COUNTER and Non-COUNTER

2017 ◽  
pp. 73-96
Keyword(s):  
Depth Information ◽  
Unique Data ◽  
Single Vendor ◽  
Counter Data ◽  
User Activities ◽  
Usage Data

While ebrary, EBSCO, and Safari have implemented COUNTER reports to supply customers with standardized usage data, their “coexisting” non-COUNTER reports offer unique and in-depth information on user activities. Therefore, librarians should explore the benefits of both data and find strategies to overcome the inconsistencies and fill the gaps. Chapter 6 evaluates vendor COUNTER and non-COUNTER data in a larger context. It explores the possibility and feasibility to consolidate useful data from vendor COUNTER and non-COUNTER reports and discusses how to overcome the disparities and fill the gaps among the usage data from different vendors. The chapter focuses on the following basic questions: 1) What unique data from each vendor are significant? 2) Is it feasible to consolidate COUNTER and non-COUNTER usage data provided by a single vendor? 3) Can the differences between COUNTER and non-COUNTER data be reconciled?

Case Study Part 3

2017 ◽  
pp. 55-72
Keyword(s):  
The Third ◽  
Unique Data ◽  
Counter Data

In addition to their COUNTER reports, ebrary, EBSCO, and Safari provide their own custom reports. Vendors' non-COUNTER data, which are unique and more detailed, provide librarians with a potentially deeper perspective of overall usage. Chapter 5, the third part of the case study, evaluates vendor non-COUNTER reports against the same principles; it examines in detail what unique data these “local” reports provide, as well as identifies potential issues in interpreting these reports. The chapter addresses several issues and questions while exploring the uniqueness of the data contributed by the non-COUNTER reports. The chapter also looks at the challenge of comparing variant terminology used to describe data categories among the vendors, and whether, despite their differences, the non-COUNTER data are compatible with COUNTER data. The goal of this chapter is to help the customer sort out the data, interpret their meaning, and find the value of each report.

What Is COUNTER and Why Is It Important?

2017 ◽  
pp. 1-9
Keyword(s):  
Electronic Resources ◽  
Code Of Practice ◽  
Important Purpose ◽  
User Activities ◽  
Usage Data

Electronic usage data serves an important purpose for librarians who need to assess user activities with electronic collections. Comparing usage reports by different vendors requires sorting out the various types of reports that are available and assessing how and if they can effectively be compared. This book attempts to investigate what makes vendor usage reports compatible or incompatible, and to what degree. It includes a case study where the authors analyze and interpret their institution's data in order to provide others with possible strategies for productively engaging with e-book usage reports. Chapter 1 gives a brief account on Project COUNTER (Counting Networked Electronic Resources). COUNTER supports the process of collection assessment by providing standards for vendors and publishers to follow in delivering usage data to libraries. The COUNTER Code of Practice aims for usage data to be credible, consistent, and comparable, three core principles. This chapter describes the purpose of COUNTER, its underlying principles and core standards, and more importantly, who will benefit from the COUNTER standards.

Case Study Part 1

2017 ◽  
pp. 20-29
Keyword(s):  
Academic Library ◽  
Current Implementation ◽  
Academic Year ◽  
Counter Data ◽  
Usage Data

Chapter 3 introduces a case study, which involves a medium-sized academic library that has been acquiring e-books primarily through large subscription packages from three major vendors. All three vendors in this case study – ebrary, EBSCO, and Safari – provide COUNTER usage reports to their customers. All three vendors have joined the COUNTER membership and been registered as COUNTER-compliant. The chapter describes their current implementation of the COUNTER book reports. The usage reports discussed throughout the case study were retrieved from each vendor for the academic year of July 2015–June 2016, and include COUNTER and non-COUNTER reports. The chapter also identifies what COUNTER reports each vendor provides and evaluates the degree of their compliance. Despite the variations in the COUNTER reports they implement, all three vendors supply their customers with essential COUNTER data on e-books usage, i.e. the numbers of successful requests, turnaways, and searches. In addition to the COUNTER reports, they all provide non-COUNTER reports to their customers. Although the number of non-COUNTER reports vary widely among ebrary, EBSCO, and Safari, all three vendors provide abundant and unique usage data.

scholarly journals New usage reports, new insights! How to use your COUNTER data in decision making processes

2020 ◽  
Author(s):  
Athena Hoeppner ◽  
Sonja Lendi ◽  
Kornelia Junge
Keyword(s):  
Decision Making ◽  
Decision Making Process ◽  
Microsoft Excel ◽  
Code Of Practice ◽  
Decision Making Processes ◽  
Total Usage ◽  
Insight Into ◽  
Counter Data ◽  
Usage Data

Librarians have been receiving COUNTER Release 5 reports since February 2019 and are becoming familiar with the new robust usage data. In this paper three experts explain how the new usage reports provide greater clarity and how they give insight into users’ actions. Athena Hoeppner outlines the new reports and metrics and explains how to interpret book usage data and how to use the data effectively in decision making process. Sonja Lendi focuses on journal usage data and the differences between Release 4 and Release 5 of the COUNTER Code of Practice. She also explains Distributed Usage Logging (DUL). This protocol enables publishers to capture traditional usage activity related to their content that happens on sites other than their own so they can provide reports of “total usage” regardless of where that usage happens. Kornelia Junge explains how librarians can use Microsoft Excel to analyse usage.

Painting a Spring 2020 library portrait with collection usage data

2020 ◽  
Author(s):  
Rebecca Lubas ◽  
Sydney Thompson ◽  
Lauren Wittek
Keyword(s):  
Usage Data

Financial Statement Ratio Analysis to Predict Bankruptcy nn Company Registered in BEI - Jakarta (Altman Z-Score Method and Zmijewski)

2020 ◽  
Vol 3 (3) ◽  
pp. 122
Author(s):  
Andi Silvan
Keyword(s):  
Financial Distress ◽  
Stock Exchange ◽  
Good Condition ◽  
Financial Statement ◽  
Depth Information ◽  
Z Score ◽  
Industrial Manufacturing ◽  
Financial Difficulties ◽  
A Value ◽  
Score Method

AbstractThis study takes the topic of predicting corporate bankruptcies. This research dqlam use traditional methods Altman Z-Score and Zmijewski. The purpose of this study was to obtain in-depth information about predicting bankruptcy of companies that are not necessarily directly to bankruptcy, but there is financial distress.Based on the results of research conducted on the four (4) non industrial manufacturing company listed on the Indonesia Stock Exchange (BEI). Obtaining the value z-score represents the average company are in good condition, which means no financial distress. Acquisition value of x-score has a value of less than 0 (zero) which means that the company is in good condition and is predicted not experiencing financial difficulties. This study led to the conclusion that the Altman Z-Score and Zmijewski method can be used to predict corporate bankruptcy. Keywords: Financial Ratios, Bankruptcy, Company.

Sign in / Sign up

Export Citation Format

Share Document