Utilizing discovery tools for classrooms: how do librarian attitudes on discovery impact tools they teach?

Purpose – The purpose of this paper is to examine the utilization of discovery tools in classrooms with the aim of trying to assess the attitude toward them. Design/methodology/approach – The methodology adopted in this article is a literature review. Findings – Despite the author\u27s best efforts to look at the data from all angles, the author found no statistical significance in any of the data pulled from the survey. The author also tested to see if personal preference had any bearing on reference preference and found that there was no statistical significance between personal preference and reference preference. The author removed all responses that said “it depends” and the results showed that there still was no statistical significance between personal preference and reference preference. Originality/value – Libraries can rebrand their services by utilizing and advocating for discovery tools, but it will only happen if they are willing to make changes on their attitudes toward discovery tools

Check Your Expectations: Testing Self Check in a Consortium Environment

In the spring of 2016, the California State University (CSU) System began the process of creating formalized self-check testing procedures for campuses utilizing self-check in anticipation of our switch to Alma in June of 2017. Ten of 23 CSU campuses have self-check machines in service. Each campus presented its own unique challenges for migrating to a new system including various self-check vendors, options on self-check machines, authentication procedures, and usage of automated materials handling systems. Our group of five was tasked with creating standardized goals and procedures for linking our self-check systems to Alma despite myriad configurations. Our goal for this presentation will be to share experiences and provide ideas on how to create formalized testing procedures in a consortial environment. We will discuss how we communicated during the project, documented progress, as well as provide insight into troubleshooting problems that arose during the testing process

Visualizing Success: Transforming Disparate Data into a Dashboard that Tells a Story

In 2018, SJSU Library completed a project to revamp our public dashboard, incorporating data visualization best practices using Tableau. This session will focus on the step-by-step process of implementing this project, from the first stages to a successful debut. After some preliminary research, we began by creating a prototype and gathering feedback from stakeholders. Next we cleaned and extracted our data from Alma Analytics and other sources. We’ll talk about how we got our data into Tableau, including the pros and cons of using the Web Data Connector. Next we created our visualizations in Tableau. We’ll explain why we chose the chart types we did, as well as some tricks to improve the quality and consistency of the vizzes. Lastly, and with the help of our Web Team, we set up a test server to check our final layout and functionality. We’ll also discuss how we managed our timeline and strategies for successful feedback with stakeholders

P05. Team Interdependence: Construct and Measurement Challenges

Background: Teams are “interdependent collections of individuals who share responsibility for specific outcomes” (Sundstrom et al., 1990; p. 120), and they are used in work organizations to achieve difficult and complex goals. Indeed, the degree to which teams are interdependent serves an important role in team research. However, researchers use varying measures of interdependence (see Pearce, 1993; Staples & Webster, 2008; van der Vegt et al., 2001) that have received little systematic construct validation. Given the criticality of the interdependence construct in teams, the fact that there is conceptual and methodological confusion in this area is troublesome. Methods: In this study, we examined the reliability of various team interdependence measures and the intercorrelations among them. The members of 147 student project teams (N = 547) responded to six measures of team interdependence. Results: Preliminary results suggest that, although they are often used interchangeably by researchers, these interdependence measures are only minimally interrelated, suggesting that this construct and its measurement needs more serious attention. Discussion: Given that team interdependence measures were not strongly interrelated, this suggests these measures are not measuring the same underlying construct. Conclusion: More research is needed to compare these task-related measures to other types of interdependence, such as goal and reward interdependence. Furthermore, future studies should examine the predictive power of interdependence measures on team process variables. Interdisciplinary Reflection: Team research bridges the fields of psychology, business, and sociology. By understanding teams in the workplace, we can understand social processes, how individuals behave in groups, and how to properly design work tasks for optimal performance

4-[(E)-2-Ferrocenylethen­yl]-1,8-naphthalic anhydride

In the structure of the title compound, [Fe(C5H5)(C19H11O3)], the plane of the substituted ferrocene ring is tilted by 14.17 (6)° with respect to the mean plane through the naphthalene ring system. In the crystal structure, centrosymmetric dimers are formed through π–π inter­actions [centroid–centroid distance = 3.624 (2) Å] between the substituted ferrocene ring and the three fused rings of the naphthalic anhydride unit. Pairs of dimers are held together by further naphthalene–naphthalene π–π interactions [distance between parallel mean planes 3.45 (3) Å]. Each dimer inter­acts with four neighbouring dimers in a herringbone fashion through C—H⋯π inter­actions, so forming a two-dimensional sheet-like structure

Long-term health outcomes in young women with Polycystic Ovary Syndrome: a narrative review

Polycystic ovary syndrome (PCOS) has long been recognized as a common disorder in young women leading to reproductive and cutaneous sequelae. However, the associated health risks are now known to extend beyond these familiar manifestations to a range of longer-term comorbidities. Here we review the evidence for an association of PCOS with adverse long-term health outcomes, discussing the pathophysiological mechanisms involved in addition to opportunities for therapeutic intervention. Cross-sectional and longitudinal studies point to an increased risk of type 2 diabetes, hypertension and dyslipidaemia, with recent data confirming that these translate to an increased risk of cardiovascular events independently of obesity. Obstructive sleep apnoea, nonalcoholic fatty liver disease and endometrial cancer are also more prevalent, while mental health disorders, notably anxiety and depression, are common but under-appreciated associations. Uncertainties remain as to whether these risks are apparent in all patients with PCOS or are confined to particular subtypes, whether risks persist post-menopausally and how risk may be affected by ethnicity. Further work is also needed in establishing if systematic screening and targeted intervention can lead to improved outcomes. Until such data are available, clinicians managing women with PCOS should counsel patients on long-term health risks and invest in strategies that limit progression to metabolic and non-metabolic morbidities

Monitoring Vegetation Dynamics and Carbon Stock Density in Miombo Woodlands

Background The United Nation’s Program for Reducing Emissions from Deforestation and Forest Degradation (REDD+) aims to reduce the 20% contribution to global emissions of greenhouse gases from the forest sector, offering a financial value of the carbon stored in forests as an incentive for local communities. The pre-requisite for the setup of a participatory REDD + Program is the monitoring, reporting and verification (MRV) of baseline carbon stocks and their changes over time. In this study, we investigated miombo woodland’s dynamics in terms of composition, structure and biomass over a 4-year period (2005–2009), and the Carbon Stock Density (CSD) for the year 2009. The study was conducted in the Niassa National Reserve (NNR) in northern Mozambique, which is the 14th largest protected area in the world. Results Mean tree density distributed across 79 species increased slightly between 2005 and 2009, respectively, from 548 to 587 trees ha-1. Julbernardia globiflora (Benth.) was the most important species in this area [importance value index (IVI2005= 61 and IVI2009 = 54)]. The woodlands presented an inverted J-shaped diametric curve, with 69% of the individuals representing the young cohort. Woody biomass had a net increase of 3 Mg ha-1 with the highest growth observed in Dyplorhynchus condilocarpon (Müll.Arg.) Pichon (0.54 Mg ha-1). J. globiflora had a net decrease in biomass of 0.09 Mg ha-1. Total CSD density was estimated at ca. 67 MgC ha-1 ± 24.85 with soils (average 34.72 ± 17.93 MgC ha-1) and woody vegetation (average 29.8 MgC ha-1 ± 13.07) representing the major carbon pools. The results point to a relatively stable ecosystem, but they call for the need to refocus management activities. Conclusions The miombo woodlands in NNR are representative of the woodlands in the eco-region in terms of vegetation structure and composition. They experienced net increase in woody biomass, a considerable recruitment level and low mortality. According to our results, NNR may present good potential for carbon sequestration especially in soils and woody biomass, representing an important potential carbon sink. However, further investigations are needed in order to address the contribution of this area to MRV REDD + initiatives

Modelling Aboveground Biomass of Miombo Woodlands in Niassa Special Reserve, Northern Mozambique

Aboveground biomass (AGB) estimation plays a crucial role in forest management and carbon emission reporting, especially for developing countries wishing to address REDD+ projects. Both passive and active remote-sensing technologies can provide spatially explicit information of AGB by using a limited number of field samples, thus reducing the substantial budgetary cost of field inventories. The aim of the current study was to estimate AGB in the Niassa Special Reserve (NSR) using fusion of optical (Landsat 8/OLI and Sentinel 2A/MSI) and radar (Sentinel 1B and ALOS/PALSAR-2) data. The performance of multiple linear regression models to relate ground biomass with different combinations of sensor data was assessed using root-mean-square error (RMSE), and the Akaike and Bayesian information criteria (AIC and BIC). The mean AGB and carbon stock (CS) estimated from field data were estimated at 56 Mg ha−1 (ranging from 11 to 95 Mg ha−1) and 28 MgC ha−1, respectively. The best model estimated AGB at 63 ± 20.3 Mg ha−1 for NSR, ranging from 0.6 to 200 Mg ha−1 (r2 = 87.5%, AIC = 123, and BIC = 51.93). We obtained an RMSE % of 20.46 of the mean field estimate of 56 Mg ha−1. The estimation of AGB in this study was within the range that was reported in the existing literature for the miombo woodlands. The fusion of vegetation indices derived from Landsat/OLI and Sentinel 2A/MSI, and backscatter from ALOS/PALSAR-2 is a good predictor of AGB.info:eu-repo/semantics/publishedVersio