Method of Data Collection in Requirement Analysis Phase Based on ERP

2013 ◽  
Vol 416-417 ◽  
pp. 1495-1501
Author(s):  
Wei Yao
Keyword(s):  
Data Collection ◽  
New Method ◽  
Requirement Analysis ◽  
Theory Problem ◽  
Quality Of Data ◽  
Analysis Phase ◽  
Enterprise Informationization ◽  
Practice Problem

During the process of implementing enterprise informationization, the main content is the system of ERP, collecting data is very important in the preliminary requirement analysis stage. Data collection is not only theory problem, but also practice problem. In this paper, the author focuses on the topic research, aiming at some problems existing in the process of data collection, and puts forward the new method of data collection, trying to improve the quality of data collection.

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 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.

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.

scholarly journals MAD data collection – current trends

1999 ◽  
Vol 55 (10) ◽  
pp. 1726-1732 ◽  
Author(s):  
Martin A. Walsh ◽  
Gwyndaf Evans ◽  
Ruslan Sanishvili ◽  
Irene Dementieva ◽  
Andrzej Joachimiak
Keyword(s):  
Protein Structure ◽  
Data Collection ◽  
Structure Determination ◽  
Protein Structure Determination ◽  
Quality Of Data ◽  
Charge Coupled Device ◽  
Current Trends ◽  
Recent Developments ◽  
Widespread Availability

The multiwavelength anomalous dispersion (MAD) method of protein structure determination is becoming a routine technique in protein crystallography. The increased number of wavelength-tuneable synchrotron beamlines capable of performing challenging MAD experiments, coupled with the widespread availability of charge-coupled device (CCD) based X-ray detectors with fast read-out times have brought MAD structure determination to a new exciting level. Ultrafast MAD data collection is now possible and, with the widespread use of selenium in the form of selenomethionine for phase determination, the method is growing in popularity. Recent developments in crystallographic software are complementing the above advances, paving the way for rapid protein structure determination. An overview of a typical MAD experiment is described, with emphasis on the rates and quality of data acquisition now achievable at third-generation synchrotron sources.

scholarly journals Efficiency and Quality of Data Collection Among Public Mental Health Surveys Conducted During the COVID-19 Pandemic: Systematic Review

10.2196/25118 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e25118
Author(s):  
Yu-Hsuan Lin ◽  
Chung-Yen Chen ◽  
Shiow-Ing Wu
Keyword(s):  
Mental Health ◽  
Systematic Review ◽  
Data Collection ◽  
Health Surveys ◽  
Public Mental Health ◽  
World Health ◽  
Quality Of Data ◽  
Newcastle Ottawa Scale ◽  
Ottawa Scale

Background The World Health Organization has recognized the importance of assessing population-level mental health during the COVID-19 pandemic. During a global crisis such as the COVID-19 pandemic, a timely surveillance method is urgently needed to track the impact on public mental health. Objective This brief systematic review focused on the efficiency and quality of data collection of studies conducted during the COVID-19 pandemic. Methods We searched the PubMed database using the following search strings: ((COVID-19) OR (SARS-CoV-2)) AND ((Mental health) OR (psychological) OR (psychiatry)). We screened the titles, abstracts, and texts of the published papers to exclude irrelevant studies. We used the Newcastle-Ottawa Scale to evaluate the quality of each research paper. Results Our search yielded 37 relevant mental health surveys of the general public that were conducted during the COVID-19 pandemic, as of July 10, 2020. All these public mental health surveys were cross-sectional in design, and the journals efficiently made these articles available online in an average of 18.7 (range 1-64) days from the date they were received. The average duration of recruitment periods was 9.2 (range 2-35) days, and the average sample size was 5137 (range 100-56,679). However, 73% (27/37) of the selected studies had Newcastle-Ottawa Scale scores of <3 points, which suggests that these studies are of very low quality for inclusion in a meta-analysis. Conclusions The studies examined in this systematic review used an efficient data collection method, but there was a high risk of bias, in general, among the existing public mental health surveys. Therefore, following recommendations to avoid selection bias, or employing novel methodologies considering both a longitudinal design and high temporal resolution, would help provide a strong basis for the formation of national mental health policies.

Computer-Assisted Elaboration of Job Histories

1990 ◽  
Vol 29 (02) ◽  
pp. 146-152 ◽  
Author(s):  
A. Mouaddib ◽  
P. Robaux ◽  
J.M. Martin
Keyword(s):  
Data Collection ◽  
Occupational Physician ◽  
Previous Article ◽  
Computer Assisted ◽  
Medical Surveillance ◽  
Quality Of Data ◽  
Job Exposure Matrix

AbstractThree ways are proposed to help the occupational physician in constructing a worker’s job history or Curriculum Laboris (CL) with a PC. The quality and, therefore, the usefulness of any job history is greatly conditioned by the method and quality of data collection. The Curriculum Laboris method explained in a previous article has been briefly summarized as a basis of departure. Then, the workers who were submitted to special medical surveillance were considered. After this, the scrolling menu technique was applied in the elaboration of a job history. Finally, the authors show how the representation of company organization by means of a job exposure matrix (JEM) can help to efficiently elaborate job histories.

Best data handling practices in water-related research

2013 ◽  
Vol 8 (3-4) ◽  
pp. 390-398
Author(s):  
H. Sonnenberg ◽  
M. Rustler ◽  
M. Riechel ◽  
N. Caradot ◽  
P. Rouault ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Processing ◽  
Point Of View ◽  
Data Handling ◽  
Quality Of Data ◽  
Related Research ◽  
Long Run ◽  
Handling Practices

Data play an important role in water-related research. Based on experiences in data collection and data processing in water-related research this paper proposes – both from a computer scientist's and an environmental engineer's point of view – a set of rules for data handling: Rule 1: Protect raw data; Rule 2: Save metadata; Rule 3: Use databases; Rule 4: Separate data from processing; Rule 5: Use programming; Rule 6: Avoid redundancy; Rule 7: Be transparent; Rule 8: Use standards and naming conventions. Applying these rules (i) increases the quality of data and results, (ii) allows to prepare data for long-term usage and make data accessible to different people, (iii) makes data processing transparent and results reproducible, and (iv) saves – at least in the long run – time and effort. With this contribution the authors would like to start a discussion about best data handling practices and present a first checklist of data handling and data processing for practitioners and researchers working in the water sector.

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