Simplifying data access: the Energy Data Collection project

Computer ◽  
2001 ◽  
Vol 34 (3) ◽  
pp. 47-54 ◽  
Author(s):  
J.L. Ambite ◽  
Y. Arens ◽  
E. Hovy ◽  
A. Philpot ◽  
L. Gravano ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Access ◽  
Energy Data

Industry 4.0 Digital Technologies for data collection and control

2021 ◽  
pp. 43-58
Author(s):  
S. S. Yudachev ◽  
P. A. Monakhov ◽  
N. A. Gordienko
Keyword(s):  
Data Collection ◽  
Open Source ◽  
Software Defined Radio ◽  
Block Diagram ◽  
Data Access ◽  
Gnu Radio ◽  
Time Data ◽  
Object A ◽  
Data Propagation ◽  
And Control

This article describes an attempt to create open source LabVIEW software, equivalent to data collection and control software. The proposed solution uses GNU Radio, OpenCV, Scilab, Xcos, and Comedi in Linux. GNU Radio provides a user-friendly graphical interface. Also, GNU Radio is a software-defined radio that conducts experiments in practice using software rather than the usual hardware implementation. Blocks for data propagation, code deletion with and without code tracking are created using the zero correlation zone code (ZCZ, a combination of ternary codes equal to 1, 0, and –1, which is specified in the program). Unlike MATLAB Simulink, GNU Radio is open source, i. e. free, and the concepts can be easily accessed by ordinary people without much programming experience using pre-written blocks. Calculations can be performed using OpenCV or Scilab and Xcos. Xcos is an application that is part of the Scilab mathematical modeling system, and it provides developers with the ability to design systems in the field of mechanics, hydraulics and electronics, as well as queuing systems. Xcos is a graphical interactive environment based on block modeling. The application is designed to solve problems of dynamic and situational modeling of systems, processes, devices, as well as testing and analyzing these systems. In this case, the modeled object (a system, device or process) is represented graphically by its functional parametric block diagram, which includes blocks of system elements and connections between them. The device drivers listed in Comedi are used for real-time data access. We also present an improved PyGTK-based graphical user interface for GNU Radio. English version of the article is available at URL: https://panor.ru/articles/industry-40-digital-technology-for-data-collection-and-management/65216.html

scholarly journals Twenty Years of Data Linkage in The Australian Longitudinal Study on Women’s Health

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Colleen Loos ◽  
Gita Mishra ◽  
Annette Dobson ◽  
Leigh Tooth
Keyword(s):  
Longitudinal Study ◽  
Data Collection ◽  
Women’S Health ◽  
Women's Health ◽  
Linked Data ◽  
Data Linkage ◽  
Data Access ◽  
National Study ◽  
Australian Longitudinal Study ◽  
Data Collections

IntroductionLinked health record collections, when combined with large longitudinal surveys, are a rich research resource to inform policy development and clinical practice across multiple sectors. Objectives and ApproachThe Australian Longitudinal Study on Women’s Health (ALSWH) is a national study of over 57,000 women in four cohorts. Survey data collection commenced in 1996. Over the past 20 years, ALSWH has also established an extensive data linkage program. The aim of this poster is to provide an overview of ALSWH’s program of regularly up-dated linked data collections for use in parallel with on-going surveys, and to demonstrate how data are made widely available to research collaborators. ResultsALSWH surveys collect information on health conditions, ageing, reproductive characteristics, access to health services, lifestyle, and socio-demographic factors. Regularly updated linked national and state administrative data collections add information on health events, health outcomes, diagnoses, treatments, and patterns of service use. ALSWH’s national linked data collections, include Medicare Benefits Schedule, Pharmaceutical Benefits Scheme, the National Death Index, the Australian Cancer Database, and the National Aged Care Data Collection. State and Territory hospital collections include Admitted Patients, Emergency Department and Perinatal Data. There are also substudies, such as the Mothers and their Children’s Health Study (MatCH), which involves linkage to children’s educational records. ALSWH has an internal Data Access Committee along with systems and protocols to facilitate collaborative multi-sectoral research using de-identified linked data. Conclusion / ImplicationsAs a large scale Australian longitudinal multi-jurisdictional data linkage and sharing program, ALSWH is a useful model for anyone planning similar research.

scholarly journals Better data, better knowledge, better society: Developing an ideal homelessness data system drawing on lessons from global practice

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Ian Thomas ◽  
Peter Mackie
Keyword(s):  
Data Collection ◽  
Central Government ◽  
Service Providers ◽  
Data Access ◽  
Evidence Base ◽  
Data System ◽  
Policy And Practice ◽  
Good Data ◽  
Ideal System ◽  
Health And Social Care

The aim of this paper is to set out the principles of an ideal data system. Good data is crucial to effective policy and practice development in all social policy spheres and this is a particular challenge in the context of homelessness policy. Policy makers, practitioners and researchers have been highly critical of the current state of homelessness data across the globe, with concerns largely focused on the incompleteness of the data. Most research has narrowly focused on the strengths and weaknesses of different data collection techniques, such as Point-In-Time counts. However, good data does not only derive from the data collection method - consideration must also be given to the wider data system, including how data are generated, reported, analysed, and crucially, how they are made accessible and to who. The evidence base for the paper is a desk-based review of 49 data collection systems from 8 countries, including systems in health and social care settings—where data are being increasingly used to drive more effective care. The different systems are synthesised to generate 8 areas of design, being: data architecture, governance, data quality, ethical and legal, privacy/security, data access, and importantly, purpose. Drawing these elements together, the paper concludes that data collection should adopt a common data standard shared across the sector, enabling inter-organisational information sharing and improving collaboration; reporting to local and central government must not be one-sided, instead data providers should receive some tangible benefit for their engagement; the focus of analysis needs to shift from statistics toward evaluation into the effectiveness of interventions; and access must be available to a range of sector actors, including service providers and academia. Importantly, the paper also concludes that in delivering the ideal system, care must be taken not to interrupt the delivery of effective homelessness interventions.

scholarly journals The use of biodiversity data in spatial planning and impact assessment in Europe

2018 ◽  
Vol 4 ◽  
pp. e28045 ◽  
Author(s):  
Evelyn Underwood ◽  
Katie Taylor ◽  
Graham Tucker
Keyword(s):  
Data Collection ◽  
Impact Assessment ◽  
Local Governments ◽  
Spatial Planning ◽  
Data Access ◽  
Individual Development ◽  
Federal State ◽  
Biodiversity Data ◽  
Existing Data ◽  
The Eu

This review identifies successful approaches to collating and using biodiversity data in spatial planning and impact assessment, the barriers to obtaining and using existing data sources, and the key data gaps that hinder effective implementation. The analysis is a contribution to the EU BON project funded by the European Commission FP7 research programme, which aimed to identify and pilot new approaches to overcome gaps in biodiversity data in conservation policy at European and national levels. The consideration of biodiversity in impact assessments and spatial planning requires spatially explicit biodiversity data of various types. Where spatial plans take account of biodiversity, there are opportunities through Strategic Environmental Assessment (SEA) of development plans and Environmental Impact Assessment (EIA) of individual development proposals to ensure that consented activities are consistent with no net loss of biodiversity or even a net gain, and help to maintain or develop coherent ecological networks. However, biodiversity components of SEAs and EIAs have often been found to be of insufficient quality due to the lack of data or the inadequate use of existing data. Key obstacles to providing access to biodiversity data include the need for data standardisation and data quality governance and systems, licensing approaches to increase data access, and lack of resources to target gaps in data coverage and to develop and advertise policy-relevant data products. Existing data platforms differ in the degree to which they successfully provide a service to spatial planners and impact assessment practitioners. Some local governments, for example Somerset County Council in the UK and the Bremen federal state in Germany, have invested in integrated data collection and management systems that now provide intensively used tools for spatial planning and impact assessment informed by local data collection and monitoring. The EU BON biodiversity data portal aims to provide a platform that is an access point to datasets relevant to essential biodiversity variables on species, habitats and ecosystems. The EU BON taxonomic backbone provides an integrated search function for species and taxa according to different classifications, and also provides a range of tools for data analysis and decision-support. This will increase the accessibility of the vast range of biodiversity data available in different sources and allow the targeting of future data collection to address current gaps.

COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Dataset of Anti-vaccine Content, Vaccine Misinformation and Conspiracies (Preprint)

2021 ◽  
Author(s):  
Goran Muric ◽  
Yusong Wu ◽  
Emilio Ferrara
Keyword(s):  
Social Media ◽  
Data Collection ◽  
Herd Immunity ◽  
Application Programming Interface ◽  
Data Access ◽  
Vaccine Hesitancy ◽  
Global Public Health ◽  
News Sources ◽  
Online Social Media ◽  
Vaccination Campaigns

BACKGROUND False claims about COVID-19 vaccines can undermine public trust in ongoing vaccination campaigns, thus posing a threat to global public health. Misinformation originating from various sources has been spreading online since the beginning of the COVID-19 pandemic. Anti-vaccine activists have also begun to utilize platforms like Twitter to share their views. To properly understand the phenomenon of vaccine hesitancy through the lens of online social media, it is of greatest importance to gather the relevant data. OBJECTIVE In this paper, we describe a dataset of Twitter posts that exhibit a strong anti-vaccine stance. The dataset is made available to the research community via our AvaxTweets dataset GitHub repository. METHODS We started the ongoing data collection on October 18, 2020, leveraging the Twitter streaming application programming interface (API) to follow a set of specific anti-vaccine related keywords. Additionally, we collect the historical tweets of the set of accounts that engaged in spreading anti-vaccination narratives at some point during 2020. RESULTS Since the inception of our collection, we have published two collections: a) a streaming keyword-centered data collection with more than 1.8 million tweets, and b) a historical account-level collection with more than 135 million tweets. In this paper we present descriptive analyses showing the volume of activity over time, geographical distributions, topics, news sources, and inferred accounts’ political leaning. CONCLUSIONS The vaccine-related misinformation on social media may exacerbate the levels of vaccine hesitancy, hampering the progress toward vaccine-induced herd immunity, and potentially increase infections related to new COVID-19 variants. For these reasons, understanding vaccine hesitancy through the lens of social media is of paramount importance. Since data access is the first obstacle to attain that, we publish the dataset that can be used in studying anti-vaccine misinformation on social media and enable a better understanding of vaccine hesitancy.

scholarly journals Study profile: the Durban Diabetes Study (DDS): a platform for chronic disease research

2016 ◽  
Vol 1 ◽  
Author(s):  
T. R. Hird ◽  
E. H. Young ◽  
F. J. Pirie ◽  
J. Riha ◽  
T. M. Esterhuizen ◽  
...  
Keyword(s):  
Chronic Disease ◽  
Data Collection ◽  
Black Population ◽  
Data Access ◽  
Population Based ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Health Lifestyle ◽  
Disease Research ◽  
Processing And Storage

The Durban Diabetes Study (DDS) is a population-based cross-sectional survey of an urban black population in the eThekwini Municipality (city of Durban) in South Africa. The survey combines health, lifestyle and socioeconomic questionnaire data with standardised biophysical measurements, biomarkers for non-communicable and infectious diseases, and genetic data. Data collection for the study is currently underway and the target sample size is 10 000 participants. The DDS has an established infrastructure for survey fieldwork, data collection and management, sample processing and storage, managed data sharing and consent for re-approaching participants, which can be utilised for further research studies. As such, the DDS represents a rich platform for investigating the distribution, interrelation and aetiology of chronic diseases and their risk factors, which is critical for developing health care policies for disease management and prevention. For data access enquiries please contact the African Partnership for Chronic Disease Research (APCDR) at [email protected] or the corresponding author.

scholarly journals Controlled Sharing Mechanism of Data Based on the Consortium Blockchain

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jin Li ◽  
Songqi Wu ◽  
Yundan Yang ◽  
Fenghui Duan ◽  
Hui Lu ◽  
...  
Keyword(s):  
Data Storage ◽  
Data Flow ◽  
Electric Energy ◽  
Electrical Energy ◽  
Data Access ◽  
Original Data ◽  
Third Party ◽  
Data Accessibility ◽  
Energy Data ◽  
Data Access Control

In the process of sharing data, the costless replication of electric energy data leads to the problem of uncontrolled data and the difficulty of third-party access verification. This paper proposes a controlled sharing mechanism of data based on the consortium blockchain. The data flow range is controlled by the data isolation mechanism between channels provided by the consortium blockchain by constructing a data storage consortium chain to achieve trusted data storage, combining attribute-based encryption to complete data access control and meet the demands for granular data accessibility control and secure sharing; the data flow transfer ledger is built to record the original data life cycle management and effectively record the data transfer process of each data controller. Taking the application scenario of electric energy data sharing as an example, the scheme is designed and simulated on the Linux system and Hyperledger Fabric. Experimental results have verified that the mechanism can effectively control the scope of access to electrical energy data and realize the control of the data by the data owner.

scholarly journals Disparities in Students’ Propensity to Consent to Learning Analytics

2020 ◽  
Author(s):  
Warren Li ◽  
Kaiwen Sun ◽  
Florian Schaub ◽  
Christopher Brooks
Keyword(s):  
Data Collection ◽  
University Students ◽  
Predictive Models ◽  
Learning Analytics ◽  
Online Survey ◽  
Data Access ◽  
Institutional Trust ◽  
Perceived Benefits ◽  
Opt Out ◽  
And Gender

Use of university students’ educational data for learning analytics has spurred a debate about whether and how to provide students with agency regarding data collection and use. A concern is that students opting out of learning analytics may skew predictive models, in particular if certain student populations disproportionately opt out and biases are unintentionally introduced into predictive models. We investigated university students’ propensity to consent to learning analytics through an email prompt, and collected respondents’ perceived benefits and privacy concerns regarding learning analytics in a subsequent online survey. In particular, we studied whether and why students’ consent propensity differs among student subpopulations by sending our email prompt to a sample of 4,000 students at our institution stratified by ethnicity and gender. 272 students interacted with the email, of which 119 completed the survey. We identified that institutional trust, concerns with the amount of data collection versus perceived benefits, and comfort with instructors’ data access were key determinants in students’ decision to participate in learning analytics. We find that students identifying ethnically as Black were significantly less likely to respond and self-reported lower levels of institutional trust. Female students reported concerns with data collection but were also more comfortable with use of their data by instructors . Students’ comments corroborate these findings and we discuss the implications of these concerns on educational data collection.

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