Data Integrity Report Sends Journals Back to the Drawing Board

Science ◽  
2009 ◽  
Vol 325 (5939) ◽  
pp. 381-381 ◽  
Author(s):  
Jocelyn Kaiser
Keyword(s):  
Data Integrity ◽  
Drawing Board

Sovietica in English: in the Press, in the Works, on the Drawing Board

1966 ◽  
Vol 11 (8) ◽  
pp. 382-383 ◽  
Author(s):  
JOSEF BROŽEK ◽  
JIŘÍ HOSKOVEC
Keyword(s):  
The Press ◽  
Drawing Board

Data Integrity Techniques in Cloud Computing: An Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 121 ◽  
Author(s):  
Neha Thakur ◽  
Aman Kumar Sharma
Keyword(s):  
Cloud Computing ◽  
Cloud Storage ◽  
Data Integrity ◽  
Advantages And Disadvantages ◽  
Security Models ◽  
Cloud Storage Service ◽  
Storage Service ◽  
Cloud Users ◽  
It Enterprises

Cloud computing has been envisioned as the definite and concerning solution to the rising storage costs of IT Enterprises. There are many cloud computing initiatives from IT giants such as Google, Amazon, Microsoft, IBM. Integrity monitoring is essential in cloud storage for the same reasons that data integrity is critical for any data centre. Data integrity is defined as the accuracy and consistency of stored data, in absence of any alteration to the data between two updates of a file or record.  In order to ensure the integrity and availability of data in Cloud and enforce the quality of cloud storage service, efficient methods that enable on-demand data correctness verification on behalf of cloud users have to be designed. To overcome data integrity problem, many techniques are proposed under different systems and security models. This paper will focus on some of the integrity proving techniques in detail along with their advantages and disadvantages.

"Do not Worry Your Head": The Impossibility of Indigenising Social Work Education and Practice in Africa

2018 ◽  
Vol 30 (3) ◽  
Author(s):  
Kwaku Osei-Hwedie ◽  
Doris Akyere Boateng
Keyword(s):  
Social Work ◽  
Knowledge Base ◽  
North American ◽  
Social Work Education ◽  
Culturally Relevant ◽  
Social Work Educators ◽  
The People ◽  
African Context ◽  
Drawing Board ◽  
Work Education

As the discussions and debates rage on about the content and direction of social work in Africa, the challenges associated with weaning the profession off its Western and North American roots become apparent. The desire to indigenise or make the profession culturally relevant is well articulated in the literature. Some efforts have been undertaken toward achieving this desire. However, it is evident that despite the numerous discussions and publications, it appears that efforts at indigenising, localising, or making social work culturally relevant have not made much progress. While what must be achieved is somewhat clear; how to achieve it and by what process remain a conundrum. The article, therefore, revisits the issue of making social work culturally relevant in Africa and its associated challenges. Despite the indictment of current social work education and practice in Africa, it appears that many academics and professionals have accepted that what is Western is global, fashionable, and functional, if not perfect. Given this, perhaps, “we should not worry our heads” about changing it. Instead, social work educators and practitioners in Africa should go back to the drawing board to determine how current social work education and practice can be blended with a traditional African knowledge base, approaches and models to reflect and align with the critical principles and ideals within the African context. This is with the hope of making the profession more relevant to the needs of the people of Africa.

Privacy Preserving Data Integrity Verification in Cloud

2014 ◽  
Vol 1 (2) ◽  
pp. 25-31
Author(s):  
T. Subha ◽  
◽  
S. Jayashri ◽  
Keyword(s):  
Data Integrity ◽  
Privacy Preserving ◽  
Integrity Verification

Towards guaranteed data-integrity: A method of preventing Questionable Research Practices

2018 ◽  
Author(s):  
Dick Bierman ◽  
Jacob Jolij
Keyword(s):  
Real Time ◽  
Open Data ◽  
Data Integrity ◽  
Drop Out ◽  
Research Practices ◽  
Major Aspect ◽  
Questionable Research Practices ◽  
Blockchain Technology ◽  
Integrity System ◽  
Secure Server

We have tested the feasibility of a method to prevent the occurrence of so-called Questionable Research Practices (QRP). A part from embedded pre-registration the major aspect of the system is real-time uploading of data on a secure server. We outline the method, discuss the drop-out treatment and compare it to the Born-open data method, and report on our preliminary experiences. We also discuss the extension of the data-integrity system from secure server to use of blockchain technology.

Need and Importance of Healthcare Data Integrity

2019 ◽  
Vol 11 (4) ◽  
pp. 854-859 ◽  
Author(s):  
Alka Agrawal ◽  
Nawaf Rasheed Alharbe
Keyword(s):  
Data Integrity ◽  
Healthcare Data

A feature-based hybrid recommender system for risk prediction : Machine learning approach (Preprint)

2020 ◽  
Author(s):  
Uzair Bhatti
Keyword(s):  
Machine Learning ◽  
Risk Prediction ◽  
Predictive Accuracy ◽  
Correct Diagnosis ◽  
Recommendation Systems ◽  
Data Integrity ◽  
Machine Learning Algorithms ◽  
Patient Counseling ◽  
Hybrid Filtering ◽  
Novel Algorithm

BACKGROUND In the era of health informatics, exponential growth of information generated by health information systems and healthcare organizations demands expert and intelligent recommendation systems. It has become one of the most valuable tools as it reduces problems such as information overload while selecting and suggesting doctors, hospitals, medicine, diagnosis etc according to patients’ interests. OBJECTIVE Recommendation uses Hybrid Filtering as one of the most popular approaches, but the major limitations of this approach are selectivity and data integrity issues.Mostly existing recommendation systems & risk prediction algorithms focus on a single domain, on the other end cross-domain hybrid filtering is able to alleviate the degree of selectivity and data integrity problems to a better extent. METHODS We propose a novel algorithm for recommendation & predictive model using KNN algorithm with machine learning algorithms and artificial intelligence (AI). We find the factors that directly impact on diseases and propose an approach for predicting the correct diagnosis of different diseases. We have constructed a series of models with good reliability for predicting different surgery complications and identified several novel clinical associations. We proposed a novel algorithm pr-KNN to use KNN for prediction and recommendation of diseases RESULTS Beside that we compared the performance of our algorithm with other machine algorithms and found better performance of our algorithm, with predictive accuracy improving by +3.61%. CONCLUSIONS The potential to directly integrate these predictive tools into EHRs may enable personalized medicine and decision-making at the point of care for patient counseling and as a teaching tool. CLINICALTRIAL dataset for the trials of patient attached

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