Data Science Journal最新文献

{"title":"On the Application of Principal Component Analysis to Classification Problems","authors":"Jianwei Zheng, C. Rakovski","doi":"10.5334/dsj-2021-026","DOIUrl":"https://doi.org/10.5334/dsj-2021-026","url":null,"abstract":"Principal Component Analysis (PCA) is a commonly used technique that uses the correlation structure of the original variables to reduce the dimensionality of the data. This reduction is achieved by considering only the first few principal components for a subsequent analysis. The usual inclusion criterion is defined by the proportion of the total variance of the principal components exceeding a predetermined threshold. We show that in certain classification problems, even extremely high inclusion threshold can negatively impact the classification accuracy. The omission of small variance principal components can severely diminish the performance of the models. We noticed this phenomenon in classification analyses using high dimension ECG data where the most common classification methods lost between 1 and 6% of accuracy even when using 99% inclusion threshold. However, this issue can even occur in low dimension data with simple correlation structure as our numerical example shows. We conclude that the exclusion of any principal components should be carefully investigated.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48310066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SASSCAL WebSAPI: A Web Scraping Application Programming Interface to Support Access to SASSCAL’s Weather Data","authors":"Tsaone Swaabow Thapelo, M. Namoshe, O. Matsebe, T. Motshegwa, Mary-Jane M. Bopape","doi":"10.5334/dsj-2021-024","DOIUrl":"https://doi.org/10.5334/dsj-2021-024","url":null,"abstract":"The Southern African Science Service Centre for Climate and Land Management (SASSCAL) was initiated to support regional weather monitoring and climate research in Southern Africa. As a result, several Automatic Weather Stations (AWSs) were implemented to provide numerical weather data within the collaborating countries. Meanwhile, access to the SASSCAL weather data is limited to a number of records that are achieved via a series of clicks. Currently, end users can not efficaciously extract the desired weather values. Thus, the data is not fully utilised by end users. This work contributes with an open source Web Scraping Application Programming Interface (WebSAPI) through an interactive dashboard. The objective is to extend functionalities of the SASSCAL Weathernet for: data extraction, statistical data analysis and visualisation. The SASSCAL WebSAPI was developed using the R statistical environment. It deploys web scraping and data wrangling techniques to support access to SASSCAL weather data. This WebSAPI reduces the risk of human error, and the researcher’s effort of generating desired data sets. The proposed framework for the SASSCAL WebSAPI can be modified for other weather data banks while taking into consideration the legality and ethics of the toolkit.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42327269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Capacity Development and Collaboration for Sustainable African Agriculture: Amplification of Impact Through Hackathons","authors":"K. Charvát, B. Bye, Hana Kubícková, Foteini Zampati, Tuula Löytty, Kizito Odhiambo, K. Kamau, S. Anand, P. Kasoma, Maximilien Houël, Elias Cherenet, Akaninyene Obot, Felix Kariuki, Antoine Kantiza, Ronald Ssembajwe, Samuel Njogo, W. Kamau","doi":"10.5334/dsj-2021-023","DOIUrl":"https://doi.org/10.5334/dsj-2021-023","url":null,"abstract":"The paper describes the concept of INSPIRE Kampala virtual hackathons, with the main focus to build and strengthen relationships between several European Union (EU) projects and African communities that started in 2019 with the Nairobi INSPIRE Hackathon. The main focus is exploring a new model for capacity building based on virtual hackathons as an excellent opportunity for bringing together people from different work environments, culture and disciplinary backgrounds. This paper is describing experience and lessons learned from the Kampala INSPIRE Hackathon. INSPIRE Hackathons have evolved over a five year period since it started and during this period we developed a model of fully virtual Hackathons, which we recognise as optimal for Africa. The paper describes all stages of Hackathon building: definition of themes, selection of mentors, development, webinars as tools for sharing experience, final presentation, selection of winners and awarding ceremony. As important we consider also planning other actions, because we don’t see INSPIRE Hackathon as an event, but as a continuous process. Demonstration part of paper describes the lessons learnt from the winning challenge: Desert Locus Monitoring. The description of all phases demonstrate Kampala INSPIRE Hackathon approach. On the basis of experience we defined strategy for the future, how to continue and successfully extend such a model in Africa.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47985350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SwissEnvEO: A FAIR National Environmental Data Repository for Earth Observation Open Science","authors":"G. Giuliani, Hugues Cazeaux, Pierre-Yves Burgi, Charlotte Poussin, Jean-Philippe Richard, B. Chatenoux","doi":"10.5334/DSJ-2021-022","DOIUrl":"https://doi.org/10.5334/DSJ-2021-022","url":null,"abstract":"Environmental scientific research is highly becoming data-driven and dependent on high performance computing infrastructures to process ever increasing large volume and diverse data sets. Consequently, there is a growing recognition of the need to share data, methods, algorithms, and infrastructure to make scientific research more effective, efficient, open, transparent, reproducible, accessible, and usable by different users. However, Earth Observations (EO) Open Science is still undervalued, and different challenges remains to achieve the vision of transforming EO data into actionable knowledge by lowering the entry barrier to massive-use Big Earth Data analysis and derived information products. Currently, FAIR-compliant digital repositories cannot fully satisfy the needs of EO users, while Spatial Data Infrastructures (SDI) are not fully FAIR-compliant and have difficulties in handling Big Earth Data. In response to these issues and the need to strengthen Open and Reproducible EO science, this paper presents SwissEnvEO, a Spatial Data Infrastructure complemented with digital repository capabilities to facilitate the publication of Ready to Use information products, at national scale, derived from satellite EO data available in an EO Data Cube in full compliance with FAIR principles.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42884058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Out of Cite, Out of Mind: The Current State of Practice, Policy, and Technology for the Citation of Data","authors":"M. Parsons","doi":"10.5334/DSJ-2021-021","DOIUrl":"https://doi.org/10.5334/DSJ-2021-021","url":null,"abstract":"","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46526260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Controlled Vocabulary and Metadata Schema for Materials Science Data Discovery","authors":"Andrea Medina-Smith, C. Becker, R. Plante, L. Bartolo, A. Dima, J. Warren, R. Hanisch","doi":"10.5334/DSJ-2021-018","DOIUrl":"https://doi.org/10.5334/DSJ-2021-018","url":null,"abstract":"The International Materials Resource Registries (IMRR) working group of the Research Data Alliance (RDA) was created to spur initial development of a federated registry system to allow for easier discovery and access to materials data. As part of this effort, a controlled vocabulary and metadata schema were developed with contributions from members of the working group and other experts. Here we describe the process, the resulting vocabulary and XML schema, and lessons learned in the development and use of the schema.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47855339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Discovery and Use of NASA’s Earth Observation Data Through Metadata Quality Assessments","authors":"K. Bugbee, J. le Roux, Adam W. Sisco, A. Kaulfus, Patrick Staton, Camille Woods, V. Dixon, C. Lynnes, R. Ramachandran","doi":"10.5334/DSJ-2021-017","DOIUrl":"https://doi.org/10.5334/DSJ-2021-017","url":null,"abstract":"High quality descriptive metadata is essential to enabling the effective discovery of Earth observation data to a growing number of diverse users. In this paper, we define a framework to assess the quality of NASA’s Earth observation metadata with the overarching goal of improving the discoverability, accessibility and usability of the data it describes. The framework, developed by the Analysis and Review of the Common Metadata Repository (ARC) team, focuses on the metadata quality dimensions of correctness, completeness, and consistency. The methodology used by the ARC team to implement the framework is described, as well as best practices, lessons learned and recommendations for implementing similar metadata quality assessment processes. Initial results from the project indicate that this methodology, in combination with community and stakeholder collaboration, is effective in improving metadata quality.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47738336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey of researchers' needs and priorities for data sharing","authors":"I. Hrynaszkiewicz, J. Harney, L. Cadwallader","doi":"10.31219/osf.io/njr5u","DOIUrl":"https://doi.org/10.31219/osf.io/njr5u","url":null,"abstract":"PLOS has long supported Open Science. One of the ways in which we do so is via our stringent data availability policy established in 2014. Despite this policy, and more data sharing policies being introduced by other organizations, best practices for data sharing are adopted by a minority of researchers in their publications. Problems with effective research data sharing persist and these problems have been quantified by previous research as a lack of time, resources, incentives, and/or skills to share data. In this study we built on this research by investigating the importance of tasks associated with data sharing, and researchers’ satisfaction with their ability to complete these tasks. By investigating these factors we aimed to better understand opportunities for new or improved solutions for sharing data. In May-June 2020 we surveyed researchers from Europe and North America to rate tasks associated with data sharing on (i) their importance and (ii) their satisfaction with their ability to complete them. We received 728 completed and 667 partial responses. We calculated mean importance and satisfaction scores to highlight potential opportunities for new solutions to and compare different cohorts.Tasks relating to research impact, funder compliance, and credit had the highest importance scores. 52% of respondents reuse research data but the average satisfaction score for obtaining data for reuse was relatively low. Tasks associated with sharing data were rated somewhat important and respondents were reasonably well satisfied in their ability to accomplish them. Notably, this included tasks associated with best data sharing practice, such as use of data repositories. However, the most common method for sharing data was in fact via supplemental files with articles, which is not considered to be best practice.We presume that researchers are unlikely to seek new solutions to a problem or task that they are satisfied in their ability to accomplish, even if many do not attempt this task. This implies there are few opportunities for new solutions or tools to meet these researcher needs. Publishers can likely meet these needs for data sharing by working to seamlessly integrate existing solutions that reduce the effort or behaviour change involved in some tasks, and focusing on advocacy and education around the benefits of sharing data. There may however be opportunities - unmet researcher needs - in relation to better supporting data reuse, which could be met in part by strengthening data sharing policies of journals and publishers, and improving the discoverability of data associated with published articles.","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45088251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementing a Registry Federation for Materials Science Data Discovery.","authors":"Raymond L Plante, Chandler A Becker, Andrea Medina-Smith, Kevin Brady, Alden Dima, Benjamin Long, Laura M Bartolo, James A Warren, Robert J Hanisch","doi":"10.5334/dsj-2021-015","DOIUrl":"10.5334/dsj-2021-015","url":null,"abstract":"<p><p>As a result of a number of national initiatives, we are seeing rapid growth in the data important to materials science that are available over the web. Consequently, it is becoming increasingly difficult for researchers to learn what data are available and how to access them. To address this problem, the Research Data Alliance (RDA) Working Group for International Materials Science Registries (IMRR) was established to bring together materials science and information technology experts to develop an international federation of registries that can be used for global discovery of data resources for materials science. A resource registry collects high-level metadata descriptions of resources such as data repositories, archives, websites, and services that are useful for data-driven research. By making the collection searchable, it aids scientists in industry, universities, and government laboratories to discover data relevant to their research and work interests. We present the results of our successful piloting of a registry federation for materials science data discovery. In particular, we out a blueprint for creating such a federation that is capable of amassing a global view of all available materials science data, and we enumerate the requirements for the standards that make the registries interoperable within the federation. These standards include a protocol for exchanging resource descriptions and a standard metadata schema for encoding those descriptions. We summarize how we leveraged an existing standard (OAI-PMH) for metadata exchange. Finally, we review the registry software developed to realize the federation and describe the user experience.</p>","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":"20 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8596377/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39747338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Public Health Benefits and Ethical Aspects in the Collection and Open Sharing of Wastewater-Based Epidemic Data on COVID-19","authors":"R. Honda, M. Murakami, A. Hata, M. Ihara","doi":"10.5334/dsj-2021-027","DOIUrl":"https://doi.org/10.5334/dsj-2021-027","url":null,"abstract":"Collection and open sharing of wastewater-based epidemic data potentially provide immense public health benefits during outbreak of infectious diseases such as COVID-19. By early detection and localization of unidentified infections, wastewater surveillance is expected to enable early and targeted containment of the local outbreak. Wastewater surveillance renders potentially high public health benefits when a small catchment is targeted;however, it possibly leads to stigmatization and discrimination against the targeted group. Therefore, public commitment is crucial for the collection and open sharing of wastewater-based epidemic data. With respect to the sharing of wastewater-based epidemic data, technical limitations and uncertainty of collected data also should be simultaneously shared on the basis of scientific communication. Useful application of wastewater-based epidemic data is to complement clinical epidemic data, which is possibly biased and overlooks unidentified infections. To acquire public commitment toward the collection and open sharing of wastewater-based epidemic data, stakeholders need to reach a consensus on possible options of restrictive measures taken with respect to the collected data as well as appropriate handling of the collected data to prevent stigmatization and discrimination. © 2021 The Author(s).","PeriodicalId":35375,"journal":{"name":"Data Science Journal","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71068218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}