Difference between revisions of "Main Page/Featured article of the week/2019"
Shawndouglas (talk | contribs) (Added last week's article of the week) |
Shawndouglas (talk | contribs) (Added last week's article of the week) |
||
(23 intermediate revisions by the same user not shown) | |||
Line 17: | Line 17: | ||
<!-- Below this line begin pasting previous news --> | <!-- Below this line begin pasting previous news --> | ||
|<h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: January 28-February 3:</h2> | <h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: July 08–14:</h2> | ||
'''"[[Journal:Smart information systems in cybersecurity: An ethical analysis|Smart information systems in cybersecurity: An ethical analysis]]"''' | |||
This report provides an overview of the current implementation of smart information systems (SIS) in the field of [[cybersecurity]]. It also identifies the positive and negative aspects of using SIS in cybersecurity, including ethical issues which could arise while using SIS in this area. One company working in the industry of telecommunications (Company A) is analysed in this report. Further specific ethical issues that arise when using SIS technologies in Company A are critically evaluated. Finally, conclusions are drawn on the case study, and areas for improvement are suggested. Increasing numbers of items are becoming connected to the internet. Cisco—a global leader in information technology, networking, and [[cybersecurity]]—estimates that more than 8.7 billion devices were connected to the internet by the end of 2012, a number that will likely rise to over 40 billion in 2020. ('''[[Journal:Smart information systems in cybersecurity: An ethical analysis|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: July 01–07:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Mudge ScientificReports2018 8.png|240px]]</div> | |||
'''"[[Journal:Chemometric analysis of cannabinoids: Chemotaxonomy and domestication syndrome|Chemometric analysis of cannabinoids: Chemotaxonomy and domestication syndrome]]"''' | |||
''[[wikipedia:Cannabis|Cannabis]]'' is an interesting domesticated crop with a long history of cultivation and use. [[wikipedia:Cannabis strains|Strains]] have been selected through informal breeding programs with undisclosed parentage and criteria. The term “strain” refers to minor morphological differences and grower branding rather than distinct cultivated varieties. We hypothesized that strains sold by different licensed producers are chemotaxonomically indistinguishable and that the commercial practice of identifying strains by the ratio of total Δ9-[[wikipedia:Tetrahydrocannabinol|tetrahydrocannabinol]] (THC) and [[wikipedia:Cannabidiol|cannabidiol]] (CBD) is insufficient to account for the reported human health outcomes. We used targeted [[wikipedia:Metabolomics|metabolomics]] to analyze 11 known [[wikipedia:Cannabinoid|cannabinoid]]s and an untargeted metabolomics approach to identify 21 unknown cannabinoids. Five clusters of chemotaxonomically indistinguishable strains were identified from the 33 commercial products. Only three of the clusters produce cannabidiolic acid (CBDA) in significant quantities, while the other two clusters redirect metabolic resources toward the [[wikipedia:Tetrahydrocannabinolic acid|tetrahydrocannabinolic acid]] (THCA) production pathways. ('''[[Journal:Chemometric analysis of cannabinoids: Chemotaxonomy and domestication syndrome|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: June 24–30:</h2> | |||
'''"[[Journal:National and transnational security implications of asymmetric access to and use of biological data|National and transnational security implications of asymmetric access to and use of biological data]]"''' | |||
Biology and [[biotechnology]] have changed dramatically during the past 20 years, in part because of increases in computational capabilities and use of engineering principles to study biology. The advances in supercomputing, data storage capacity, and [[Cloud computing|cloud platforms]] enable scientists throughout the world to generate, analyze, share, and store vast amounts of data, some of which are biological and much of which may be used to understand the human condition, agricultural systems, evolution, and environmental ecosystems. These advances and applications have enabled: (1) the emergence of data science, which involves the development of new algorithms to analyze and [[Data visualization|visualize data]]; and (2) the use of engineering approaches to manipulate or create new biological organisms that have specific functions, such as production of industrial chemical precursors and development of environmental bio-based sensors. Several biological sciences fields harness the capabilities of computer, data, and engineering sciences, including synthetic biology, precision medicine, precision agriculture, and systems biology. These advances and applications are not limited to one country. This capability has economic and physical consequences but is vulnerable to unauthorized intervention. ('''[[Journal:National and transnational security implications of asymmetric access to and use of biological data|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: June 17–23:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab4 Wholey FrontPubHealth2019 6.jpg|240px]]</div> | |||
'''"[[Journal:Developing workforce capacity in public health informatics: Core competencies and curriculum design|Developing workforce capacity in public health informatics: Core competencies and curriculum design]]"''' | |||
We describe a master’s level [[public health informatics]] (PHI) curriculum to support workforce development. Public health decision-making requires intensive [[information management]] to organize responses to health threats and develop effective health education and promotion. PHI competencies prepare the public health workforce to design and implement these information systems. The objective for a master's and certificate in PHI is to prepare public health informaticians with the competencies to work collaboratively with colleagues in public health and other health professions to design and develop information systems that support population health improvement. The PHI competencies are drawn from computer, information, and organizational sciences. A curriculum is proposed to deliver the competencies, and the results of a pilot PHI program are presented. Since the public health workforce needs to use information technology effectively to improve population health, it is essential for public health academic institutions to develop and implement PHI workforce training programs. ('''[[Journal:Developing workforce capacity in public health informatics: Core competencies and curriculum design|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: June 10–16:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Schabacker FrontBioengBiotech2019 7.jpg|240px]]</div> | |||
'''"[[Journal:Assessing cyberbiosecurity vulnerabilities and infrastructure resilience|Assessing cyberbiosecurity vulnerabilities and infrastructure resilience]]"''' | |||
The convergence of advances in [[biotechnology]] with [[laboratory automation]], access to data, and computational biology has democratized biotechnology and accelerated the development of new therapeutics. However, increased access to biotechnology in the digital age has also introduced additional security concerns and ultimately spawned the new discipline of cyberbiosecurity, which encompasses [[cybersecurity]], cyber-physical security, and biosecurity considerations. With the emergence of this new discipline comes the need for a logical, repeatable, and shared approach for evaluating facility and system vulnerabilities to cyberbiosecurity threats. In this paper, we outline the foundation of an assessment framework for cyberbiosecurity, accounting for both security and resilience factors in the physical and cyber domains. This is a unique problem set, yet despite the complexity of the cyberbiosecurity field in terms of operations and governance, previous experience developing and implementing physical and cyber assessments applicable to a wide spectrum of critical infrastructure sectors provides a validated point of departure for a cyberbiosecurity assessment framework. ('''[[Journal:Assessing cyberbiosecurity vulnerabilities and infrastructure resilience|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: June 3–9:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab2 Haugsbakken NordicJOfSciTechStud2018 6-1.png|240px]]</div> | |||
'''"[[Journal:What is the meaning of sharing: Informing, being informed or information overload?|What is the meaning of sharing: Informing, being informed or information overload?]]"''' | |||
In recent years, several Norwegian public organizations have introduced enterprise social media platforms (ESMPs). The rationale for their implementation pertains to a goal of improving internal communications and work processes in organizational life. Such objectives can be attained on the condition that employees adopt the platform and embrace the practice of sharing. Although sharing work on ESMPs can bring benefits, making sense of the practice of sharing constitutes a challenge. In this regard, the paper performs an analysis on a case whereby an ESMP was introduced in a Norwegian public organization. The analytical focus is on the challenges and experiences of making sense of the practice of sharing. The research results show that users faced challenges in making sense of sharing. ('''[[Journal:What is the meaning of sharing: Informing, being informed or information overload?|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: May 27–June 2:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Murch FrontBioengBiotech2019 6.jpg|240px]]</div> | |||
'''"[[Journal:Cyberbiosecurity: An emerging new discipline to help safeguard the bioeconomy|Cyberbiosecurity: An emerging new discipline to help safeguard the bioeconomy]]"''' | |||
Cyberbiosecurity is being proposed as a formal new enterprise which encompasses cybersecurity, cyber-physical security, and biosecurity as applied to biological and biomedical-based systems. In recent years, an array of important meetings and public discussions, commentaries, and publications have occurred that highlight numerous vulnerabilities. While necessary first steps, they do not provide a systematized structure for effectively promoting communication, education and training, elucidation, and prioritization for analysis, research, development, testing and evaluation, and implementation of scientific and technological standards of practice, policy, or regulatory or legal considerations for protecting the bioeconomy. Further, experts in biosecurity and cybersecurity are generally not aware of each other's domains, expertise, perspectives, priorities, or where mutually supported opportunities exist for which positive outcomes could result. ('''[[Journal:Cyberbiosecurity: An emerging new discipline to help safeguard the bioeconomy|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: May 20–26:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Duncan FrontBioengBiotech2019 7.jpg|240px]]</div> | |||
'''"[[Journal:Cyberbiosecurity: A new perspective on protecting U.S. food and agricultural system|Cyberbiosecurity: A new perspective on protecting U.S. food and agricultural system]]"''' | |||
Our national data and infrastructure security issues affecting the “bioeconomy” are evolving rapidly. Simultaneously, the conversation about cybersecurity of the U.S. [[Agriculture industry|food and agricultural system]] (cyber biosecurity) is incomplete and disjointed. The food and agricultural production sectors influence over 20% of the nation's economy ($6.7T) and 15% of U.S. employment (43.3M jobs). The food and agricultural sectors are immensely diverse, and they require advanced technologies and efficiencies that rely on computer technologies, big data, [[Cloud computing|cloud-based]] data storage, and internet accessibility. There is a critical need to safeguard the cyber biosecurity of our bioeconomy, but currently protections are minimal and do not broadly exist across the food and agricultural system. Using the food safety management Hazard Analysis Critical Control Point (HACCP) system concept as an introductory point of reference, we identify important features in broad food and agricultural production and food systems: dairy, food animals, row crops, fruits and vegetables, and environmental resources (water). ('''[[Journal:Cyberbiosecurity: A new perspective on protecting U.S. food and agricultural system|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: May 13–19:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Perez-Castillo Sensors2018 18-9.png|240px]]</div> | |||
'''"[[Journal:DAQUA-MASS: An ISO 8000-61-based data quality management methodology for sensor data|DAQUA-MASS: An ISO 8000-61-based data quality management methodology for sensor data]]"''' | |||
The [[internet of things]] (IoT) introduces several technical and managerial challenges when it comes to the use of data generated and exchanged by and between various smart, connected products (SCPs) that are part of an IoT system (i.e., physical, intelligent devices with sensors and actuators). Added to the volume and the heterogeneous exchange and consumption of data, it is paramount to [[Quality assurance|assure]] that data quality levels are maintained in every step of the data chain/lifecycle. Otherwise, the system may fail to meet its expected function. While data quality (DQ) is a mature field, existing solutions are highly heterogeneous. Therefore, we propose that companies, developers, and vendors should align their data quality management mechanisms and artifacts with well-known best practices and [[Specification (technical standard)|standards]], as for example, those provided by ISO 8000-61. This standard enables a process-approach to data quality management, overcoming the difficulties of isolated data quality activities. This paper introduces DAQUA-MASS, a methodology based on ISO 8000-61 for data quality management in sensor networks. ('''[[Journal:DAQUA-MASS: An ISO 8000-61-based data quality management methodology for sensor data|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: May 06–12:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig17 Pinheiro Sensors2018 18-3.png|240px]]</div> | |||
'''"[[Journal:Security architecture and protocol for trust verifications regarding the integrity of files stored in cloud services|Security architecture and protocol for trust verifications regarding the integrity of files stored in cloud services]]"''' | |||
[[Cloud computing]] is considered an interesting paradigm due to its scalability, availability, and virtually unlimited storage capacity. However, it is challenging to organize a cloud storage service (CSS) that is safe from the client point-of-view and to implement this CSS in public clouds since it is not advisable to blindly consider this configuration as fully trustworthy. Ideally, owners of large amounts of data should trust their data to be in the cloud for a long period of time, without the burden of keeping copies of the original data, nor of accessing the whole content for verification regarding data preservation. Due to these requirements, [[Data integrity|integrity]], availability, [[Information privacy|privacy]], and trust are still challenging issues for the adoption of cloud storage services, especially when losing or leaking [[information]] can bring significant damage, be it legal or business-related. With such concerns in mind, this paper proposes an architecture for periodically monitoring both the information stored in the cloud and the service provider behavior. ('''[[Journal:Security architecture and protocol for trust verifications regarding the integrity of files stored in cloud services|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: April 29–May 05:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab2 Al-Jefri FrontInMedicine2018 5.jpg|240px]]</div> | |||
'''"[[Journal:What Is health information quality? Ethical dimension and perception by users|What Is health information quality? Ethical dimension and perception by users]]"''' | |||
The popularity of seeking health [[information]] online makes information quality (IQ) a public health issue. The present study aims at building a theoretical framework of health information quality (HIQ) that can be applied to websites and defines which IQ criteria are important for a website to be trustworthy and meet users' expectations. We have identified a list of HIQ criteria from existing tools and assessment criteria and elaborated them into a questionnaire that was promoted via social media and, mainly, the university. Responses (329) were used to rank the different criteria for their importance in trusting a website and to identify patterns of criteria using hierarchical cluster analysis. HIQ criteria were organized in five dimensions based on previous theoretical frameworks, as well as on how they cluster together in the questionnaire response. We could identify a top-ranking dimension (scientific completeness) that describes what the user is expecting to know from the websites (in particular: description of symptoms, treatments, side effects). ('''[[Journal:What Is health information quality? Ethical dimension and perception by users|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: April 22–28:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Teixeira FutureInternet2018 10-8.png|240px]]</div> | |||
'''"[[Journal:SCADA system testbed for cybersecurity research using machine learning approach|SCADA system testbed for cybersecurity research using machine learning approach]]"''' | |||
This paper presents the development of a [[supervisory control and data acquisition]] (SCADA) system testbed used for [[cybersecurity]] research. The testbed consists of a water storage tank’s control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naïve Bayes, and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environments. ('''[[Journal:Semantics for an integrative and immersive pipeline combining visualization and analysis of molecular data|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: April 15–21:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Trellet JOfIntegBioinfo2018 15-2.jpg|240px]]</div> | |||
'''"[[Journal:Semantics for an integrative and immersive pipeline combining visualization and analysis of molecular data|Semantics for an integrative and immersive pipeline combining visualization and analysis of molecular data]]"''' | |||
The advances made in recent years in the field of structural biology significantly increased the throughput and complexity of data that scientists have to deal with. Combining and [[Data analysis|analyzing]] such heterogeneous amounts of data became a crucial time consumer in the daily tasks of scientists. However, only few efforts have been made to offer scientists an alternative to the standard compartmentalized tools they use to explore their data and that involve a regular back and forth between them. We propose here an integrated pipeline especially designed for immersive environments, promoting direct interactions on semantically linked 2D and 3D heterogeneous data, displayed in a common working space. The creation of a semantic definition describing the content and the context of a molecular scene leads to the creation of an intelligent system where data are (1) combined through pre-existing or inferred links present in our hierarchical definition of the concepts, (2) enriched with suitable and adaptive analyses proposed to the user with respect to the current task and (3) interactively presented in a unique working environment to be explored. ('''[[Journal:Semantics for an integrative and immersive pipeline combining visualization and analysis of molecular data|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: April 8–14:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Talia JOfCloudComp2019 8.png|240px]]</div> | |||
'''"[[Journal:A view of programming scalable data analysis: From clouds to exascale|A view of programming scalable data analysis: From clouds to exascale]]"''' | |||
Scalability is a key feature for big data analysis and machine learning frameworks and for applications that need to analyze very large and real-time data available from data repositories, social media, sensor networks, smartphones, and the internet. Scalable big data analysis today can be achieved by parallel implementations that are able to exploit the computing and storage facilities of high-performance computing (HPC) systems and [[cloud computing]] systems, whereas in the near future exascale systems will be used to implement extreme-scale [[data analysis]]. Here is discussed how cloud computing currently supports the development of scalable data mining solutions and what the main challenges to be addressed and solved for implementing innovative data analysis applications on exascale systems currently are. ('''[[Journal:A view of programming scalable data analysis: From clouds to exascale|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: April 1–7:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Swaminathan FrontInGenetics2018 9.jpg|240px]]</div> | |||
'''"[[Journal:Transferring exome sequencing data from clinical laboratories to healthcare providers: Lessons learned at a pediatric hospital|Transferring exome sequencing data from clinical laboratories to healthcare providers: Lessons learned at a pediatric hospital]]"''' | |||
The adoption rate of [[Genomics|genome sequencing]] for clinical diagnostics has been steadily increasing, leading to the possibility of improvement in diagnostic yields. Although [[Laboratory|laboratories]] generate a summary clinical report, sharing raw genomic data with healthcare providers is equally important, both for secondary research studies as well as for a deeper analysis of the data itself, as seen by the efforts from organizations such as American College of Medical Genetics and Genomics, as well as Global Alliance for Genomics and Health. Here, we aim to describe the existing protocol of genomic data sharing between a certified [[clinical laboratory]] and a healthcare provider and highlight some of the lessons learned. This study tracked and subsequently evaluated the data transfer workflow for 19 patients, all of whom consented to be part of this research study and visited the genetics clinic at a tertiary pediatric hospital between April 2016 and December 2016. ('''[[Journal:Transferring exome sequencing data from clinical laboratories to healthcare providers: Lessons learned at a pediatric hospital|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: March 25–31:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig4 Yu JOnWireCommNet2019 2019.png|240px]]</div> | |||
'''"[[Journal:Research on information retrieval model based on ontology|Research on information retrieval model based on ontology]]"''' | |||
An information retrieval system not only occupies an important position in the network information platform, but also plays an important role in [[information]] acquisition, query processing, and wireless sensor networks. It is a procedure to help researchers extract documents from data sets as document retrieval tools. The classic keyword-based information retrieval models neglect the semantic information which is not able to represent the user’s needs. Therefore, how to efficiently acquire personalized information that users need is of concern. The ontology-based systems lack an expert list to obtain accurate index term frequency. In this paper, a domain ontology model with document processing and document retrieval is proposed, and the feasibility and superiority of the domain ontology model are proved by the method of experiment. ('''[[Journal:Research on information retrieval model based on ontology|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: March 18–24:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig9 Pathinarupothi BMCMedInfoDecMak2018 18.png|240px]]</div> | |||
'''"[[Journal:Data to diagnosis in global health: A 3P approach|Data to diagnosis in global health: A 3P approach]]"''' | |||
With connected medical devices fast becoming ubiquitous in healthcare monitoring, there is a deluge of data coming from multiple body-attached sensors. Transforming this flood of data into effective and efficient diagnosis is a major challenge. To address this challenge, we present a "3P" approach: personalized patient monitoring, precision diagnostics, and preventive criticality alerts. In a collaborative work with doctors, we present the design, development, and testing of a healthcare data analytics and communication framework that we call RASPRO (Rapid Active Summarization for effective PROgnosis). The heart of RASPRO is "physician assist filters" (PAF) that 1. transform unwieldy multi-sensor time series data into summarized patient/disease-specific trends in steps of progressive precision as demanded by the doctor for a patient’s personalized condition, and 2. help in identifying and subsequently predictively alerting the onset of critical conditions. ('''[[Journal:Data to diagnosis in global health: A 3P approach|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: March 11–17:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Pluscauskas IntJOfNeoScreen2019 5-1.png|240px]]</div> | |||
'''"[[Journal:Building a newborn screening information management system from theory to practice|Building a newborn screening information management system from theory to practice]]"''' | |||
Information management systems are the central process management and communication hub for many newborn screening programs. In late 2014, Newborn Screening Ontario (NSO) undertook an end-to-end assessment of its [[information management]] needs, which resulted in a project to develop a flexible information systems (IS) ecosystem and related process changes. This enabled NSO to better manage its current and future [[workflow]] and communication needs. An idealized vision of a screening information management system (SIMS) was developed that was refined into enterprise and functional architectures. This was followed by the development of technical specifications, user requirements, and procurement. In undertaking a holistic full product lifecycle redesign approach, a number of change management challenges were faced by NSO across the entire program. Strong leadership support and full program engagement were key for overall project success. It is anticipated that improvements in program flexibility and the ability to innovate will outweigh the efforts and costs. ('''[[Journal:Building a newborn screening information management system from theory to practice|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: March 04–10:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Read JMedLibAssoc2019 107-1.gif|240px]]</div> | |||
'''"[[Journal:Adapting data management education to support clinical research projects in an academic medical center|Adapting data management education to support clinical research projects in an academic medical center]]"''' | |||
Librarians and researchers alike have long identified research [[Information management|data management]] (RDM) training as a need in biomedical [[research]]. Despite the wealth of libraries offering RDM education to their communities, clinical research is an area that has not been targeted. Clinical RDM (CRDM) is seen by its community as an essential part of the research process where established guidelines exist, yet educational initiatives in this area are unknown. | |||
Leveraging my academic library’s experience supporting CRDM through informationist grants and REDCap training in our medical center, I developed a 1.5 hour CRDM workshop. This workshop was designed to use established CRDM guidelines in clinical research and address common questions asked by our community through the library’s existing data support program. The workshop was offered to the entire medical center four times between November 2017 and July 2018. This case study describes the development, implementation, and evaluation of this workshop. ('''[[Journal:Development of an electronic information system for the management of laboratory data of tuberculosis and atypical mycobacteria at the Pasteur Institute in Côte d’Ivoire|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: February 25–March 03:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Koné JofHlthManInfo2019 6-1.png|240px]]</div> | |||
'''"[[Journal:Development of an electronic information system for the management of laboratory data of tuberculosis and atypical mycobacteria at the Pasteur Institute in Côte d’Ivoire|Development of an electronic information system for the management of laboratory data of tuberculosis and atypical mycobacteria at the Pasteur Institute in Côte d’Ivoire]]"''' | |||
Tuberculosis remains a public health problem despite all the efforts made to eradicate it. To strengthen the surveillance system for this condition, it is necessary to have a good data management system. Indeed, the use of electronic information systems in [[Information management|data management]] can improve the quality of data. The objective of this project was to set up a laboratory-specific electronic information system for mycobacteria and atypical tuberculosis. | |||
The design of this [[laboratory information system]] required a general understanding of the workflow and the implementation processes in order to generate a realistic model. For the implementation of the system, Java technology was used to develop a web application compatible with the intranet of the company. ('''[[Journal:Development of an electronic information system for the management of laboratory data of tuberculosis and atypical mycobacteria at the Pasteur Institute in Côte d’Ivoire|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: February 18–24:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Green PubHlthRsPract2018 28-3.jpg|240px]]</div> | |||
'''"[[Journal:Codesign of the Population Health Information Management System to measure reach and practice change of childhood obesity programs|Codesign of the Population Health Information Management System to measure reach and practice change of childhood obesity programs]]"''' | |||
Childhood obesity prevalence is an issue of international public health concern, and governments have a significant role to play in its reduction. The Healthy Children Initiative (HCI) has been delivered in New South Wales (NSW), Australia, since 2011 to support implementation of childhood obesity prevention programs at scale. Consequently, a system to support local implementation and data collection, analysis, and reporting at local and state levels was necessary. The Population Health Information Management System (PHIMS) was developed to meet this need. | |||
A collaborative and iterative process was applied to the design and development of the system. The process comprised identifying technical requirements, building system infrastructure, delivering training, deploying the system, and implementing quality measures. ('''[[Journal:Codesign of the Population Health Information Management System to measure reach and practice change of childhood obesity programs|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: February 11–17:</h2> | |||
'''"[[Journal:Open data in scientific communication|Open data in scientific communication]]"''' | |||
The development of information technology makes it possible to collect and analyze a growing number of data resources. The results of research, regardless of the discipline, constitute one of the main sources of data. Currently, research results are increasingly being published in the open access model. The open access concept has been accepted and recommended worldwide by many institutions financing and implementing research. Initially, the idea of openness concerned only the results of research and scientific publications; at present, more attention is paid to the problem of sharing scientific data, including raw data. Proceedings towards open data are intricate, as data specificity requires the development of an appropriate legal, technical and organizational model, followed by the implementation of [[Information management|data management]] policies at both the institutional and national levels. ('''[[Journal:Open data in scientific communication|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: February 4–10:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Watson EnergyInfo2018 1-1.png|240px]]</div> | |||
'''"[[Journal:Simulation of greenhouse energy use: An application of energy informatics|Simulation of greenhouse energy use: An application of energy informatics]]"''' | |||
Greenhouse agriculture is a highly efficient method of food production that can greatly benefit from supplemental electric lighting. The needed electricity associated with greenhouse lighting amounts to about 30% of its operating costs. As the light level of LED lighting can be easily controlled, it offers the potential to reduce energy costs by precisely matching the amount of supplemental light provided to current weather conditions and a crop’s light needs. Three simulations of LED lighting for growing lettuce in the Southeast U.S. using historical solar radiation data for the area were conducted. Lighting costs can be potentially reduced by approximately 60%. ('''[[Journal:Simulation of greenhouse energy use: An application of energy informatics|Full article...]]''')<br /> | |||
|- | |||
|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: January 28-February 3:</h2> | |||
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Scott JofInnoHlthInfo2018 25-2.png|240px]]</div> | <div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Scott JofInnoHlthInfo2018 25-2.png|240px]]</div> | ||
'''"[[Journal:Learning health systems need to bridge the "two cultures" of clinical informatics and data science|Learning health systems need to bridge the "two cultures" of clinical informatics and data science]]"''' | '''"[[Journal:Learning health systems need to bridge the "two cultures" of clinical informatics and data science|Learning health systems need to bridge the "two cultures" of clinical informatics and data science]]"''' |
Revision as of 16:00, 15 July 2019
If you're looking for other "Article of the Week" archives: 2014 - 2015 - 2016 - 2017 - 2018 - 2019 |
Featured article of the week archive - 2019
Welcome to the LIMSwiki 2019 archive for the Featured Article of the Week.
Featured article of the week: July 08–14:"Smart information systems in cybersecurity: An ethical analysis" This report provides an overview of the current implementation of smart information systems (SIS) in the field of cybersecurity. It also identifies the positive and negative aspects of using SIS in cybersecurity, including ethical issues which could arise while using SIS in this area. One company working in the industry of telecommunications (Company A) is analysed in this report. Further specific ethical issues that arise when using SIS technologies in Company A are critically evaluated. Finally, conclusions are drawn on the case study, and areas for improvement are suggested. Increasing numbers of items are becoming connected to the internet. Cisco—a global leader in information technology, networking, and cybersecurity—estimates that more than 8.7 billion devices were connected to the internet by the end of 2012, a number that will likely rise to over 40 billion in 2020. (Full article...)
|