scholarly journals Qualitative Collaborative Sensing In Smart Phone Based Wireless Sensor Networks

2019 ◽  
Vol 8 (3S2) ◽  
pp. 476-479
Keyword(s):  
Data Collection ◽  
Qualitative Data ◽  
Smart Phone ◽  
Estimation Algorithm ◽  
Quality Of Data ◽  
Anomalous Data ◽  
Novel Approach ◽  
Collaborative Sensing ◽  
Human Participants

Collaborative sensing has become a novel approach for smart phone based data collection. In this process individuals contributes to the participatory data collection by sharing the data collected using their smart phone sensors. Since the data is gathered by human participants it is difficult to guarantee the Quality of the data received. Mobility of the participant and accuracy of the sensor also matters for the quality of data shared in such environment. If the data shared by such participants are of low quality the purpose of collaborative sensing fails. So there must be approach to gather good quality of data from participants. In this paper we propose a Truth Estimation Algorithm (TEA) to identify the truth value of the data received and filter out anomalous data items to improve the quality of data. To encourage the participants to share quality information we also propose an Incentive Allocation Algorithm (IAA) for qualitative data collection.

scholarly journals Literature Review for the Type of Interview in Qualitative Research

2017 ◽  
Vol 9 (3) ◽  
pp. 86 ◽  
Author(s):  
Essa Ali R Adhabi ◽  
Christina B Lash Anozie
Keyword(s):  
Data Collection ◽  
Technology Implementation ◽  
Qualitative Data ◽  
Subject Area ◽  
Professional Issues ◽  
Quality Of Data ◽  
Face To Face ◽  
The Subject ◽  
Qualitative Data Collection

In carrying out qualitative studies, the important issue is the quality of data collected, which is dependent on the mode of data collection used. The interview is one of the data collection techniques for qualitative researchers. Distinct from other methods, interviews have unique features that make them superior. As such, the current study explores relevant issues that are linked to interviews, especially aspects that make them central to qualitative data collection. Besides the historical appeal, the discussion covers the advantages a researcher experiences while using interviews to collect data. They require a personal commitment of both the participant and researcher. Significantly, time and resource allocation are also required. With the emerging technology, implementation of the interview process is becoming flexible thus moving away from the rigid face to face mode. Besides their strengths, there are also challenges and ethical dilemmas that are linked to interviews. As a perfect qualitative data collection method, researchers have professional issues that they have a deal with throughout the process. The link between all these issues is the subject area of the current discussion, which tackles each factor separately.

scholarly journals Improving Specimen Labelling and Data Collection in Bio-science Research using Mobile and Web Applications

2020 ◽  
Vol 10 (1) ◽  
pp. 1-16
Author(s):  
Isaac Nyabisa Oteyo ◽  
Mary Esther Muyoka Toili
Keyword(s):  
Data Collection ◽  
Web Applications ◽  
Data Entry ◽  
Science Research ◽  
Quality Data ◽  
Quality Of Data ◽  
Data Entry Form ◽  
Research Activities ◽  
Daunting Task

AbstractResearchers in bio-sciences are increasingly harnessing technology to improve processes that were traditionally pegged on pen-and-paper and highly manual. The pen-and-paper approach is used mainly to record and capture data from experiment sites. This method is typically slow and prone to errors. Also, bio-science research activities are often undertaken in remote and distributed locations. Timeliness and quality of data collected are essential. The manual method is slow to collect quality data and relay it in a timely manner. Capturing data manually and relaying it in real time is a daunting task. The data collected has to be associated to respective specimens (objects or plants). In this paper, we seek to improve specimen labelling and data collection guided by the following questions; (1) How can data collection in bio-science research be improved? (2) How can specimen labelling be improved in bio-science research activities? We present WebLog, an application that we prototyped to aid researchers generate specimen labels and collect data from experiment sites. We use the application to convert the object (specimen) identifiers into quick response (QR) codes and use them to label the specimens. Once a specimen label is successfully scanned, the application automatically invokes the data entry form. The collected data is immediately sent to the server in electronic form for analysis.

scholarly journals Civil Service Recruitment: Problems Connected With Federal Character Policy in Nigeria

2014 ◽  
Vol 4 (2) ◽  
pp. 12
Author(s):  
Emmanuel Jude Abiodun Akinwale
Keyword(s):  
Data Collection ◽  
Qualitative Data ◽  
Civil Service ◽  
National Policy ◽  
State Representation ◽  
National Integration ◽  
Federal Civil Service ◽  
Qualitative Data Collection

The purpose of this paper is to assess the extent of relevance of the federal character as a national policy in recruitment into the Nigerian federal civil service and probe whether or not the level of application of merit supersedes the application of ecological considerations in recruitment into the service. It utilizes quantitative and qualitative data collection to espouse its theme. The paper finds that there are personnel problems connected with poor application of federal character policy in recruitment into the civil service and this affects the quality of entrants. It recommends strict application of merit standard to attract best workers while implementing federal character policy through proven certification of state of applicants. The paper notes that the Nigerian federal character policy is one that places premium on state representation in governance and bureaucracy and a strategy for national integration. However, there must be predominant application of merit in recruitment.

scholarly journals Elicitation as a Multilayered Process

2018 ◽  
Vol 17 (1) ◽  
pp. 160940691879701 ◽  
Author(s):  
Emma K. Tsui ◽  
Emily Franzosa
Keyword(s):  
Data Collection ◽  
Individual Interview ◽  
Novel Approach ◽  
Shared Authority ◽  
Interview Training ◽  
The Individual ◽  
The Way

This article describes a novel approach to reciprocal peer interviewing in which participants interview one another sequentially, allowing each the space of a full interview to articulate her experiences and reflections. This structure of data collection offers a new conceptualization of the way that elicitation functions; not just as a process inside of an interview, but one that is also shaped by factors preceding and outside of the individual interview, a process we call “meta-elicitation.” We argue that this form of reciprocal peer interviewing offers a view of the emic that is both participant-led and uniquely balanced between collective and individual perspectives. However, we also argue that shared authority and rapport are actively, and not always successfully, negotiated in such interviews. To prepare participants for peer interviewing, we hosted a 1-day workshop involving interview training, planning, and the recording of interviews. To maximize quality of such projects, we recommend that external researchers consider carefully (1) the balance of structure and flexibility in designing the workshop and interviews, (2) thorough preparation of participants, and (3) the role of meta-elicitation dynamics during analysis.

scholarly journals The Quality of Data Collection by an Interview on the Prevalence of Elder Mistreatment

2000 ◽  
Vol 12 (1) ◽  
pp. 57-72 ◽  
Author(s):  
Hannie C. Comijs ◽  
Wil Dijkstra ◽  
Lex M. Bouter ◽  
Johannes H. Smit
Keyword(s):  
Data Collection ◽  
Quality Of Data ◽  
Elder Mistreatment

Abstract B33: Who am I? Improving quality of data collection for race/ethnicity and language

2015 ◽  
Author(s):  
Paula Aristizabal ◽  
Foyinsola Ani ◽  
Erica Del Muro ◽  
Teresa Cassidy ◽  
William Roberts ◽  
...  
Keyword(s):  
Data Collection ◽  
Quality Of Data ◽  
Race Ethnicity

scholarly journals Quality based drip drag match data collection in wireless sensor network

2017 ◽  
Vol 7 (1.1) ◽  
pp. 426
Author(s):  
V Jayaraj ◽  
S Alonshia
Keyword(s):  
Wireless Sensor Network ◽  
Data Collection ◽  
Sensor Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Data Loss ◽  
Quality Of Data ◽  
Deterministic Models ◽  
Multiple Data

Although data collection has received much attention by effectively minimizing delay, computational complexity and increasing the total data transmitted, the transience of sensor nodes for multiple data collection of sensed node in wireless sensor network (WSN) renders quality of service a great challenge. To circumvent transience of sensor nodes for multiple data collection, Quality based Drip-Drag-Match Data Collection (QDDM-DC) scheme have been proposed. In Drip-Drag-Match data collection scheme, initially dripping of data is done on the sink by applying Equidistant-based Optimum Communication Path from the sensor nodes which reduces the data loss. Next the drag operation pulls out the required sensed data using Neighbourhood-based model from multiple locations to reduce the delay for storage. Finally, the matching operation, compares the sensed data received by the dragging operation to that of the corresponding sender sensor node (drip stage) and stores the sensed data accurately which in turn improves the throughput and quality of data collection. Simulation is carried for the QDDM-DC scheme with multiple scenarios (size of data, number of sinks, storage capacity) in WSN with both random and deterministic models. Simulation results show that QDDM-DC provides better performance than other data collection schemes, especially with high throughput, ensuring minimum delay and data loss for effective multiple data collection of sensed data in WSN.

A novel approach to provenance management for privacy preservation

2019 ◽  
Vol 46 (2) ◽  
pp. 147-160
Author(s):  
Ozgu Can ◽  
Dilek Yilmazer
Keyword(s):  
Privacy Preservation ◽  
Quality Of Data ◽  
Semantic Web Technologies ◽  
Provenance Information ◽  
Web Technologies ◽  
Provenance Management ◽  
Novel Approach ◽  
Provenance Data ◽  
Domain Independent

Provenance determines the origin of the data by tracing and recording the actions that are performed on the data. Therefore, provenance is used in many fields to ensure the reliability and quality of data. In this work, provenance information is used to meet the security needs in information systems. For this purpose, a domain-independent provenance model is proposed. The proposed provenance model is based on the Open Provenance Model and Semantic Web technologies. The goal of the proposed provenance model is to integrate the provenance and security concepts in order to detect privacy violations by querying the provenance data. In order to evaluate the proposed provenance model, we illustrated our domain-independent model by integrating it with an infectious disease domain and implemented the Healthcare Provenance Information System.

Out-of-Hospital Violence Injury Surveillance: Quality of Data Collection

1999 ◽  
Vol 34 (6) ◽  
pp. 745-750 ◽  
Author(s):  
Lynn A Boergerhoff ◽  
Susan Goodwin Gerberich ◽  
Aparna Anderson ◽  
Laura Kochevar ◽  
Lance Waller
Keyword(s):  
Data Collection ◽  
Injury Surveillance ◽  
Quality Of Data ◽  
Hospital Violence

Unlocking Data Collection: Practical Tips for School Nurses

2017 ◽  
Vol 33 (1) ◽  
pp. 27-28 ◽  
Author(s):  
Angela M. Lepkowski
Keyword(s):  
Data Collection ◽  
Evidence Based Practice ◽  
School Nurses ◽  
Health Data ◽  
Evidence Based ◽  
Quality Of Data ◽  
Specific Health

School nurses contend with a variety of challenges related to collecting and using their own data. Seemingly small steps can be taken to overcome these challenges, which will result in significant improvements in data collection and use. Improving the quality of data collection assists school nurses to identify and define practice issues and guide implementation of evidence-based practice within their schools and districts. This article provides school nurses with practical steps to collect and use school or district specific health data.

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