Managing a Comprehensive Cost-per-Use Project in a Large Academic Library

Abstract

This article reports the findings of a study to evaluate title-level cost per use (CPU) for most recurring resources at The University of Alabama Libraries. The study relied on cost and usage data from FY2016. The purpose of the project was to identify titles for cancellation and to establish CPU for most titles ordered individually and those within packages for use in future contract negotiations. The analysis began with a list of all recurring titles having a cost of $1,000 or more. The review involved 1,035 titles with an initial objective to highlight all titles with a CPU of $200 or more. Titles exceeding this CPU threshold received additional review, with several identified for cancellation. This article presents the findings and provides an overview of the process used to collect CPU on a large scale. The article outlines some of the challenges faced by libraries when trying to align data that are stored in various silos and the limitations for various platforms and software used when collecting CPU data. The findings from this study formed the basis for a LibGuide developed and used to raise awareness of the need for cancellations. The data also assisted with future contract negotiations.     

Using EZproxy and Google Analytics to Evaluate Electronic Serials Usage

ABSTRACT

The purpose of the study is to evaluate electronic usage statistics for Belk Library at Elon University. The Electronic Resources Office (ERO) collects usage data for all serials—both print and electronic—, ebooks, and electronic databases. This study used ezPAARSE and Google Analytics to analyze the data from our website, and we will use these data to compare our vendor statistics to get a more complete picture of how the library's electronic resources are being used.     

Determining Usage When Vendors Do Not Provide Data

ABSTRACT

When library resource vendors do not provide usage data in consistent, manipulable formats, librarians must find other means to enable themselves to make data-driven collection development decisions. At Eastern Kentucky University (EKU) Libraries, two different approaches have been implemented: OpenURL link resolver analysis and EZproxy log analysis using grep, a GNU open source command-line utility. This column provides an overview of each solution.     

Seeing the Forest for Trees: Tools for Analyzing Faculty Research Output

Abstract

For academic libraries, because budgetary pressures are nearly universal, it is imperative to evaluate journal packages regularly. This article presents an overview of the data and methods that the NC State University Libraries traditionally uses to evaluate journal packages and presents additional methods to expand our evaluation of publishing and editorial activity. We describe methods for downloading and analyzing Web of Science citation data to identify the most common publishers for NC State affiliated authors as well as the journals in which NC State authors publish most frequently. This article also demonstrates a custom Python web scraping application to harvest NC State affiliated editor data from publishers’ websites. Finally, this article discusses how these data elements are combined to provide a more comprehensive evaluative strategy for our journal investments.     

Distributing Journals to the World

Abstract

In 2001, a number of initiatives began for distributing research journals to parts of the world where they are otherwise not accessible. This “Balance Point” reports on two publishers whose journals are now more broadly available in developing nations. HighWire Press offers both a free back issue archive and a developing countries initiative. Blackwell Publishing is working with a Russian consortium to make journals available to Russian universities and with nongovernmental organizations to provide free or nearly free access to biomedical journals in a number of developing nations. Serials Review 2002; 28:113–115.     

The Impact of Web-Scale Discovery on the Use of Electronic Resources

Abstract

In 2015, the University of California, Berkeley, launched EBSCO Discovery Service (EDS), a web-scale discovery tool, with a goal of improving visibility and usage of collections. This study applies linear regression analysis to usage data for ebooks, ejournals, and abstracts and indexing (A&I) databases before and after implementation of EDS in order to identify correlations between the discovery layer and usage of library electronic resources across platforms. Our findings diverge from conclusions drawn in the previous literature that indicates that resource use generally increases after a discovery tool is implemented. We examine data from a longer period of time than the previous literature had, looking for statistically significant changes in resource use. The discovery layer at UC Berkeley did not lead to equal increases across platforms, but rather to a complex array of increases and decreases in use according to a variety of factors.     

Encountering COUNTER 5

Abstract

To make fiscally and academically responsible selection and deselection of subscribed content, it is critically important to have reasonably accessible usage data for those resources. To this end, the organizers of Project Counting Online Usage of NeTworked Electronic Resources (COUNTER) initiated the development of a standard known as the COUNTER Code of Practice. Beginning with the year 2019, COUNTER 5 standardized usage reports are available for most commercially published electronic information resources such as online journals, aggregated research databases, and ebooks. This column explores some of the differences between the COUNTER 5 Title Master Report-Journal Report (TR-JR1) report and the COUNTER 4 JR1 report for online journal usage and offers some assistance in extracting specific data from the TR-JR1 report that is not as readily visible as it was in COUNTER 4 reports.     

Reports of Conferences,Institutes, and Seminars

ABSTRACT

This quarter's column reports on sessions from NASIG 2017: Racing to the Crossroads, held in Indianapolis, IN, June 8 to 11, and ALA Annual 2017: Transforming Our Libraries, Ourselves, which took place in Chicago, IL, June 22–27.     

Model fitting and inference under latent equilibrium processes

This paper presents a methodology for model fitting and inference in the context of Bayesian models of the type f(Y | X,θ)f(X|θ)f(θ), where Y is the (set of) observed data, θ is a set of model parameters and X is an unobserved (latent) stationary stochastic process induced by the first order transition model f(X ^(t+1)|X ^(t),θ), where X ^(t) denotes the state of the process at time (or generation) t. The crucial feature of the above type of model is that, given θ, the transition model f(X ^(t+1)|X ^(t),θ) is known but the distribution of the stochastic process in equilibrium, that is f(X|θ), is, except in very special cases, intractable, hence unknown. A further point to note is that the data Y has been assumed to be observed when the underlying process is in equilibrium. In other words, the data is not collected dynamically over time. We refer to such specification as a latent equilibrium process (LEP) model. It is motivated by problems in population genetics (though other applications are discussed), where it is of interest to learn about parameters such as mutation and migration rates and population sizes, given a sample of allele frequencies at one or more loci. In such problems it is natural to assume that the distribution of the observed allele frequencies depends on the true (unobserved) population allele frequencies, whereas the distribution of the true allele frequencies is only indirectly specified through a transition model. As a hierarchical specification, it is natural to fit the LEP within a Bayesian framework. Fitting such models is usually done via Markov chain Monte Carlo (MCMC). However, we demonstrate that, in the case of LEP models, implementation of MCMC is far from straightforward. The main contribution of this paper is to provide a methodology to implement MCMC for LEP models. We demonstrate our approach in population genetics problems with both simulated and real data sets. The resultant model fitting is computationally intensive and thus, we also discuss parallel implementation of the procedure in special cases.     

A Note on Fitting a Regression without an Intercept Term

By entering the data (y _i ,x _i ) followed by (–y _i ,–x _i ), one can obtain an intercept-free regression Y = Xβ + ε from a program package that normally uses an intercept term. There is no bias in the resultant regression coefficients, but a minor postanalysis adjustment is needed to the residual variance and standard errors.     

Bayesian Inference on Multivariate Normal Covariance and Precision Matrices in a Star-Shaped Model with Missing Data

In this article, we study Bayesian estimation for the covariance matrix Σ and the precision matrix Ω (the inverse of the covariance matrix) in the star-shaped model with missing data. Based on a Cholesky-type decomposition of the precision matrix Ω = Ψ′Ψ, where Ψ is a lower triangular matrix with positive diagonal elements, we develop the Jeffreys prior and a reference prior for Ψ. We then introduce a class of priors for Ψ, which includes the invariant Haar measures, Jeffreys prior, and reference prior. The posterior properties are discussed and the closed-form expressions for Bayesian estimators for the covariance matrix Σ and the precision matrix Ω are derived under the Stein loss, entropy loss, and symmetric loss. Some simulation results are given for illustration.     

Two graphical displays for the detection of potentially influential subsets in regression

In the context of the general linear model Y=Xβ+ε, the matrix P_z =Z(Z^TZ)^?1 Z^T , where Z=(X: Y), plays an important role in determining least squares results. In this article we propose two graphical displays for the off-diagonal as well as the diagonal elements of P_Z . The two graphs are based on simple ideas and are useful in the detection of potentially influential subsets of observations in regression. Since P_Z is invariant with respect to permutations of the columns of Z, an added advantage of these graphs is that they can be used to detect outliers in multivariate data where the rows of Z are usually regarded as a random sample from a multivariate population. We also suggest two calibration points, one for the diagonal elements of P_Z and the other for the off-diagonal elements. The advantage of these calibration points is that they take into consideration the variability of the off-diagonal as well as the diagonal elements of P_Z . They also do not suffer from masking.     

Spatial Regression Models Using Inter-Region Distances in a Non-Random Context

This article considers spatial data z( s ₁), z( s ₂),…, z( s _n ) collected at n locations, with the objective of predicting z( s ₀) at another location. The usual method of analysis for this problem is kriging, but here we introduce a new signal-plus-noise model whose essential feature is the identification of hot spots. The signal decays in relation to distance from hot spots. We show that hot spots can be located with high accuracy and that the decay parameter can be estimated accurately. This new model compares well to kriging in simulations.     

Concomitants of multivariate order statistics from multivariate elliptical distributions

ABSTRACT

In this article, we consider a (k + 1)n-dimensional elliptically contoured random vector (X^T₁, X₂^T, …, X^T_k, Z^T)^T = (X₁₁, …, X_1n, …, X_k1, …, X_kn, Z₁, …, Z_n)^T and derive the distribution of concomitant of multivariate order statistics arising from X₁, X₂, …, X_k. Specially, we derive a mixture representation for concomitant of bivariate order statistics. The joint distribution of the concomitant of bivariate order statistics is also obtained. Finally, the usefulness of our result is illustrated by a real-life data.     

Partly linear models on Riemannian manifolds

In partly linear models, the dependence of the response y on (x ^T, t) is modeled through the relationship y=x ^T β+g(t)+?, where ? is independent of (x ^T, t). We are interested in developing an estimation procedure that allows us to combine the flexibility of the partly linear models, studied by several authors, but including some variables that belong to a non-Euclidean space. The motivating application of this paper deals with the explanation of the atmospheric SO₂ pollution incidents using these models when some of the predictive variables belong in a cylinder. In this paper, the estimators of β and g are constructed when the explanatory variables t take values on a Riemannian manifold and the asymptotic properties of the proposed estimators are obtained under suitable conditions. We illustrate the use of this estimation approach using an environmental data set and we explore the performance of the estimators through a simulation study.     

Prediction intervals for growth curves

The growth curve model Y^n×p = A^{n×p ξ mtimes;k}B^k×p+ E^nxp, where Y is an observation matrix, &sigma is a matrix of unknown parameters, A is a known matrix of rank m, B is a known matrix of rank k with 1'= (1, …, 1) as its first row, and the rows of E are independent each distributed as N^p(0,Σ,) is considered. The problem of constructing the prediction intervals for future observations using the above model is considered and approximate intervals assuming different structures on σ are derived. The results are illustrated with several data sets.     

POISSON REGRESSION WITH A PERIODIC FUNCTION

ABSTRACT

Let {y_t } be a Poisson-like process with the mean μ _t which is a periodic function of time t. We discuss how to fit this type of data set using quasi-likelihood method. Our method provides a new avenue to fit a time series data when the usual assumption of stationarity and homogeneous residual variances are invalid. We show that the estimators obtained are strongly consistent and also asymptotically normal.