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Background Data papers have emerged as a powerful instrument for open data publishing, obtaining credit, and establishing priority for datasets generated in scientific experiments. Academic publishing improves data and metadata quality through peer review and increases the impact of datasets by enhancing their visibility, accessibility, and reusability. Objective We aimed to establish a new type of article structure and template for omics studies: the omics data paper. To improve data interoperability and further incentivize researchers to publish well-described datasets, we created a prototype workflow for streamlined import of genomics metadata from the European Nucleotide Archive directly into a data paper manuscript. Methods An omics data paper template was designed by defining key article sections that encourage the description of omics datasets and methodologies. A metadata import workflow, based on REpresentational State Transfer services and Xpath, was prototyped to extract information from the European Nucleotide Archive, ArrayExpress, and BioSamples databases. Findings The template and workflow for automatic import of standard-compliant metadata into an omics data paper manuscript provide a mechanism for enhancing existing metadata through publishing. Conclusion The omics data paper structure and workflow for import of genomics metadata will help to bring genomic and other omics datasets into the spotlight. Promoting enhanced metadata descriptions and enforcing manuscript peer review and data auditing of the underlying datasets brings additional quality to datasets. We hope that streamlined metadata reuse for scholarly publishing encourages authors to create enhanced metadata descriptions in the form of data papers to improve both the quality of their metadata and its findability and accessibility.

Contains fulltext : 237219.pdf (Publisher’s version ) (Open Access) Objective: To describe characteristics of randomised controlled trials (RCTs) conducted using electronic health records (EHRs), including completeness and transparency of reporting assessed against the 2021 CONSORT Extension for RCTs Conducted Using Cohorts and Routinely Collected Data (CONSORT-ROUTINE) criteria. Study design: MEDLINE and Cochrane Methodology Register were searched for a sample of RCTs published from 2011–2018. Completeness of reporting was assessed in a random sample using a pre-defined coding form. Results 183 RCT publications were identified; 122 (67%) used EHRs to identify eligible participants, 139 (76%) used the EHR as part of the intervention and 137 (75%) to ascertain outcomes. When 60 publications were evaluated against the CONSORT 2010 item and the corresponding extension for the 8 modified items, four items were 'adequately reported' for the majority of trials. Five new reporting items were identified for the CONSORT-ROUTINE extension; when evaluated, one was 'adequately reported', three were reported 'inadequately or not at all', the other 'partially'. There were, however, some encouraging signs with adequate and partial reporting of many important items, including descriptions of trial design, the consent process, outcome ascertainment and interpretation. Conclusion: Aspects of RCTs using EHRs are sub-optimally reported. Uptake of the CONSORT-ROUTINE Extension may improve reporting. 12 p.

With the publication of the Data Act proposal in February 2022, the European Commission approached an important milestone in the implementation of the data strategy it had announced two years earlier. The legislative proposal includes a package of measures that are supposed to make more IoT data available to data-driven enterprises. The legislation is expected to bring about more competition in the aftermarkets for IoT devices and related services, more value generation from such data and more techno-logical innovation enabled by access to data. The most innovative and far-reaching regulative instrument applied in this context is, without doubt, the mandatory access rights regime that would facilitate flow of data from private (mostly large) enterprises to other (mostly smaller) enterprises and to the public sector. This regime is accompanied by rules about the necessity and content of commercial contracts that define private entitlements concerning access to as well as use of co-generated IoT data, including statutory requirements concerning fairness, non-discrimination and compensation. This Position Paper primarily addresses the access rights regime and its accompanying rules focusing on contracts regarding access to data. It also briefly touches upon the provisions on data portability, rules for switching between providers and trade secrets. We conclude that the consolidated impact of the access rights regime on IoT device manufacturers, third parties and the data economy at large is hard to predict. At the same time, we argue that the legal positions and entitlements the Data Act would create require further scrutiny and that there is certainly room for clarifications and im-provements in the legislative proposal. The analysis concludes with several specific recommendations.

Recent years have seen a massive growth in ethical and legal frameworks to govern data science practices. Yet one of the core questions associated with ethical and legal frameworks is the extent to which they are implemented in practice. A particularly interesting case in this context comes to public officials, for whom higher standards typically exist. We are thus trying to understand how ethical and legal frameworks influence the everyday practices on data and algorithms of public sector data professionals. The following paper looks at two cases: public sector data professionals (1) at municipalities in the Netherlands and (2) at the Netherlands Police. We compare these two cases based on an analytical research framework we develop in this article to help understanding of everyday professional practices. We conclude that there is a wide gap between legal and ethical governance rules and the everyday practices.

“Setiap orang berhak atas perlindungan diri pribadi, keluarga, kehormatan, martabat, dan harta benda yang di bawah kekuasaannya, serta berhak atas rasa aman dan perlindungan dari ancaman ketakutan untuk berbuat atau tidak berbuat sesuatu yang merupakan hak asasi”. Inilah rumusan dari Pasal 28 G Ayat (1) dari Undang-Undang Dasar, yang dinyatakan Perubahannya yang Kedua dalam suatu Ketetapan Majelis Permusyawaratan Rakyat Republik Indonesia. Dalam rumusan ketentuan ini orang dapat menyuling semua asas yang mengatur perlindungan data dalam Sistem Hukum Pancasila.Di samping rumusan ketentuan di atas, sejumlah rumusan kaedah hukum lainnya berisi asas-asas yang sama dapat juga ditemukan dalam beberapa Undang-Undang yang berlaku dalam sistem hukum Indonesia. Antara lain, UU No. 8 tahun 2011 tentang ITE. Penulis artikel ini berpendapat bahwa seluruh rumusan ketentuan dimaksud adalah bentuk-bentuk perwujudan diri dari jiwa bangsa (Volksgeist) tempat orang dapat menemukan asas-asas dan kaidah yang mengatur perlindungan data dalam Sistem Hukum Pancasila. Satu dari asas hukum yang melindungi data pribadi dapat digunakan untuk memecahkan skandal terkini, yaitu Panamapapers.Abstract“Every person has the rights to get protection on his personal/privacy, family, honor, dignity and properties under his power, and has rights to feel secure and to get protection from fear of any threat in order to do or not to do something related to their fundamental rights. This is the formulation of the Article 28 G (1) of the Indonesian Basic Act, mentioned in its Second Amendment mentioned in the Decision of the People's Consultative Assembly of the Republic of Indonesia. In this stipulation one could distil all the legal principles governing data protection in the Pancasila Legal System. Apart from the stipulation mentioned above, some further formulation of legal rules contained similar principles may also be found in several Acts in the Indonesian legal system. Among those Acts, is the Act number 8 of 2011 concerning Information and Electronic Transaction. This writer argues that all of the stipulations are forms of manifestation of the spirit of the Indonesian people (Volksgeist) in which one could find rules and principles governing data protection in the Pancasila Legal System. One of the principles of Law that protect the personal data could be used in order to solve the rescent scandal called Panamapapers.

Aquest article és la presentació del projecte que analitza l'impacte ambiental del procés de digitalització de la Universitat Autònoma de Barcelona (UAB) des del 2017 fins l'any 2022. S'examinen diversos factors com el consum de paper a la universitat, el protagonisme dels sistemes informàtics i les xarxes dins i fora del campus, l'impacte del teletreball en les dinàmiques dels treballadors de la universitat o la mobilitat generada pel funcionament del centre. Tot això s'intenta analitzar tenint en compte les conseqüències de l'impacte de la COVID-19 i també un factor que ha afectat exclusivament a la universitat com és el hackeig produït l'octubre del 2021. Este artículo es la presentación del proyecto que analiza el impacto ambiental del proceso de digitalización de la Universidad Autónoma de Barcelona (UAB) des del 2017 hasta el año 2022. Se examinan diversos factores como el consumo de paper en la universitat, el protagonismo de los sistemas informáticos y las redes dentro y fuera del campus, el impacto del teletrabajo en las dinámicas de los trabajadores de la universidad o la movilidad generada por el funcionamientodel centro. Todo esto se intenta analizar teniendo en cuenta las consecuencias del impacto del COVID-19 y también un factor que ha afectado exclusivamente a la universidad como es el hackeo producido en octubre del 2021. This article is the presentation of the project that analyzes the environmental impact of the digitization process of the Autonomous University of Barcelona (UAB) from 2017 to 2022. It examines various factors such as the consumption of paper in the university, the role of computer systems and networks on and off campus, the impact of telework on the dynamics of university workers or the mobility generated by the operation of the center. All this is being analyzed in the light of the consequences of the impact of COVID-19 and also a factor that has affected the university exclusively, such as the hacking that took place in October 2021.

Background The scale and quality of the global scientific response to the COVID-19 pandemic have unquestionably saved lives. However, the COVID-19 pandemic has also triggered an unprecedented “infodemic”; the velocity and volume of data production have overwhelmed many key stakeholders such as clinicians and policy makers, as they have been unable to process structured and unstructured data for evidence-based decision making. Solutions that aim to alleviate this data synthesis–related challenge are unable to capture heterogeneous web data in real time for the production of concomitant answers and are not based on the high-quality information in responses to a free-text query. Objective The main objective of this project is to build a generic, real-time, continuously updating curation platform that can support the data synthesis and analysis of a scientific literature framework. Our secondary objective is to validate this platform and the curation methodology for COVID-19–related medical literature by expanding the COVID-19 Open Research Dataset via the addition of new, unstructured data. Methods To create an infrastructure that addresses our objectives, the PanSurg Collaborative at Imperial College London has developed a unique data pipeline based on a web crawler extraction methodology. This data pipeline uses a novel curation methodology that adopts a human-in-the-loop approach for the characterization of quality, relevance, and key evidence across a range of scientific literature sources. Results REDASA (Realtime Data Synthesis and Analysis) is now one of the world’s largest and most up-to-date sources of COVID-19–related evidence; it consists of 104,000 documents. By capturing curators’ critical appraisal methodologies through the discrete labeling and rating of information, REDASA rapidly developed a foundational, pooled, data science data set of over 1400 articles in under 2 weeks. These articles provide COVID-19–related information and represent around 10% of all papers about COVID-19. Conclusions This data set can act as ground truth for the future implementation of a live, automated systematic review. The three benefits of REDASA’s design are as follows: (1) it adopts a user-friendly, human-in-the-loop methodology by embedding an efficient, user-friendly curation platform into a natural language processing search engine; (2) it provides a curated data set in the JavaScript Object Notation format for experienced academic reviewers’ critical appraisal choices and decision-making methodologies; and (3) due to the wide scope and depth of its web crawling method, REDASA has already captured one of the world’s largest COVID-19–related data corpora for searches and curation.

Biodiversity informatics is a new and evolving field, requiring efforts to develop capacity and a curriculum for this field of science. The main objective was to summarise the level of activity and the efforts towards developing biodiversity informatics curricula, for work-based training and/or academic teaching at universities, taking place within the Global Biodiversity Information Facility (GBIF) countries and its associated network. A survey approach was used to identify existing capacities and resources within the network. Most of GBIF Nodes survey respondents (80%) are engaged in onsite training activities, with a focus on work-based professionals, mostly researchers, policy-makers and students. Training topics include data mobilisation, digitisation, management, publishing, analysis and use, to enable the accessibility of analogue and digital biological data that currently reside as scattered datasets. An initial assessment of academic teaching activities highlighted that countries in most regions, to varying degrees, were already engaged in the conceptualisation, development and/or implementation of formal academic programmes in biodiversity informatics, including programmes in Benin, Colombia, Costa Rica, Finland, France, India, Norway, South Africa, Sweden, Taiwan and Togo. Digital e-learning platforms were an important tool to help build capacity in many countries. In terms of the potential in the Nodes network, 60% expressed willingness to be recruited or commissioned for capacity enhancement purposes. Contributions and activities of various country nodes across the network have been highlighted and a working curriculum framework has been defined.

The application of computers in acquisition and logistics support is to major requirement of future weapons systems acquisitions. Although the design of the SEAWOLF preceded most new DOD sponsored requirements, the program incorporated many initiatives that will serve as prototypes for most acquisitions. It The SEAWOLF Program is employing computer technology to integrate the design, production and logistic the ship's life cycle. The transportabiiity of TI electronic data from the design phase ti to construction, and on to logistics dc is key to improving efficiency and a more closely linking designer, shipbuilder and maintainer. SFAWOLF is an important step in the overall effort to improve weapons system acquisition efficiency. Five Lessons learned by SEAWOLF will be valuable in preparing other acquisition programs to take advantage of the integration of computer data bases that can bring greater success in the execution of design, production mi and logistics support phases.

This article examines the relation between counting, counts and accountability. It does so by comparing the responses of the British government to deaths associated with Covid-19 in 2020 to its responses to deaths associated with the 2003 invasion of Iraq. Similarities and dissimilarities between the cases regarding what counted as data, what data were taken to count, what data counted for, and how data were counted provide the basis for considering how the bounds of democratic accountability are constituted. Based on these two cases, the article sets out the metaphors of leaks and cascades as ways of characterising the data practices whereby counts, counting and accountability get configured. By situating deaths associated with Covid-19 against previous experience with deaths from war, the article also proposes how claims to truth and ignorance might figure in any future official inquiry into the handling of the pandemic.

Background Empirically driven personalized diagnostic applications and treatment stratification is widely perceived as a major hallmark in psychiatry. However, databased personalized decision making requires standardized data acquisition and data access, which are currently absent in psychiatric clinical routine. Objective Here, we describe the informatics infrastructure implemented at the psychiatric Münster University Hospital, which allows standardized acquisition, transfer, storage, and export of clinical data for future real-time predictive modelling in psychiatric routine. Methods We designed and implemented a technical architecture that includes an extension of the electronic health record (EHR) via scalable standardized data collection and data transfer between EHRs and research databases, thus allowing the pooling of EHRs and research data in a unified database and technical solutions for the visual presentation of collected data and analyses results in the EHR. The Single-source Metadata ARchitecture Transformation (SMA:T) was used as the software architecture. SMA:T is an extension of the EHR system and uses module-driven engineering to generate standardized applications and interfaces. The operational data model was used as the standard. Standardized data were entered on iPads via the Mobile Patient Survey (MoPat) and the web application Mopat@home, and the standardized transmission, processing, display, and export of data were realized via SMA:T. Results The technical feasibility of the informatics infrastructure was demonstrated in the course of this study. We created 19 standardized documentation forms with 241 items. For 317 patients, 6451 instances were automatically transferred to the EHR system without errors. Moreover, 96,323 instances were automatically transferred from the EHR system to the research database for further analyses. Conclusions In this study, we present the successful implementation of the informatics infrastructure enabling standardized data acquisition and data access for future real-time predictive modelling in clinical routine in psychiatry. The technical solution presented here might guide similar initiatives at other sites and thus help to pave the way toward future application of predictive models in psychiatric clinical routine.

Background Customer churn is the rate at which customers stop doing business with an entity. In the field of digital health care, user churn prediction is important not only in terms of company revenue but also for improving the health of users. Churn prediction has been previously studied, but most studies applied time-invariant model structures and used structured data. However, additional unstructured data have become available; therefore, it has become essential to process daily time-series log data for churn predictions. Objective We aimed to apply a recurrent neural network structure to accept time-series patterns using lifelog data and text message data to predict the churn of digital health care users. Methods This study was based on the use data of a digital health care app that provides interactive messages with human coaches regarding food, exercise, and weight logs. Among the users in Korea who enrolled between January 1, 2017 and January 1, 2019, we defined churn users according to the following criteria: users who received a refund before the paid program ended and users who received a refund 7 days after the trial period. We used long short-term memory with a masking layer to receive sequence data with different lengths. We also performed topic modeling to vectorize text messages. To interpret the contributions of each variable to model predictions, we used integrated gradients, which is an attribution method. Results A total of 1868 eligible users were included in this study. The final performance of churn prediction was an F1 score of 0.89; that score decreased by 0.12 when the data of the final week were excluded (F1 score 0.77). Additionally, when text data were included, the mean predicted performance increased by approximately 0.085 at every time point. Steps per day had the largest contribution (0.1085). Among the topic variables, poor habits (eg, drinking alcohol, overeating, and late-night eating) showed the largest contribution (0.0875). Conclusions The model with a recurrent neural network architecture that used log data and message data demonstrated high performance for churn classification. Additionally, the analysis of the contribution of the variables is expected to help identify signs of user churn in advance and improve the adherence in digital health care.

Background COVID-19 is a scientifically and medically novel disease that is not fully understood because it has yet to be consistently and deeply studied. Among the gaps in research on the COVID-19 outbreak, there is a lack of sufficient infoveillance data. Objective The aim of this study was to increase understanding of public awareness of COVID-19 pandemic trends and uncover meaningful themes of concern posted by Twitter users in the English language during the pandemic. Methods Data mining was conducted on Twitter to collect a total of 107,990 tweets related to COVID-19 between December 13 and March 9, 2020. The analyses included frequency of keywords, sentiment analysis, and topic modeling to identify and explore discussion topics over time. A natural language processing approach and the latent Dirichlet allocation algorithm were used to identify the most common tweet topics as well as to categorize clusters and identify themes based on the keyword analysis. Results The results indicate three main aspects of public awareness and concern regarding the COVID-19 pandemic. First, the trend of the spread and symptoms of COVID-19 can be divided into three stages. Second, the results of the sentiment analysis showed that people have a negative outlook toward COVID-19. Third, based on topic modeling, the themes relating to COVID-19 and the outbreak were divided into three categories: the COVID-19 pandemic emergency, how to control COVID-19, and reports on COVID-19. Conclusions Sentiment analysis and topic modeling can produce useful information about the trends in the discussion of the COVID-19 pandemic on social media as well as alternative perspectives to investigate the COVID-19 crisis, which has created considerable public awareness. This study shows that Twitter is a good communication channel for understanding both public concern and public awareness about COVID-19. These findings can help health departments communicate information to alleviate specific public concerns about the disease.

As the COVID-19 outbreak is developing the two most frequently reported statistics seem to be the raw confirmed case and case fatalities counts. Focusing on Italy, one of the hardest hit countries, we look at how these two values could be put in perspective to reflect the dynamics of the virus spread. In particular, we find that merely considering the confirmed case counts would be very misleading. The number of daily tests grows, while the daily fraction of confirmed cases to total tests has a change point. It (depending on region) generally increases with strong fluctuations till (around, depending on region) 15th-22nd March and then decreases linearly after. Combined with the increasing trend of daily performed tests, the raw confirmed case counts are not representative of the situation and are confounded with the sampling effort. This we observe when regressing on time the logged fraction of positive tests and for comparison the logged raw confirmed count. Hence, calibrating model parameters for this virus's dynamics should not be done based only on confirmed case counts (without rescaling by the number of tests), but take also fatalities and hospitalization count under consideration as variables not prone to be distorted by testing efforts. Furthermore, reporting statistics on the national level does not say much about the dynamics of the disease, which are taking place at the regional level. These findings are based on the official data of total death counts up to 15th April 2020 released by ISTAT and up to 10th May 2020 for the number of cases. In this work we do not fit models but we rather investigate whether this task is possible at all. This work also informs about a new tool to collect and harmonize official statistics coming from different sources in the form of a package for the R statistical environment and presents the COVID-19 Data Hub., Comment: updated references

Ten years ago, in 2009, “e-Patient Dave” deBronkart delivered an influential keynote speech at the Medicine 2.0 conference in Toronto, organized by the Journal of Medical Internet Research’s (JMIR’s) editor-in-chief Gunther Eysenbach, who themed the conference around the topics of participation, openness, collaboration, apomediation, and social networking to improve health care for the 21st century—with patient participation being a major component. Many see this as a defining event within the participatory medicine movement, perhaps the beginning of a social movement, similar to the women’s rights movement, with the title of Dave’s keynote “Gimme my damn data” becoming a rallying cry and hashtag for patients demanding more access to their electronic health records. On the occasion of the 20th anniversary of JMIR (and 10 years after the keynote), we are celebrating the impact of the keynote for the participatory medicine movement and #gimmemydamndata (also #GMDD) by publishing the transcript of these initial conversations as a manifesto of patients’ rights to access their data and their right to save their lives.

Background Antimicrobial resistance (AMR) is an emerging public health crisis in Uganda. The World Health Organization (WHO) Global Action Plan recommends that countries should develop and implement National Action Plans for AMR. We describe the establishment of the national AMR program in Uganda and present the early microbial sensitivity results from the program. Objective The aim of this study is to describe a national surveillance program that was developed to perform the systematic and continuous collection, analysis, and interpretation of AMR data. Methods A systematic qualitative description of the process and progress made in the establishment of the national AMR program is provided, detailing the progress made from 2015 to 2020. This is followed by a report of the findings of the isolates that were collected from AMR surveillance sites. Identification and antimicrobial susceptibility testing (AST) of the bacterial isolates were performed using standard methods at both the surveillance sites and the reference laboratory. Results Remarkable progress has been achieved in the establishment of the national AMR program, which is guided by the WHO Global Laboratory AMR Surveillance System (GLASS) in Uganda. A functional national coordinating center for AMR has been established with a supporting designated reference laboratory. WHONET software for AMR data management has been installed in the surveillance sites and laboratory staff trained on data quality assurance. Uganda has progressively submitted data to the WHO GLASS reporting system. Of the 19,216 isolates from WHO GLASS priority specimens collected from October 2015 to June 2020, 22.95% (n=4411) had community-acquired infections, 9.46% (n=1818) had hospital-acquired infections, and 68.57% (n=12,987) had infections of unknown origin. The highest proportion of the specimens was blood (12,398/19,216, 64.52%), followed by urine (5278/19,216, 27.47%) and stool (1266/19,216, 6.59%), whereas the lowest proportion was urogenital swabs (274/19,216, 1.4%). The mean age was 19.1 (SD 19.8 years), whereas the median age was 13 years (IQR 28). Approximately 49.13% (9440/19,216) of the participants were female and 50.51% (9706/19,216) were male. Participants with community-acquired infections were older (mean age 28, SD 18.6 years; median age 26, IQR 20.5 years) than those with hospital-acquired infections (mean age 17.3, SD 20.9 years; median age 8, IQR 26 years). All gram-negative (Escherichia coli, Klebsiella pneumoniae, and Neisseria gonorrhoeae) and gram-positive (Staphylococcus aureus and Enterococcus sp) bacteria with AST showed resistance to each of the tested antibiotics. Conclusions Uganda is the first African country to implement a structured national AMR surveillance program in alignment with the WHO GLASS. The reported AST data indicate very high resistance to the recommended and prescribed antibiotics for treatment of infections. More effort is required regarding quality assurance of laboratory testing methodologies to ensure optimal adherence to WHO GLASS–recommended pathogen-antimicrobial combinations. The current AMR data will inform the development of treatment algorithms and clinical guidelines.

Background COVID-19 has threatened the health of tens of millions of people all over the world. Massive research efforts have been made in response to the COVID-19 pandemic. Utilization of clinical data can accelerate these research efforts to combat the pandemic since important characteristics of the patients are often found by examining the clinical data. Publicly accessible clinical data on COVID-19, however, remain limited despite the immediate need. Objective To provide shareable clinical data to catalyze COVID-19 research, we present Columbia Open Health Data for COVID-19 Research (COHD-COVID), a publicly accessible database providing clinical concept prevalence, clinical concept co-occurrence, and clinical symptom prevalence for hospitalized patients with COVID-19. COHD-COVID also provides data on hospitalized patients with influenza and general hospitalized patients as comparator cohorts. Methods The data used in COHD-COVID were obtained from NewYork-Presbyterian/Columbia University Irving Medical Center’s electronic health records database. Condition, drug, and procedure concepts were obtained from the visits of identified patients from the cohorts. Rare concepts were excluded, and the true concept counts were perturbed using Poisson randomization to protect patient privacy. Concept prevalence, concept prevalence ratio, concept co-occurrence, and symptom prevalence were calculated using the obtained concepts. Results Concept prevalence and concept prevalence ratio analyses showed the clinical characteristics of the COVID-19 cohorts, confirming the well-known characteristics of COVID-19 (eg, acute lower respiratory tract infection and cough). The concepts related to the well-known characteristics of COVID-19 recorded high prevalence and high prevalence ratio in the COVID-19 cohort compared to the hospitalized influenza cohort and general hospitalized cohort. Concept co-occurrence analyses showed potential associations between specific concepts. In case of acute lower respiratory tract infection in the COVID-19 cohort, a high co-occurrence ratio was obtained with COVID-19–related concepts and commonly used drugs (eg, disease due to coronavirus and acetaminophen). Symptom prevalence analysis indicated symptom-level characteristics of the cohorts and confirmed that well-known symptoms of COVID-19 (eg, fever, cough, and dyspnea) showed higher prevalence than the hospitalized influenza cohort and the general hospitalized cohort. Conclusions We present COHD-COVID, a publicly accessible database providing useful clinical data for hospitalized patients with COVID-19, hospitalized patients with influenza, and general hospitalized patients. We expect COHD-COVID to provide researchers and clinicians quantitative measures of COVID-19–related clinical features to better understand and combat the pandemic.

WOS: 000453160000066, PubMed: 26958601, A huge amount of energy is consumed during sugar production in the food industry. The large amount of steam used and the power of the turbine power plant are key factors. This makes energy and exergy analysis important in sugar factories. The data given in the following paper are related to input and output information of the paper entitled Energy-exergy analysis and optimisation of a model sugar factory in Turkey by Taner and Sivrioglu (2015)111. Factory total energy efficiency and exergy efficiency are found to be rier,T= 722% and riexT=37.4%, respectively, and according to these results, the total energy quality 0T=0.64. These results indicate higher efficiency than similar studies (Vuckovic et al., 2014; Pellegrini and Oliviera Junior, 2011; Deshmukh et al., 2013; Palacios-Bereche et al., 2015) [2-51. This study can be a model for these similar factories by Taner and Sivrioglu (2015) [1]. (C) 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/40).

Background Surgical audit is an essential aspect of modern reflective surgical practice and is key to improving surgical outcomes. The surgical logbook is an important method of data collection for both personal and unit audits; however, current electronic data collection tools, especially mobile apps, lack the minimum recommended data fields. Objective This feasibility study details the creation of a free, effective surgical logbook tool with the iOS Shortcuts app and investigates the time investment required to maintain a surgical logbook with this tool. In addition, we investigate the potential utility of the Shortcuts app in creating medical data collection tools. Methods Using the iOS Shortcuts app, we created a shortcut “Operation Note,” which collects surgical logbook data by using the minimum and extended audit data sets recommended by the Royal Australasian College of Surgeons. We practically assessed the feasibility of the tool, assessing the time requirement for entry, accuracy, and completeness of the entered data. Results The shortcut collected accurate and useful data for a surgical audit. Data entry took on average 65 seconds per case for the minimum data set, and 135 seconds per case for the extended data set, with a mean difference of 68 seconds (P Conclusions This feasibility study demonstrates the utility of the iOS Shortcuts app in the creation of a surgical logbook and the time-consuming nature of data collection for surgical audit. Our iOS Operation Note shortcut is a free, rapid, and customizable alternative to currently available logbook apps and offers surgical trainees and consultants a method for recording surgical operations, complications, and demographic data.

Background In clinical research, important variables may be collected from multiple data sources. Physical pooling of patient-level data from multiple sources often raises several challenges, including proper protection of patient privacy and proprietary interests. We previously developed an SAS-based package to perform distributed regression—a suite of privacy-protecting methods that perform multivariable-adjusted regression analysis using only summary-level information—with horizontally partitioned data, a setting where distinct cohorts of patients are available from different data sources. We integrated the package with PopMedNet, an open-source file transfer software, to facilitate secure file transfer between the analysis center and the data-contributing sites. The feasibility of using PopMedNet to facilitate distributed regression analysis (DRA) with vertically partitioned data, a setting where the data attributes from a cohort of patients are available from different data sources, was unknown. Objective The objective of the study was to describe the feasibility of using PopMedNet and enhancements to PopMedNet to facilitate automatable vertical DRA (vDRA) in real-world settings. Methods We gathered the statistical and informatic requirements of using PopMedNet to facilitate automatable vDRA. We enhanced PopMedNet based on these requirements to improve its technical capability to support vDRA. Results PopMedNet can enable automatable vDRA. We identified and implemented two enhancements to PopMedNet that improved its technical capability to perform automatable vDRA in real-world settings. The first was the ability to simultaneously upload and download multiple files, and the second was the ability to directly transfer summary-level information between the data-contributing sites without a third-party analysis center. Conclusions PopMedNet can be used to facilitate automatable vDRA to protect patient privacy and support clinical research in real-world settings.

While DNA's perpetual role in biology and life science is well documented, its burgeoning digital applications are beginning to garner significant interest. As the development of novel technologies requires continuous research, product development, startup creation, and financing, this work provides an overview of each respective area and highlights current trends, challenges, and opportunities. These are supported by numerous interviews with key opinion leaders from across academia, government agencies and the commercial sector, as well as investment data analysis. Our findings illustrate the societal and economic need for technological innovation and disruption in data storage, paving the way for nature's own time-tested, advantageous, and unrivaled solution. We anticipate a significant increase in available investment capital and continuous scientific progress, creating a ripe environment on which DNA data storage-enabling startups can capitalize to bring DNA data storage into daily life., Highlights • Overview on current DNA data storage technologies and commercialization hurdles • Insights from leading DNA data storage experts and investment financing data • DNA synthesis remains the biggest challenge in the industry • Archiving cold data is the low-hanging fruit in DNA data storage • Upwards trend in investment landscape suggests optimal startup fundraising period

Background AIDSVu is a public resource for visualizing HIV surveillance data and other population-based information relevant to HIV prevention, care, policy, and impact assessment. Objective The site, AIDSVu.org, aims to make data about the US HIV epidemic widely available, easily accessible, and locally relevant to inform public health decision making. Methods AIDSVu develops visualizations, maps, and downloadable datasets using results from HIV surveillance systems, other population-based sources of information (eg, US Census and national probability surveys), and other data developed specifically for display and dissemination through the website (eg, pre-exposure prophylaxis [PrEP] prescriptions). Other types of content are developed to translate surveillance data into summarized content for diverse audiences using infographic panels, interactive maps, local and state fact sheets, and narrative blog posts. Results Over 10 years, AIDSVu.org has used an expanded number of data sources and has progressively provided HIV surveillance and related data at finer geographic levels, with current data resources providing HIV prevalence data down to the census tract level in many of the largest US cities. Data are available at the county level in 48 US states and at the ZIP Code level in more than 50 US cities. In 2019, over 500,000 unique users consumed AIDSVu data and resources, and HIV-related data and insights were disseminated through nearly 4,000,000 social media posts. Since AIDSVu’s inception, at least 249 peer-reviewed publications have used AIDSVu data for analyses or referenced AIDSVu resources. Data uses have included targeting of HIV testing programs, identifying areas with inequitable PrEP uptake, including maps and data in academic and community grant applications, and strategically selecting locations for new HIV treatment and care facilities to serve high-need areas. Conclusions Surveillance data should be actively used to guide and evaluate public health programs; AIDSVu translates high-quality, population-based data about the US HIV epidemic and makes that information available in formats that are not consistently available in surveillance reports. Bringing public health surveillance data to an online resource is a democratization of data, and presenting information about the HIV epidemic in more visual formats allows diverse stakeholders to engage with, understand, and use these important public health data to inform public health decision making.

Motivation: Medical Subject Headings (MeSHs) are used by National Library of Medicine (NLM) to index almost all citations in MEDLINE, which greatly facilitates the applications of biomedical information retrieval and text mining. To reduce the time and financial cost of manual annotation, NLM has developed a software package, Medical Text Indexer (MTI), for assisting MeSH annotation, which uses k-nearest neighbors (KNN), pattern matching and indexing rules. Other types of information, such as prediction by MeSH classifiers (trained separately), can also be used for automatic MeSH annotation. However, existing methods cannot effectively integrate multiple evidence for MeSH annotation. Methods: We propose a novel framework, MeSHLabeler, to integrate multiple evidence for accurate MeSH annotation by using ‘learning to rank’. Evidence includes numerous predictions from MeSH classifiers, KNN, pattern matching, MTI and the correlation between different MeSH terms, etc. Each MeSH classifier is trained independently, and thus prediction scores from different classifiers are incomparable. To address this issue, we have developed an effective score normalization procedure to improve the prediction accuracy. Results: MeSHLabeler won the first place in Task 2A of 2014 BioASQ challenge, achieving the Micro F-measure of 0.6248 for 9,040 citations provided by the BioASQ challenge. Note that this accuracy is around 9.15% higher than 0.5724, obtained by MTI. Availability and implementation: The software is available upon request. Contact: zhusf@fudan.edu.cn

Summary: Diseases caused by zoonotic viruses (viruses transmittable between humans and animals) are a major threat to public health throughout the world. By studying virus migration and mutation patterns, the field of phylogeography provides a valuable tool for improving their surveillance. A key component in phylogeographic analysis of zoonotic viruses involves identifying the specific locations of relevant viral sequences. This is usually accomplished by querying public databases such as GenBank and examining the geospatial metadata in the record. When sufficient detail is not available, a logical next step is for the researcher to conduct a manual survey of the corresponding published articles. Motivation: In this article, we present a system for detection and disambiguation of locations (toponym resolution) in full-text articles to automate the retrieval of sufficient metadata. Our system has been tested on a manually annotated corpus of journal articles related to phylogeography using integrated heuristics for location disambiguation including a distance heuristic, a population heuristic and a novel heuristic utilizing knowledge obtained from GenBank metadata (i.e. a ‘metadata heuristic’). Results: For detecting and disambiguating locations, our system performed best using the metadata heuristic (0.54 Precision, 0.89 Recall and 0.68 F-score). Precision reaches 0.88 when examining only the disambiguation of location names. Our error analysis showed that a noticeable increase in the accuracy of toponym resolution is possible by improving the geospatial location detection. By improving these fundamental automated tasks, our system can be a useful resource to phylogeographers that rely on geospatial metadata of GenBank sequences. Contact: davy.weissenbacher@asu.edu

The Doha multilateral round of trade negotiations sponsored by the WTO has been dragging on for over a decade, with no end in sight. In this short paper we assess empirically what determines the duration of trade negotiations, focusing on the span between the start of trade talks and their conclusion. We use data from 88 regional trade agreements between 1988 and 2009, and a semi-parametric Cox proportional hazards model. Four factors are robust determinants of the length of RTA negotiations. Negotiations are more protracted when there are more countries at the negotiation table, and when the countries are not from the same region. Negotiations between more open and richer countries are also finished more quickly.

The aim of this study was to obtain output through certain mix proportions of multiple raw material input and to determine the optimal product mix that maximizes profitability in the flour industry in which products are obtained using the mixtures of these outputs. To this end, a multi-product linear programming model which is based on hypothetical data was developed under the constraint of resources and this model was analyzed using computer software and the results were evaluated. © Medwell Journals, 2012.

Motivation: Systematically predicting gene (or protein) function based on molecular interaction networks has become an important tool in refining and enhancing the existing annotation catalogs, such as the Gene Ontology (GO) database. However, functional labels with only a few (, National Institute of General Medical Sciences (U.S.) (Grant 1U54GM114838)

Motivation: Large-scale evolutionary events such as genomic rearrange.ments and segmental duplications form an important part of the evolution of genomes and are widely studied from both biological and computational perspectives. A basic computational problem is to infer these events in the evolutionary history for given modern genomes, a task for which many algorithms have been proposed under various constraints. Algorithms that can handle both rearrangements and content-modifying events such as duplications and losses remain few and limited in their applicability. Results: We study the comparison of two genomes under a model including general rearrangements (through double-cut-and-join) and segmental duplications. We formulate the comparison as an optimization problem and describe an exact algorithm to solve it by using an integer linear program. We also devise a sufficient condition and an efficient algorithm to identify optimal substructures, which can simplify the problem while preserving optimality. Using the optimal substructures with the integer linear program (ILP) formulation yields a practical and exact algorithm to solve the problem. We then apply our algorithm to assign in-paralogs and orthologs (a necessary step in handling duplications) and compare its performance with that of the state-of-the-art method MSOAR, using both simulations and real data. On simulated datasets, our method outperforms MSOAR by a significant margin, and on five well-annotated species, MSOAR achieves high accuracy, yet our method performs slightly better on each of the 10 pairwise comparisons. Availability and implementation: http://lcbb.epfl.ch/softwares/coser. Contact: mingfu.shao@epfl.ch or bernard.moret@epfl.ch

Motivation: Proteins are responsible for a multitude of vital tasks in all living organisms. Given that a protein’s function and role are strongly related to its subcellular location, protein location prediction is an important research area. While proteins move from one location to another and can localize to multiple locations, most existing location prediction systems assign only a single location per protein. A few recent systems attempt to predict multiple locations for proteins, however, their performance leaves much room for improvement. Moreover, such systems do not capture dependencies among locations and usually consider locations as independent. We hypothesize that a multi-location predictor that captures location inter-dependencies can improve location predictions for proteins. Results: We introduce a probabilistic generative model for protein localization, and develop a system based on it—which we call MDLoc—that utilizes inter-dependencies among locations to predict multiple locations for proteins. The model captures location inter-dependencies using Bayesian networks and represents dependency between features and locations using a mixture model. We use iterative processes for learning model parameters and for estimating protein locations. We evaluate our classifier MDLoc, on a dataset of single- and multi-localized proteins derived from the DBMLoc dataset, which is the most comprehensive protein multi-localization dataset currently available. Our results, obtained by using MDLoc, significantly improve upon results obtained by an initial simpler classifier, as well as on results reported by other top systems. Availability and implementation: MDLoc is available at: http://www.eecis.udel.edu/~compbio/mdloc. Contact: ude.ledu@yaktahs.

In the crystal structure of the title compound, [WCl(2)(NO)(C(6)H(16)P(2))(2)]Cl, the seven-coordinated tungsten(II) center displays a distorted penta-gonal-bipyramidal geometry with trans nitrosyl and chloride ligands. The NO and Cl ligands are disordered over two positions; the site occupancy factors are 0.6 and 0.4.

Data standards play an important role to provide interoperability among different system. As other applications of telemedicine, the tele-radiology needs these standards to work properly. In this article, we conducted a review to introduce some data standards about tele-radiology. By searching PUBMED and Google Scholar database, we find more relevant articles about data standards in tele-radiology. Three categories of standards identified, including data interchange, document and terminology standards. Data interchange standards, including those which facilitate the understanding of the format of a massage between systems, such as DICOM and HL7. Document standards, including those which facilitate the contents of a massage, such as DICOM SR and HL7 CDA. And terminology standards, including those which facilitate the understanding of concepts of the domain. Since, the harmonization between different standards are important to meet interoperability, so the more effort is needed to conduct harmonization between tele-radiology standards and other domain.

Об’єми даних, які зберігаються та передаються, постійно ростуть. Коли потрібно передати великі об’єми даних, на допомогу приходить компресія. Добре підібраний алгоритм компресії здатен зменшити розмір даних у середньому на 60 %. Проблема появи нових і модифікації чи оптимізації старих алгоритмів компресії стоїть дуже гостро. У статті розглянуто деякі відомі сьогодні алгоритми компресії. Наведено короткий опис основних властивостей і варіантів реалізації таких алгоритмів. У рамках роботи над статтею було реалізовано ці алгоритми та проведено експериментальний аналіз їхньої якості та швидкості роботи. Робота може бути цікавою та корисною дослідникам галузі компресії даних., The amount of data that is stored and transferred grows regularly and rapidly. When it comes to transferring large data volumes, a data compression algorithm can be useful. A well-chosen data compression algorithm can reduce the size of the data to up to 60%. The problem of creating new and modifying or optimizing old algorithms is up to date. This article discloses some of the most widespread algorithms of data comparison; more exactly, four of them. Shannon–Fano coding, named after Claude Shannon and Robert Fano, is a technique for constructing a prefix code based on a set of symbols and their probabilities. It is suboptimal in the sense that it does not achieve the lowest possible expected code word length like Huffman coding. Huffman code is a particular type of the optimal prefix code that is commonly used for lossless data compression. The process of finding or using such a code proceeds by means of Huffman coding; an algorithm developed by David A. Huffman. Lempel–Ziv–Storer–Szymanski (LZSS) is a lossless data compression algorithm, a derivative of LZ77 that was created in 1982 by James Storer and Thomas Szymanski. The main difference between LZ77 and LZSS is that in LZ77 the dictionary reference could actually be longer than the string it was replacing. In LZSS, such references are omitted if the length is less than the “break even” point. Furthermore, LZSS uses one-bit flags to indicate whether the next chunk of data is a literal (byte) or a reference to an offset/length pair. Lempel–Ziv–Welch (LZW) is a lossless data compression algorithm created by Abraham Lempel, Jacob Ziv, and Terry Welch. It was published as an improved implementation of the LZ78 algorithm. As part of the work on the article, these algorithms were implemented and an experimental analysis of their quality and speed was carried out. Those experiments gave the conclusion that the best compression speed results were shown by LZSS and the best compression ratio was reached by LZW. The work can be useful for researchers in the field of data compression.

Computer science is often viewed with an extreme polarity. Hype for rapid change in industry and everyday life is counterbalanced by a growing distrust of all things digital. This thesis portfolio feeds into both sides of this dichotomy within computer science. In my technical project, I discuss a personal experience which shed light on how programs are changing modern finance, showing the intersection between UVA computer science education and industry. In the STS research paper, I perform a case study in which I investigate the effects of diminished internet privacy today in social media platforms. Both sections are totally distinct, yet they are two topics that capture the love-hate relationship a lot of people have towards programs. The STS portion of this portfolio examines the social media giant TikTok and the risks inherent in their aggressive collection of user data. More specifically, the paper delves into how TikTok’s collection of user data and algorithmic uses of this data work to eliminate human choice and intellectual freedom, resulting in polarized relations on both a person-to-person and global scale. Starting off with a historical background, the paper explores the company’s scandals and its developing relationship with the Chinese and United States governments. Since TikTok had gone international, their relations with the US progressively worsened, also breeding distrust between the US and China. The paper goes on to look into the kinds of data TikTok collects and their most immediate uses of this data, primarily looking into the company’s privacy policy and studies on their “For You” page’s video recommendations. This section’s findings reveal that TikTok’s highly individualized data on each user enables the platform to hijack the user’s attention with the recommended content, eliminating the need for the user to choose what content they view. Given that TikTok’s userbase is largely children, this gives a computer algorithm a concerning amount of power over the ideologies and mental beliefs a person may develop from a young age. The paper’s final section explores the broader societal consequences resulting from a platform’s extreme personalization, looking into the Cambridge Analytica scandal and an analysis on highly segmented advertisement and political discourse. When everybody is fed a different truth, people’s ability to participate in healthy discussion of modern events is minimized, harming the democratic process as a whole. The technical portion of this portfolio expands on a summer internship experience I had going into my fourth year. During my internship with a consulting firm, I worked on an engagement with a major investment bank, and my team was tasked with implementing a platform for pricing trades and risk. The investment bank had already built out its own platform using proprietary software, but my team of consultants sought to create a more efficient solution through third-party cloud-computing resources. Normally, these financial calculations involve numerous, time-consuming simulations and other computations for a batch of trades; however, by dividing up the task into subtasks and distributing them onto a grid of servers, they could be completed in parallel, overnight. My focus during the summer was on the “middleware” of the platform, which was the code responsible for packaging and sequencing tasks requested from the frontend and placing them on a queue to be sent to the backend for computation. It was my task to review and present code improvements for the middleware to the broader team and implement several new features essential to the middleware’s functioning. I added two major features to the middleware and also presented to the broader team the inner workings of the platform’s dependency graph, a data structure that eliminates redundant tasks and ultimately places the correct sequence of jobs to be performed onto the queue to the backend grid. As a result, the team gained a more unified and cohesive understanding of the middleware, whose code the other workstreams of the project never directly see. By the time my internship concluded, only one of the six steps for pricing a trade had been implemented. The team must continue to implement the remaining steps for a fully-functioning platform.

Data security and privacy is a growing concern for people and companies with the rise of data breaches. The technical report describes a password rotation tool that was created. The tool was created to help automate the process making it more efficient and secure, which allowed for an overall increase in password security. The science, technology, and society (STS) research paper reviews data breaches and presents certain steps individuals can take to secure their data. The tightly coupled technical and STS topics cover a range of commercial and personal solutions that can be used to better protect data. The technical report outlines the creation of a software tool to automate password rotations for an existing system. The goal of the tool was to make password rotations more efficient since they were previously a manual process. The automation of the process would make the passwords more secure and allow the rotation to happen more often. All of this helps increase data privacy and protection. The tool was able to completely automate the process. Now the rotations can happen almost automatically this reduces the chance for human error. It also means the rotations can happen more often which would further secure the system. The tool was a commercial solution to data privacy and worked effectively to solve the problem. The STS paper addresses how individuals can take steps to increase data privacy and security. At first the idea was that individuals should take steps on their own, however later it was realized engineers can also play an important role. Bijker’s Social Construction of Technology theory was used to show how engineers are pressured to secure data, but how they can also enable the individual to keep their data secure. The evidence shows that there has been a massive amount of data breaches that could have easily been prevented. The breaches usually revolve around one or two passwords being compromised. Basic steps could have been taken to protect these passwords. Some of these steps include having longer and stronger passwords. The engineers can build the systems that force the users to take these steps. This will lead to a great increase in data security. Large amounts of personal data live on the internet and are constantly stolen. The technical report presents a commercial solution, and the STS report shows how engineers and individuals can take steps to better protect their data. Data security and privacy is important to protect all individuals and their personal data.

GESIS Papers

This paper explores how trust in teacher professional judgment has been reconstituted through the globalised trends of performative accountability and reductive data-driven logics. The article draws upon empirical research from interviews with teachers and school leaders as well as observations of teacher preparation days, classroom and staff professional learning communities (PLCs), as part of a larger study of schooling practices in two geographically and contextually bound Queensland public schools. The paper focuses attention upon the socio-political, material-economic and cultural-discursive conditions inscribed in how data are currently understood and deployed, and how these conditions constrain trust in teachers, devaluing teachers’ own professional judgment. Specifically, we flag how the practices and conditions that constrained trust were manifest in a) pressures to ensure teachers generated and collected data on an ongoing basis to substantiate their claims about student learning, and b) a perceived mistrust amongst parents and a subsequent need to justify decision-making on the basis of ‘hard evidence’.

With the increased use of data in the governance of education, the study of the performative effects of this type of governance on various elements of education becomes increasingly important. One central aspect of the performative effects of data is the effects on human beings addressed (directly or indirectly) by the data. Recent scholarship has conceptualized human responses to data through the notion of affectivity. While this conceptualization offers a viable alternative to previous rational choice theorizations of human responses to data that over-emphasize rational though, it in turn under-emphasizes rational thought. This paper theorizes human responses to data as a matter of being set in motion by data, a term that includes rational thought, affect, and many more responses. Through the philosophy of agential realism, the paper elaborates how data and human beings (among other entities) radically co-constitute each other, causing re-configurations of human subjectivities which make people think, feel, and act in certain (but unpredictable) ways. The theorization of how data re-configure subjectivities is illustrated through three case examples, drawn from Danish higher education, which show that both those governed and those governing are set in motion in similar ways by data.

This paper proposes Structured Data Nets (StDN), a Petri net extension that describes transactional systems with data. In StDNs, tokens are structured documents. Each transition is attached to a query, guarded by patterns, (logical assertions on the contents of its preset) and transforms tokens. We define StDNs and their semantics. We then consider their formal properties: coverability of a marking, termination and soundness of transactions. Unrestricted StDNs are Turing complete, so these properties are undecidable. We thus use an order on documents, and show that under reasonable restrictions on documents and on the expressiveness of patterns and queries, StDNs are well-structured transition systems, for which coverability, termination and soundness are decidable.

The following paper dives into the implications of the growing presence of the Internet and other technologies in our daily lives, especially in relation to handling healthcare data and privacy. In each section, the paper explores the associations of technologies to knowledge, power, and control in the field of healthcare. Ultimately, it warns against the increasingly exploitative nature of today’s technology products which oftentimes trade personal information for usage and convenience. In addition, it discusses the benefits and potential consequences of current healthcare privacy laws. To transition into practical applications and world systems, examples such as the Mexican healthcare system are presented as case studies of how technology companies and producers can adapt their policies and products to best cater to the needs and wants of marginalized communities and populations.

This project is supplementary materials for a submitted paper in the Journal of Corpus Linguistic and Linguistic Theory. There are nine databases, R scripts used to construct figures, as well as codes for our statistical calculations given in the paper. As I am using R programming language, this supplementary material is designed to be compatible to run in R. Should you need another format of our database or codes, please contact me at karlinadenistia(at)gmail.dom. Please cite our paper if you use one of these materials for further research.

Data and scripts underlying a paper on expressiveness and grammatical integration, by Mark Dingemanse and Kimi Akita (Journal of Linguistics, 2016). Download and view analysis.html for readable R code and figures, or check analysis.Rmd for the source. The data is in ExpInt.txt (CSV) and ExpInt.Rdata (R). If you find this to be of use, please cite the paper: Dingemanse, Mark and Kimi Akita. (2016) An inverse relation between expressiveness and grammatical integration: on the morphosyntactic typology of ideophones, with special reference to Japanese. Journal of Linguistics.

In the era of Industry 4.0 and digitalization, planning solutions need to co-exist with each other and be able to manage higher complexity and with a higher performance. As the concept smart production planning and control is a part of industry 4.0, it is highly relevant to study and is in this paper explored on the four elements of smart PPC (real-time data management, dynamic production planning and re-planning, autonomous production control, and continuous learning). This paper provides a framework for linking the four elements of smart PPC with data quality issues in state-of-the-art production planning and control environments. Maintaining a high standard of data quality in the business processes aids the organization to stay competitive in its market. Hence, our assumption is that a high level of data quality is needed in production planning and control for a high-performance outcome. The empirical part of our study results in a bar-chart of seven data quality problems and their occurrences together with their causes in PPC. According to the empirical data results, inaccurate data entries is the most common data quality problem related to PPC. The causes of the inaccurate data entries can be linked to human resources and organizational control. Future research should strengthen the validity of the proposed linkages between data quality problems and elements of smart PPC and implications on strategic, tactical, and operational planning levels.

Peer reviewed: True, Seeds and other plant materials in seed and gene bank collections are rarely considered adequately conserved today unless genetically identical duplicate samples have been created and safely stored elsewhere. This paper explores the history of seed banking to understand how, why and with what consequences copying collections came to occupy this central place. It highlights a shift in the guiding metaphor for long-term preservation of seed collections, from banking to backup. To understand the causes and consequences of this shift in metaphor, the paper traces the intertwined histories of the central long-term seed storage facility of the United States (opened in 1958) and the international seed conservation system into which that facility was integrated in the 1970s. This account reveals how changing conceptions of security, linked to changing economic, political and technological circumstances, transformed both the guiding metaphors and the practices of seed conservation in these institutions. Early instantiations of long-term cold storage facilities vested security in robust infrastructures and the capacities of professional staff; between the 1960s and 1990s, this configuration gave way to one in which security was situated in copies rather than capacities. This observation ultimately raises questions about the security promised and achieved through present-day infrastructures for crop genetic resources conservation.

Coastal flooding in cities is on the rise due to factors such as climate change, changes in precipitation, and land subsidence. Ho Chi Minh City, among the top flood-prone cities, is predicted to be submerged along with most of southern Vietnam by 2050. A critical step to addressing this problem is through collecting flood data. My capstone project, FloodWatch, fills in this step by serving as a platform for collecting user-generated flood data. At the same time, the application provides immediate benefits by enabling residents of Ho Chi Minh City to respond to local flooding in real-time. My STS research topic discusses the consequences of existing or proposed solutions to flooding such as sea dikes or physical flood barriers. It delves into a variety of factors, particularly environmental and societal, which can impact the effectiveness of flooding solutions. My STS research primarily serves as inspiration for the FloodWatch project by exposing the non-technical factors involved in addressing flooding. It is important for engineers to consider the broader context of their technical solutions. This can influence their technical decisions or change their approach entirely. In the long-term, we intend FloodWatch to inform decision-makers and engineers about the many factors involved in flooding in order to help them derive the most effective solutions. In my STS research, I expose the challenges and harms of implementing gray infrastructure flooding solutions in various coastal cities. Here, gray infrastructure refers to man-made structures such as concrete barriers or pipes. Specifically, I use Ho Chi Minh City and Venice as case studies for discussing the impact of gray flooding solutions. From this research, I found that gray infrastructure, although effective at preventing flooding, can have negative environmental and societal effects. Some of these include diminishing marine life or disrupting economic activity. An alternative to the gray infrastructure I discuss in my research is green-blue-gray infrastructure. This approach involves considering environmental benefits as a secondary goal with the primary goal being flood mitigation or prevention. Regardless of what approach is used, I’ve found that one must look at flooding as a sociotechnical system involving not just engineering challenges but also environmental and societal ones. FloodWatch, my technical project, aids engineers or city officials in sociotechnical systems thinking by providing the flood data they need to implement the most comprehensive flooding solution. FloodWatch is a mobile application that allows city residents to upload and view real-time reports on flooding in their area. Residents can use this app to quickly and safely navigate streets or prepare for incoming flooding. The app could also provide a wealth of data for researchers. They can use this data to analyze flooding patterns or train machine learning models to predict these patterns. City officials or engineers can then use insights or predictions to inform their decisions on what flooding solutions to implement. My technical project was the primary inspiration for my STS research paper. As I was developing the FloodWatch application, I questioned its purpose and its effectiveness in addressing flooding. The STS research paper was an opportunity to explore this question and develop a stronger motivation for the technical project. From the paper, I realized the main value my technical project provided was flood data. Although this data would not address flooding directly, it would help researchers and engineers to determine optimal flooding solutions. Completing both the technical project and STS research paper has given me a practical understanding of how STS fits into engineering practice. The technical project allowed me to dive deep into solving technical problems and allowed me to develop my software development skills. The STS research paper allowed me to understand the broader picture and purpose of my technical project. I was able to gain domain knowledge of coastal flooding, and more importantly, realize the multifaceted nature of tackling flooding. This project as a whole has allowed me to realize that engineering and STS are not separable; rather, engineers must take both technical and non-technical considerations to arrive at an ethical, sustainable, and effective solution. This project has been funded by grants won by Professor Nguyen, Department of Computer Science. He has helped with defining the vision of the project, gaining support from collaborators at HCMUS, and overseeing the project’s progress.

During the AGIPEB-Project several event history analyses were implemented to find an-swers to the question to what extent fixed-term employment as well as other sociostructural or labour market variables influence transition-rates into partnership-stabilizing events. These events under consideration were: cohabitation, marriage, parenthood and homeownership. While we relied on cox regressions and exponential models in earlier studies, we went on to use piecewise-constant models in later phases of the project. In the present working paper we document the data management and the episode splitting process in praparation for our empirical analyses (for these analyses see the remaining working papers in the AGIPEB-series). For all our data management as well as episode splitting we used Stata IC, Version 14.

With the rapid development of Internet technology and the popularization and application of various terminals, human beings live in the era of big data. In the process of people's daily use of the network, all kinds of data are produced at all times. At the same time, the problem of information security is increasingly prominent and the situation is more and more complex. As the increasing threat of information security has caused irreparable economic losses to human beings, which seriously hinders the further development of information technology, it is urgent to combat computer crime. When users use search engine to get information, the whole process is recorded, including the user's query log. Through the analysis of these query logs, we can indirectly get the real needs of users, so as to provide reference for the optimization and improvement of search engine. The analysis of the network user behavior requires the analysis of the data generated by the network user behavior. The traditional manual data analysis and the single computer data transmission mode have not been able to better analyze the increasing data information. Therefore, we should think of an effective technology, which can effectively analyze the huge and complex data and show the results. Based on the log analysis of voice data system, this paper constructs a user behavior analysis system under the network environment. Experimental results show that the method proposed in this paper can effectively reflect the behavior of network users, and timely feedback.

Customer is a king and Customers are the mainly focused in making new marketing strategy. In the banking field a unique relationship exists between the customers and the bank. But because of various reasons like lack of training ,new technology literacy, financial targets, risk of failure etc., some banks are still following the traditional ways of marketing and another hand some are making attempts to adapt CRM. It is with this background, the researcher has made a modest attempt towards the idea that CRM can be adapted uniformly in the banking industry for betterment of Banking Services. Understanding on Customer Relationship Management is always a concern among the service providers especially banks. Banks makes their own way of managing their relationships new and existing customers. The aim of this paper is to examine the Customer Relationship Management as a new methodology looks forward to identify and attract consumers through the process of developing relationships (business - customer). The methodology of the CRM aims to maintain customer satisfaction and increase consumer loyalty. The purpose of this paper is to study the importance of CRM systems and in-depth knowledge of methods and management techniques customer relationships. The main issue of this study is that CRM has become a multi-faceted and complex phenomenon that is ridden by various factors. Due to this complexity, a number of different variables have been used to measure CRM which investigated by several prior studies. However, most of businesses need to know and look at the particular measures and dimensions of the CRM that have a significant impact on customer satisfaction and loyalty, which would enrich the business' performance, especially with the increase in competition as well as lack of differentiation in providing a service. This paper aimed to review literature on CRM and to identify its impact on customer satisfaction and customer loyalty. The studies are analyzed on the basis of some general characteristics and variables that significantly enhance CRM and its influence on customer satisfaction and customer loyalty. For this purpose, we investigate the existing literature on the impact of CRM on customer satisfaction and customer loyalty along with its spread among publications to identify the potential development in the field.

PurposeIn line with the cognitive viewpoint on the phenomenon of information, the constructivist tradition based on Maturana and Varela's theory of knowing, and some aspects of Shannon's theory of communication, the purpose of this paper is to shed more light on the role of information, data, and knowledge in the cognitive system (domain) of the observer.Design/methodology/approachIn addition to the literature review, a proposed description of the communication and knowledge acquisition processes within the observer's cognitive system/domain is elaborated.FindingsThe paper recognizes communication and knowledge acquisition as separate processes based on two roles of information within the observer's cognitive system, which are emphasized. The first role is connected with the appropriate communication aspects of Shannon's theory related to encoding cognitive entities in the cognitive domain as data representations for calculating their informativeness. The second role involves establishing relations between cognitive entities encoded as data representations through the knowledge acquisition process in the observer's cognitive domain.Originality/valueIn this way, according to the cognitive viewpoint, communication and knowledge acquisition processes are recognized as important aspects of the cognitive process as a whole. In line with such a theoretical approach, the paper seeks to provide an extension of Shannon's original idea, intending to involve the observer's knowledge structure as an important framework for the deepening of information theory.

International organisations whose bodies monitor the realisation and protection of the rights of national and ethnic minorities (Council of Europe, United Nations) have expressed their concern about the lack of data in the Republic of Serbia?s reports on the protection of the rights of national minorities, and they have provided some recommendations in order to amend the situation. Prompted by these remarks, this paper considers the flaws in Serbia?s policy of multiculturalism. The main finding ? that Serbia?s policy of multiculturalism is not founded on verified and measurable data ? has been examined further by indicating the principles and methodologies of collecting data based on ethnic and national identity. By using a range of examples, the paper points to the social issues that occur due to neglect in collecting data on citizens? ethnicity, while the necessity of an interdisciplinary approach to collecting such data is also considered. A holistic approach to researching ethnicity assumes using not only demographic methods, but also methods from and knowledge of other social sciences and humanities. The United Nations supports the idea that the approach to ethnically sensitive data should be holistically founded with its position that all data regarding people?s identity must be based on human rights ? a Human Rights-Based Approach to Data (HRBAD), in which case the relevance of statistical data is promoted.