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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.

Translation from the French version: https://cloud.parisdescartes.fr/index.php/s/mTAbrXWN4sReA3Q#pdfviewer; The Earth Politics Center (EPC) aims to produce interdisciplinary research of excellence, visible and attractive in France and internationally, in collaboration with stakeholders acting at both local and global levels.With the aim of responding to the scientific and political issues raised by various diagnoses on the state of the planet (growing effects of industrial and agricultural activities on the major balances of the biosphere, disruption of the major water, carbon or nitrogen cycles, etc.), the Center has set itself the mission of developing new ways of knowing and governing these socio-environmental phenomena, while at the same time forming part of a transformed relationship between science and society.Founded in 2019, the EPC has enabled the emergence of an interdisciplinary research community based on the joint exploration of nature and societies by the experimental sciences (physical and biological) and the humanities and social sciences. An Earth challenged by habitability. University and the Challenges of Earth Knowledge, a strategic positioning text, defines its research agenda for the years to come and gives concrete expression to these three years of interdisciplinary dialogue.

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.

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.

U periodu 1962.–2012., tijekom 60 godina neprekidnog izlaženja, u časopisu Metalurgija su objavljivali autori iz preko 40 država, od Mexica do Kine (sa svih 5 kontinenata). Cilj članka je dati Listu objavljenih radova u 199 svezaka ili 238 brojeva, sa 2721 znanstvenih i stručnih, te 287 priloga (ukupno 3008 radova), autora, koji su svoje rezultate i ideje provjeravali ili ih našli na stranicama ovog časopisa. Svima hvala., For the interval 1962-2022 y, during the continuos publication last 60 years, in Metalurgija Journal Authors from 40 countries from Mexico to China (all 5 continent ) have publish. The goal of the Article is give List of Papers published in this interval, 199 issues or 238 numbers, 2721 scientific and technical, and 287 contributions (total 3008 papers ) of Authors whose investigation results and ideas have been examined and found on the pages of this Journal. Thanks for all.

This book constitutes the refereed post-conference proceedings of the 7th European Conference on Information Literacy, ECIL 2021, held in online mode in September 2021. The 61 revised papers included in this volume were carefully reviewed and selected from 192 submissions. The papers are organized in the topical sections on ​information literacy in a post-truth era and news literacy ; health literacy ; data literacy ; digital literacy and digital empowerment ; other literacies ; information literacy in different contexts ; information literacy education in different sectors ; information literacy instruction ; assessment and evaluation of information literacy ; academic integrity, plagiarism and digital piracy ; information behaviour ; information literacy, libraries and librarians ; information literacy in different cultures and countries ; information literacy and democracy, citizenship, active participation.

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.

Milk microfiltration process plays a key role in the dairy industry. Crossflow microfiltration of skimmed milk using a membrane with 0.1 µm mean pore size is widely used to fractionate the two main groups of dairy proteins: casein micelles (~150 nm) and serum proteins (~2-15 nm). Retentate, containing mainly casein micelles, is generally used to enrich vat milk for cheese making. Permeate, containing serum proteins, lactose and minerals, is usually ultrafiltered in order to produce protein-rich concentrate with a high nutritional value dedicated to specific populations such as infants and seniors. The great interest in these protein fractions explains the increasing number of microfiltration equipments in the dairy industry. This data article contains data associated with milk microfiltration process experiments and properties of the resulting dairy fractions annotated from a collection of scientific documents. These data are stored in INRAE public repository (see Data accessibility in the Specification Table for direct links to data). They have been structured using MILK MICROFILTRATION ontology and are replicated in @Web data warehouse providing additional querying tools (https://www6.inrae.fr/cati-icat-atweb/).

This paper presents the recently finished EU-funded RICHFIELDS project aimed to design a new research infrastructure that would foster research in the areas of food and nutrition with a focus on consumers' behavior and lifestyle. In this project, an architecture of a new consumer data platform was designed and discussed from the researchers, business, management, ethical and legal points of view. Also new methodology for supporting big and open data standardization and interoperability was developed.

The purpose of this whitepaper is to describe a largely unrecognised and unsupported but very common research data management (RDM) use-case: that of the traditional “Small Data” Humanities and Cultural Heritage (HCH) research project producing or working with “primary source” research data (i.e. facsimiles and representations of cultural objects as digital text, media, or models). This paper complements the submission from the Canadian Society for Digital Humanities/Société canadienne des humanités numériques (CSDH/SCHN), which is concerned with the case of research and data in the Digital Humanities, including in such small data contexts, more broadly. As we shall argue in this paper, the kind of traditional data and RDM use-case we are discussing here has gone largely unrecognised by Digital Research Infrastructure (DRI) developers and policy makers — in part because the nature, size, methods of production, and purpose of these datasets are quite different from data production and management in other disciplines, and in part because the data themselves are not always understood as data (or their management as an RDM problem) by the relevant research community (e.g. 1,2). The result is that large quantities of small-project HCH research data are poorly managed and maintained and that often extremely well-curated datasets produced by HCH researchers remain invisible, siloed, or difficult to access by Big Data researchers (where such access is appropriate and ethical); unnecessarily expensive to produce and maintain; and, as a result, in danger of premature loss or obsolescence to researchers and the wider community. The specific tools and techniques required to address these problems already mostly exist within the international DRI ecosystem, though we are aware of no single system or provider that includes them all. We are also aware of no system or provider that specifically supports the workflow, use-case and datatypes we describe here, including such Humanities-focussed projects as Humanities Commons. The “Good Things come in Small Packages” Partnership was created in order to promote recognition and support of this RDM use-case and has been in discussions with several developers, providers, and policy makers that are interested in promoting and supporting improved RDM practices for traditional “Small Data” HCH research. This exercise represents an excellent opportunity for Canada, through NDRIO, to lead internationally in supporting this societally important research.

In the digital era like today the growth of data in the database is very rapid, all things related to technology have a large contribution to data growth as well as social media, financial technology and scientific data. Therefore, topics such as big data and data mining are topics that are often discussed. Data mining is a method of extracting information through from big data to produce an information pattern or data anomaly

This study examines data from the California EMS Information System (CEMSIS) to identify factors that influence prehospital time for EMS events related to motor vehicle collisions (MVCs). While only 19 percent of the United States population resides in rural areas, over half of all traffic fatalities involve rural motor vehicle collisions. Rural and urban MVCs result in similar injury severities, however relative inaccessibility of trauma centers and prehospital EMS time (activation, response, and transport time) likely contribute to the generally higher mortality rate in rural areas. For the present study, 24 CEMSIS data variables were requested, many of which involved missing data, which severely restricted the potential analysis of the impact of EMS response times. However, the findings did show that average overall EMS time (including response, scene and transport time) were approximately twice as long for collisions in rural zip codes compared with urban zip codes. Several limitations influence the interpretation of these results. Data on prehospital EMS times is missing for much of the state—even for zip codes with records of EMS events, data is likely incomplete. In addition, zip code level location data is insufficient for adequate study of the effects of the built environment and road network on prehospital time. Furthermore, according to the National EMS Information System (NEMSIS) User Manual, the national dataset suffers from selection and information bias, which are likely also present in the CEMSIS data. Although the present study cannot analyze the effect of longer prehospital times on patient outcome, other research has found that longer prehospital times may negatively impact patient health. Recommendations for reducing time from injury to appropriate medical care in rural areas include improving cell phone coverage, compliance of rural 911 center with FCC wireless, use of GPS technology, and integration of automatic vehicle location and computer aided navigation technologies into all computer-aided dispatch systems. In addition, CEMSIS should improve the coverage of their dataset and ensure that all EMS activities are recorded. To expand the type of analyses that can be conducted using CEMSIS data, EMS records must include fields that allow them to be linked to hospital and police datasets. When such data becomes available, research must be conducted to determine whether prehospital time is significantly related to patient outcome following motor vehicle collisions.

CLAIRE the ‘initiative of a pan-EU confederation of AI Research Labs’[Cl18] anticipates a humane AI which is based on ethical and trustworthy tenets empowering citizens and society. Hence in order to achieve a human AI, new semantic categories of standards must be written enabling stakeholders to implement a responsible AI. Communication however generates lots of unstructured data sets to be classified and structured into data types. AI-based algorithms are suitable to derive - from data sets and data types - more sophisticated and implicitly given information that can further be enriched to knowledge about autonomous communicating processes or even autonomous behaving humans. The knowledge gained from applying data enrichment algorithms in turn may be used for reasoning and prediction purposes, thus improving applications a lot. The aim of this project is to find a common unified tool able to handle big data flows and types that allows the recognition of hidden information from both data flows and data types. The hidden information is sometimes also called meta-data, thus being implicitly existing but by tools becoming explicitly seen.

Based on comparative case studies of four digital humanities projects, this paper identifies six community-centric factors in the sustainability of digital scholarship: factors that transcend organizational, technical, or financial considerations, and which relate to and rely upon the communities that support and surround digital humanties projects.

The paper presents the work on the Telamon and the Tituli projects which aim at creating online databases respectively of Greek and Latin epigraphic heritage from Bulgaria. The AIAX front-end service for the indexing and visualisation of EpiDoc XML files is described, as well as collaborations with different Bulgarian museums.

This paper discusses interdisciplinary collaborative practices based on the AdA project (meaning “Affektrhetoriken des Audiovisuellen” https://projectada.github.io/) in which institutionally situated research groups from computational sciences and film studies developed approaches for the analysis of the temporal dynamics of audiovisual expressivity by bringing together a theoretically informed methodology of film analysis and computational video analysis and semantic video annotation. In an earlier publication, we already presented how co-building a Digital Humanities tool could be seen as instrumental genesis. In it we concentrated on the iterative development of a visualisation framework to highlight the need for a common language and for the consideration of time needed for collaboration.We would like to pursue the reflection on the instrumental genesis (Rabardel, 1995) of collaboration tools along the time dimension. We saw that every step of tool development for such qualitative and exploratory research requires time: may it be for tool sketching, development, testing, or documenting its usage for manual annotation.

This presentation offers a side-by-side comparison of the publication processes for traditional monographs versus complex interactive digital projects. Prepared by Stanford University Press whose 6-year Mellon-funded digital publishing initiative is coming to a close in December 2023, the presentation reflects on the highly collaborative processes digital publications require.

Engaging both creators of digital web-based content and researchers of content on the web, this tutorial introduces and demonstrates toolsets for crawling and sharing web archives. Unlike large-scale subscription services like ArchiveIt, these open-source tools allow individuals and small groups to archive websites and web-hosted data more granularly and deeply.

The Russia-Ukraine war became an impetus for the creation of many objects of folk and professional art. There are street art, memes, fine arts, poetry, music, etc. The goal of the research is development of knowledge representation models of this objects, as well as collecting and analysis of the collection.

This study explores cultural exchanges of organ building between Central and South-Eastern Europe using multimodal analyses and interactive visualizations. Findings reveal specific trends of organ characteristics and diverse exchanges across the regions and timespans. Future research aims to further examine the findings through audiovisual digitization, acoustical analyses and virtualization approaches.

We propose to leverage public vulnerability scanners to examine the servers where online digital projects are hosted and list their common vulnerabilities. Provided in an anonymized, aggregated fashion, this assessment will shed light on our hypothesis: cyber threats are a significant factor in the degradation of online projects.

OstData is a research data service for Central, East, and Southeast European Studies. Based on its fine-grained metadata schema and faceted classification system, the research data repository and discovery system enables interdisciplinary collaboration as opportunity and fosters networking of national and international research communities, research infrastructures, and funders.

Our goal is to collect the sounds of Hebrew and to make them accessible for linguistic studies and cultural heritage preservation. Our presentation is focused on the pipeline we have developed and on two main tools that we have developed for it: 1. A Hebrew sound parser and 2. The database platform.

Eulalie is an operational, open-source, and free information system dedicated to electroacoustic music to document and preserve works. It is based on the Doremus ontology. We propose adapting this ontology to the specific field of electroacoustic music and its implementation in a database (Heurist, in dialogue with Nakala to host the documents). The project was born in the context of Art Zoyd Studios, a center for musical creation. One of the missions is to preserve the repertoire of the XXth and XXIst century pieces involving electronics. The principles of interoperability and Open Data have guided the different choices made throughout the project. Thus, it is not only possible to make the Art Zoyd Studios information database's content interact with other music resources, but the system itself, Eulalie, can be used by other institutions facing problems similar to those of Art Zoyd Studios.

The contemporary focus on the 'data problem' in the humanities centers on the uniqueness of humanities data. We argue this is a mistake. Humanities data are not special because of what they are, but rather because of how they are used. The "problem" with Humanities data lies in the use-case.

Since CATMA version 6.0 project data can be accessed in the form of Git repositories. A Python library which enables easy access to this data was developed at the department of Digital Philology at the Technical University of Darmstadt. It makes it possible to further process annotations using established and popular Python data science tools. The poster will serve as a kind of instruction manual for the use of the CATMA Git Access and the Python library.

The use of Artificial Intelligence techniques for the automatic recognition of architectural elements from point clouds can provide valuable support to facilitate preservation operations, and support many other activities related to Digital Cultural Heritage domain.

Our poster discusses HI's activities to construct a data structure and its operations in Japanese history for long-term utilisation and future issues related to multidisciplinary links. It focuses on specific repositories and examples of metadata exchange.

The poster aims to present (and discuss) the networking approach of the edition humboldt digital, which make extensive use of external data and web services.

The Clericus.ie is a digital humanities research project in the area of digital prosopography. The project developer a reliable, flexible and accessible digital platform to accommodate large quantities of biographical information on individuals and groups of individuals. It currently houses more than 25,000 published biographical records.

This paper presents the concepts and observations in the author’s keynote address at the May 2019 “Opportunity versus Challenge: Exploring Usage of Log-File and Process Data in International Large-Scale Assessments” conference in Dublin, Ireland. This paper recaps briefly some key points that emerged at the December 2018 ETS symposium on process data in Washington, DC, and suggests some common terminology and concepts for facilitating future meaningful discussion. It then borrows some concepts from evolutionary biologists to take a critical look at process data and some of its ethical implications before turning to discuss major applications for and challenges of using process data and logfiles. The second half of this paper then maps out the terrain in which process data emerges as a “byproduct” of the shift from paper-and-pencil testing to digital-based assessments (DBA) and looks closely at how process data interacts with the development and operationalization of assessment systems. This interaction suggests new, fresh ways of improving national and international assessment system processes, and ultimately the measurement of cognitive skills, while venturing into the new frontiers opened with process data.

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.

The objective of this paper is to review and analyze existing literature on Public Private Dialogue (PPD) and the way it can be applied to address the issues of competitiveness at the city level. The paper aims to explain how traditional PPD approaches and techniques should be adjusted for application at the city level.

The objective of this paper is to present the Competitive Cities diagnostic methodology and to guide city practitioners through the diagnostic process, tools and resources that the Competitive Cities team offers.

Background Dashboards can support data-driven quality improvements in health care. They visualize data in ways intended to ease cognitive load and support data comprehension, but how they are best integrated into working practices needs further investigation. Objective This paper reports the findings of a realist evaluation of a web-based quality dashboard (QualDash) developed to support the use of national audit data in quality improvement. Methods QualDash was co-designed with data users and installed in 8 clinical services (3 pediatric intensive care units and 5 cardiology services) across 5 health care organizations (sites A-E) in England between July and December 2019. Champions were identified to support adoption. Data to evaluate QualDash were collected between July 2019 and August 2021 and consisted of 148.5 hours of observations including hospital wards and clinical governance meetings, log files that captured the extent of use of QualDash over 12 months, and a questionnaire designed to assess the dashboard’s perceived usefulness and ease of use. Guided by the principles of realist evaluation, data were analyzed to understand how, why, and in what circumstances QualDash supported the use of national audit data in quality improvement. Results The observations revealed that variation across sites in the amount and type of resources available to support data use, alongside staff interactions with QualDash, shaped its use and impact. Sites resourced with skilled audit support staff and established reporting systems (sites A and C) continued to use existing processes to report data. A number of constraints influenced use of QualDash in these sites including that some dashboard metrics were not configured in line with user expectations and staff were not fully aware how QualDash could be used to facilitate their work. In less well-resourced services, QualDash automated parts of their reporting process, streamlining the work of audit support staff (site B), and, in some cases, highlighted issues with data completeness that the service worked to address (site E). Questionnaire responses received from 23 participants indicated that QualDash was perceived as useful and easy to use despite its variable use in practice. Conclusions Web-based dashboards have the potential to support data-driven improvement, providing access to visualizations that can help users address key questions about care quality. Findings from this study point to ways in which dashboard design might be improved to optimize use and impact in different contexts; this includes using data meaningful to stakeholders in the co-design process and actively engaging staff knowledgeable about current data use and routines in the scrutiny of the dashboard metrics and functions. In addition, consideration should be given to the processes of data collection and upload that underpin the quality of the data visualized and consequently its potential to stimulate quality improvement. International Registered Report Identifier (IRRID) RR2-10.1136/bmjopen-2019-033208

“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.

Background Rural patients with diabetes have difficulty accessing care and are at higher risk for poor diabetes management. Sustained use of patient portal features such as secure messaging (SM) can provide accessible support for diabetes self-management. Objective This study explored whether rural patients’ self-management and glycemic control was associated with the use of SM. Methods This secondary, cross-sectional, mixed methods analysis of 448 veterans with diabetes used stratified random sampling to recruit a diverse sample from the United States (rural vs urban and good vs poor glycemic control). Administrative, clinical, survey, and interview data were used to determine patients’ rurality, use of SM, diabetes self-management behaviors, and glycemic control. Moderated mediation analyses assessed these relationships. Results The sample was 51% (n=229) rural and 49% (n=219) urban. Mean participant age was 66.4 years (SD 7.7 years). More frequent SM use was associated with better diabetes self-management (P=.007), which was associated with better glycemic control (P Conclusions More frequent SM use is associated with engaging in diabetes self-management, which, in turn, is associated with better diabetes control. Among rural patients with diabetes, SM use is indirectly associated with better diabetes control. Frequent patient-team communication through SM about diabetes-related content may help rural patients with diabetes self-management, resulting in better glycemic control.

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.

Background Spontaneous reporting systems (SRSs) have been increasingly established to collect adverse drug events for fostering adverse drug reaction (ADR) detection and analysis research. SRS data contain personal information, and so their publication requires data anonymization to prevent the disclosure of individuals’ privacy. We have previously proposed a privacy model called MS(k, θ*)-bounding and the associated MS-Anonymization algorithm to fulfill the anonymization of SRS data. In the real world, the SRS data usually are released periodically (eg, FDA Adverse Event Reporting System [FAERS]) to accommodate newly collected adverse drug events. Different anonymized releases of SRS data available to the attacker may thwart our single-release-focus method, that is, MS(k, θ*)-bounding. Objective We investigate the privacy threat caused by periodical releases of SRS data and propose anonymization methods to prevent the disclosure of personal privacy information while maintaining the utility of published data. Methods We identify potential attacks on periodical releases of SRS data, namely, BFL-attacks, mainly caused by follow-up cases. We present a new privacy model called PPMS(k, θ*)-bounding, and propose the associated PPMS-Anonymization algorithm and 2 improvements: PPMS+-Anonymization and PPMS++-Anonymization. Empirical evaluations were performed using 32 selected FAERS quarter data sets from 2004Q1 to 2011Q4. The performance of the proposed versions of PPMS-Anonymization was inspected against MS-Anonymization from some aspects, including data distortion, measured by normalized information loss; privacy risk of anonymized data, measured by dangerous identity ratio and dangerous sensitivity ratio; and data utility, measured by the bias of signal counting and strength (proportional reporting ratio). Results The best version of PPMS-Anonymization, PPMS++-Anonymization, achieves nearly the same quality as MS-Anonymization in both privacy protection and data utility. Overall, PPMS++-Anonymization ensures zero privacy risk on record and attribute linkage, and exhibits 51%-78% and 59%-82% improvements on information loss over PPMS+-Anonymization and PPMS-Anonymization, respectively, and significantly reduces the bias of ADR signal. Conclusions The proposed PPMS(k, θ*)-bounding model and PPMS-Anonymization algorithm are effective in anonymizing SRS data sets in the periodical data publishing scenario, preventing the series of releases from disclosing personal sensitive information caused by BFL-attacks while maintaining the data utility for ADR signal detection.

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.

Background The current COVID-19 pandemic is associated with extensive individual and societal challenges, including challenges to both physical and mental health. To date, the development of mental health problems such as depressive symptoms accompanying population-based federal distancing measures is largely unknown, and opportunities for rapid, effective, and valid monitoring are currently a relevant matter of investigation. Objective In this study, we aim to investigate, first, the temporal progression of depressive symptoms during the COVID-19 pandemic and, second, the consistency of the results from tweets and survey-based self-reports of depressive symptoms within the same time period. Methods Based on a cross-sectional population survey of 9011 German adolescents and adults (n=4659, 51.7% female; age groups from 15 to 50 years and older) and a sample of 88,900 tweets (n=74,587, 83.9% female; age groups from 10 to 50 years and older), we investigated five depressive symptoms (eg, depressed mood and energy loss) using items from the Patient Health Questionnaire (PHQ-8) before, during, and after relaxation of the first German social contact ban from January to July 2020. Results On average, feelings of worthlessness were the least frequently reported symptom (survey: n=1011, 13.9%; Twitter: n=5103, 5.7%) and fatigue or loss of energy was the most frequently reported depressive symptom (survey: n=4472, 51.6%; Twitter: n=31,005, 34.9%) among both the survey and Twitter respondents. Young adult women and people living in federal districts with high COVID-19 infection rates were at an increased risk for depressive symptoms. The comparison of the survey and Twitter data before and after the first contact ban showed that German adolescents and adults had a significant decrease in feelings of fatigue and energy loss over time. The temporal progression of depressive symptoms showed high correspondence between both data sources (ρ=0.76-0.93; P Conclusions Overall, the results indicate relatively small differences in depressive symptoms associated with social distancing measures during the COVID-19 pandemic and highlight the need to differentiate between positive (eg, energy level) and negative (eg, depressed mood) associations and variations over time. The results also underscore previous suggestions of Twitter data’s potential to help identify hot spots of declining and improving public mental health and thereby help provide early intervention measures, especially for young and middle-aged adults. Further efforts are needed to investigate the long-term consequences of recurring lockdown phases and to address the limitations of social media data such as Twitter data to establish real-time public mental surveillance approaches.

Background Machine learning (ML) is now widely deployed in our everyday lives. Building robust ML models requires a massive amount of data for training. Traditional ML algorithms require training data centralization, which raises privacy and data governance issues. Federated learning (FL) is an approach to overcome this issue. We focused on applying FL on vertically partitioned data, in which an individual’s record is scattered among different sites. Objective The aim of this study was to perform FL on vertically partitioned data to achieve performance comparable to that of centralized models without exposing the raw data. Methods We used three different datasets (Adult income, Schwannoma, and eICU datasets) and vertically divided each dataset into different pieces. Following the vertical division of data, overcomplete autoencoder-based model training was performed for each site. Following training, each site’s data were transformed into latent data, which were aggregated for training. A tabular neural network model with categorical embedding was used for training. A centrally based model was used as a baseline model, which was compared to that of FL in terms of accuracy and area under the receiver operating characteristic curve (AUROC). Results The autoencoder-based network successfully transformed the original data into latent representations with no domain knowledge applied. These altered data were different from the original data in terms of the feature space and data distributions, indicating appropriate data security. The loss of performance was minimal when using an overcomplete autoencoder; accuracy loss was 1.2%, 8.89%, and 1.23%, and AUROC loss was 1.1%, 0%, and 1.12% in the Adult income, Schwannoma, and eICU dataset, respectively. Conclusions We proposed an autoencoder-based ML model for vertically incomplete data. Since our model is based on unsupervised learning, no domain-specific knowledge is required in individual sites. Under the circumstances where direct data sharing is not available, our approach may be a practical solution enabling both data protection and building a robust model.

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 For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic. Objective This study aimed to develop and test the feasibility of a “patients-like-me” framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases. Methods Our framework used COVID-19–like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-19–like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) at an academic medical center from 2008 to 2019. In total, 15 training cohorts were created using different combinations of the COVID-19–like cohorts with the ARDS cohort for exploratory purposes. In this study, two machine learning models were developed: one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features. Results Compared to the COVID-19–like cohorts (n=16,509), the patients hospitalized with COVID-19 (n=159) were significantly younger, with a higher proportion of patients of Hispanic ethnicity, a lower proportion of patients with smoking history, and fewer patients with comorbidities (P0.90). Validating in the COVID-19 cohort, the top-performing model for predicting IMV was the XGBoost model (AUROC=0.826) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all 4 COVID-19–like cohorts without ARDS achieved the best performance (AUROC=0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood cell count, cardiac troponin, albumin, etc). Our models had class imbalance, which resulted in high negative predictive values and low positive predictive values. Conclusions We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.

Background Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. Objective We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. Methods Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. Results Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19–related symptom compared to all other symptom-free days (P=.01). Conclusions Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19–related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

This dissertation explores the early digitalization in Sweden. As host to the first summit on the environment in Stockholm 1972 (UNCHE), as leading procurer of computer equipment as well as initiator to the world's first national Data Act of 1973, Sweden pioneered domains that have since emerged as critical to our age: environmental challenges and digital technology. Both products of the 1970s, environmental monitoring and population surveillance have typically been studied as separate phenomena. This thesis insists that they belong to the same lineage of procedures. In an effort to move beyond both celebratory and repressive discourses of technology, it turns to the infrastructural and operational aspects of histories of data. Crucially, the investigation traces a series of changes within the cultural techniques of modelling, linking and reuse. In so doing, it positions itself at the intersection of environmental and digital humanities, and contributes to both research fields. The aim of the investigation is to demonstrate how the handling of data shaped the environment and the population as objects of knowledge and to examine its consequences for state administration. By "following data" – noting how they were collected, transformed, played with, transferred, encrypted, stored and erased – the material and procedural elements of historical undertakings are emphasized. The investigation is organized in three parts: environment, population, and administration. Seeking the system dynamics origins of the 1972 Stockholm conference and the cybernetic principles of the systems ecology within the International Biological Program (IBP), the first part attempts to look past the politics of environmental movements and turns, instead, to modelling as a critical operation. The second part questions the generic surveillance studies approach to the 1970 census taking (FoB 70) and privacy debate in Sweden by considering these as part of the longer history of linking. Centering on an archival statistics system (ARKSY), a newspaper coupon campaign and the "deep map" of early computer cartography, the thesis here locates a shift from privacy as a vulnerable trait protected by law to a matter of database administration with citizens as managers of their own data. The third and final part is framed by operations of reuse. Such operations, the dissertation maintains, materialized as instrumental not only to household waste management but also to the handling of data in offices and archives. Scrutinizing test environments for word processing in the Swedish Parliament and the work of the Data Archiving Committee (DAK), the investigation argues that they should be understood as sites of reuse and recycling rather than as locations of paperless exchange. With early digitalization, the environment and the population started to emerge as "problems of data". Data were typically demanded in "raw" format and routinely portrayed as a new type of natural resource. In this setting, infrastructures of reuse proved more fundamental than imperatives of surveillance. Markedly, the manual aspects of "automatic" data processing (ADB) were downplayed even as they remained indispensable. Cycles of data appeared in many guises: in the natural environment, in models, in mapping systems, in archives. Guided by the analytical power of cultural techniques and drawing on a wide range of materials, this investigation is a prehistory of our data-driven world. QC 20230125

This dissertation explores the early digitalization in Sweden. As host to the first summit on the environment in Stockholm 1972 (UNCHE), as leading procurer of computer equipment as well as initiator to the world's first national Data Act of 1973, Sweden pioneered domains that have since emerged as critical to our age: environmental challenges and digital technology. Both products of the 1970s, environmental monitoring and population surveillance have typically been studied as separate phenomena. This thesis insists that they belong to the same lineage of procedures. In an effort to move beyond both celebratory and repressive discourses of technology, it turns to the infrastructural and operational aspects of histories of data. Crucially, the investigation traces a series of changes within the cultural techniques of modelling, linking and reuse. In so doing, it positions itself at the intersection of environmental and digital humanities, and contributes to both research fields. The aim of the investigation is to demonstrate how the handling of data shaped the environment and the population as objects of knowledge and to examine its consequences for state administration. By "following data" – noting how they were collected, transformed, played with, transferred, encrypted, stored and erased – the material and procedural elements of historical undertakings are emphasized. The investigation is organized in three parts: environment, population, and administration. Seeking the system dynamics origins of the 1972 Stockholm conference and the cybernetic principles of the systems ecology within the International Biological Program (IBP), the first part attempts to look past the politics of environmental movements and turns, instead, to modelling as a critical operation. The second part questions the generic surveillance studies approach to the 1970 census taking (FoB 70) and privacy debate in Sweden by considering these as part of the longer history of linking. Centering on an archival statistics system (ARKSY), a newspaper coupon campaign and the "deep map" of early computer cartography, the thesis here locates a shift from privacy as a vulnerable trait protected by law to a matter of database administration with citizens as managers of their own data. The third and final part is framed by operations of reuse. Such operations, the dissertation maintains, materialized as instrumental not only to household waste management but also to the handling of data in offices and archives. Scrutinizing test environments for word processing in the Swedish Parliament and the work of the Data Archiving Committee (DAK), the investigation argues that they should be understood as sites of reuse and recycling rather than as locations of paperless exchange. With early digitalization, the environment and the population started to emerge as "problems of data". Data were typically demanded in "raw" format and routinely portrayed as a new type of natural resource. In this setting, infrastructures of reuse proved more fundamental than imperatives of surveillance. Markedly, the manual aspects of "automatic" data processing (ADB) were downplayed even as they remained indispensable. Cycles of data appeared in many guises: in the natural environment, in models, in mapping systems, in archives. Guided by the analytical power of cultural techniques and drawing on a wide range of materials, this investigation is a prehistory of our data-driven world.

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 Worldwide, nonpharmacologic interventions (NPIs) have been the main tool used to mitigate the COVID-19 pandemic. This includes social distancing measures (closing businesses, closing schools, and quarantining symptomatic persons) and contact tracing (tracking and following exposed individuals). While preliminary research across the globe has shown these policies to be effective, there is currently a lack of information on the effectiveness of NPIs in the United States. Objective The purpose of this study was to create a granular NPI data set at the county level and then analyze the relationship between NPI policies and changes in reported COVID-19 cases. Methods Using a standardized crowdsourcing methodology, we collected time-series data on 7 key NPIs for 1320 US counties. Results This open-source data set is the largest and most comprehensive collection of county NPI policy data and meets the need for higher-resolution COVID-19 policy data. Our analysis revealed a wide variation in county-level policies both within and among states (P Conclusions This study is the first large-scale NPI analysis at the county level demonstrating a correlation between NPIs and decreased rates of COVID-19. Future work using this data set will explore the relationship between county-level policies and COVID-19 transmission to optimize real-time policy formulation.