Health Technology and... Sep 2019The German Center for Lung Research (DZL) is a research network with the aim of researching respiratory diseases. In order to enable consortium-wide retrospective...
The German Center for Lung Research (DZL) is a research network with the aim of researching respiratory diseases. In order to enable consortium-wide retrospective research and prospective patient recruitment, we perform data integration into a central data warehouse. The enhancements of the underlying ontology is an ongoing process for which we developed the Collaborative Metadata Repository (CoMetaR) tool. Its technical infrastructure is based on the Resource Description Framework (RDF) for ontology representation and the distributed version control system Git for storage and versioning. Ontology development involves a considerable amount of data curation. Data provenance improves its feasibility and quality. Especially in collaborative metadata development, a comprehensive annotation about "who contributed what, when and why" is essential. Although RDF and Git versioning repositories are commonly used, no existing solution captures metadata provenance information in sufficient detail. We propose an enhanced composition of standardized RDF statements for detailed provenance representation. Additionally, we developed an algorithm that extracts and translates provenance data from the repository into the proposed RDF statements.
Topics: Biological Ontologies; Data Warehousing; Humans; Metadata; Prospective Studies; Retrospective Studies
PubMed: 31483277
DOI: 10.3233/SHTI190832 -
IEEE Transactions on Visualization and... Sep 2023In domains, such as agronomy or manufacturing, experts need to consider trade-offs when making decisions that involve several, often competing, objectives. Such analysis...
In domains, such as agronomy or manufacturing, experts need to consider trade-offs when making decisions that involve several, often competing, objectives. Such analysis is complex and may be conducted over long periods of time, making it hard to revisit. In this paper, we consider the use of analytic provenance mechanisms to aid experts recall and keep track of trade-off analysis. We implemented VisProm, a web-based trade-off analysis system, that incorporates in-visualization provenance views, designed to help experts keep track of trade-offs and their objectives. We used VisProm as a technology probe to understand user needs and explore the potential role of provenance in this context. Through observation sessions with three groups of experts analyzing their own data, we make the following contributions. We first, identify eight high-level tasks that experts engaged in during trade-off analysis, such as locating and characterizing interest zones in the trade-off space, and show how these tasks can be supported by provenance visualization. Second, we refine findings from previous work on provenance purposes such as recall and reproduce, by identifying specific objects of these purposes related to trade-off analysis, such as interest zones, and exploration structure (e.g., exploration of alternatives and branches). Third, we discuss insights on how the identified provenance objects and our designs support these trade-off analysis tasks, both when revisiting past analysis and while actively exploring. And finally, we identify new opportunities for provenance-driven trade-off analysis, for example related to monitoring the coverage of the trade-off space, and tracking alternative trade-off scenarios.
PubMed: 35507619
DOI: 10.1109/TVCG.2022.3171074 -
Proceedings. International Conference... Apr 2019Data provenance tools capture the steps used to produce analyses. However, scientists must choose among work-flow provenance systems, which allow arbitrary code but only...
Data provenance tools capture the steps used to produce analyses. However, scientists must choose among work-flow provenance systems, which allow arbitrary code but only track provenance at the granularity of files; provenance APIs, which provide tuple-level provenance, but incur overhead in all computations; and database provenance tools, which track tuple-level provenance through relational operators and support optimization, but support a limited subset of data science tasks. None of these solutions are well suited for tracing errors introduced during common ETL, record alignment, and matching tasks - for data types such as strings, images, etc. Scientists need new capabilities to identify the sources of errors, find why different code versions produce different results, and identify which parameter values affect output. We propose PROVision, a provenance-driven troubleshooting tool that supports ETL and matching computations and traces extraction of content data objects. PROVision extends database-style provenance techniques to capture equivalences, support optimizations, and enable selective evaluation. We formalize our extensions, implement them in the PROVision system, and validate their effectiveness and scalability for common ETL and matching tasks.
PubMed: 31595143
DOI: 10.1109/ICDE.2019.00025