Blue Earth County Poor Farm: A Brief History

Piecing together a history of the Blue Earth County Poor Farm proved to be a daunting task. A visit today, to the residence that once was home to Blue Earth County’s poor and aged, certainly would suggest the building’s rich history. But as we found in our research, much of the story of this historic building was never recorded or has been lost. What records exist are as scant in information as they are in numbers. What does exist to tell us of the Poor Farm, are articles from the newspapers of the area including the Blue Earth County Enterprise, the Mankato Daily Review, the Mankato Free Press, and the Mankato Record. This account is in no way complete, in that the researchers were unable to find the mechanism by which people were consigned to the poor farm nor a thorough demographic look at the residents who lived there for almost 100 years. What we were able to find were accounting of grain and produce, accounts of reporters who went out to visit the farm. Utilizing the work of Ethel McClure who has explored the history of housing services for older Minnesotans exhaustively in articles and books, our hope is to put the Blue Earth County Poor Farm into the context of poor relief throughout Minnesota. Interweaving news accounts of the poor farm with information from McClure we will try to present some picture of this important building in the history of the county

The JCA DB: Journal Collection Analysis and Evaluation for Outreach and More!

At Minnesota State University, Mankato, we\u27ve created a collection analysis system that is unique for at least two reasons: (1) we can combine a wider variety of data than we’ve seen elsewhere, (2) we can combine that data more quickly, more efficiently, than we’ve seen elsewhere. Because we can combine data efficiently, we have created reports approaching new kinds of problems. We can be more responsive as a library to the needs of our academic departments. We think our liaison support service is especially interesting and can provide other librarians with new ideas about how to garner support for the library

Journal Package and Subscription Analysis: Combining Data in New Ways to Standardize Collection Review

NOTE: In order to see the full presenter’s notes, you must download and open in Acrobat Reader. The presenter’s notes will be cut-off if the PDF is viewed in some browsers. We produce a variety of collection analysis reports which are used for several collection management functions, such as assessment, evaluation, and administration. We have now developed a Collection Review report which includes 100+ journal and package level data elements and dozens of data visualizations, built up from several hundred underlying lists from numerous data sources. Because we bring so many data elements together, we have also developed new metrics for journal package assessment. Some of these metrics could be shared across libraries to better understand package deals. We describe how we’ve used the Collection Review report for biennial collection review and provide examples of how the new metrics informed discussion and led to new questions. We also describe how elements from the Collection Review report and our standard subject-based collection analysis reports were then used to guide follow-up conversations with academic departments

Telling Our Story with Data: From Numbers to a Narrative

In this session we will share our story about moving from quantitative reference statistics to qualitative data. Our journey involved both in-person and online reference transactions prior to the pandemic and moved to 100% virtual reference service during the pandemic. We will describe how our approaches evolved due to changes in software options and how we have continued to tell our story during the pandemic. We will identify opportunities to use qualitative reference statistics to demonstrate library value and we will articulate how qualitative and quantitative reference statistics can be used for internal library planning. We will offer library professionals the opportunity to consider how they can use qualitative and quantitative data collection practices to increase internal and external communication, demonstrate how librarians impact student success, allow for better follow through when problems arise, and increase opportunities to analyze reference transactions and improve resource management

The Collections PBI: Interactive Data Visualization for Campus Outreach

At Minnesota State University, Mankato, we have developed a new approach to collections outreach utilizing interactive data visualization in Microsoft Power BI. The new tool, which we call the Collections PBI, empowers us to demonstrate very clearly and vividly the library\u27s value to campus. The Collections PBI can be used for a variety of purposes, including collection development and collection evaluation, for program review and accreditation. We will demonstrate the Collections PBI and talk about how we are using it to support collections outreach and other library priorities. We\u27ll also discuss design and development issues so others can see how they might implement Power BI or otherwise deploy similar functionality

Automating Collection Analysis Data Visualization in Jupyter Notebook: What\u27s Possible and Why Would You Do It

NOTE: In order to see the full presenter’s notes, you must download and open in Acrobat Reader. The presenter’s notes will be cut-off if the PDF is viewed in some browsers. Data visualizations help librarians see through the clutter of collection analysis metrics, and can be very useful for outreach to academic partners. Automating the production of data visualizations in Jupyter Notebook from standardized data inputs enables us to produce more reports for more academic departments, which increases our impact

New Developments for Journal Package Analysis and Data Visualization

What metrics are most useful for comparing journal packages? What data visualizations enable the most insight into the value of these packages? How can libraries produce these kinds of reports, including data visualizations, as efficiently as possible? Over the last several years at Minnesota State University, Mankato, we have iteratively developed standardized reports for journal collection development, outreach, and academic program support. We previously presented an early version of our package-level analysis reports, where we focused on how to use Tableau for data visualization. Now, we will demonstrate new and improved reports, with new package-level and subject-level metrics leading to additional insights, and we’ll highlight why we prefer Python/ Jupyter Notebook for data visualization. We will also stress why it is important to develop package-level analysis and comparison capabilities beyond what can be provided by UnSub or the library management system. In addition to talking about the applications of these reports for collection development, we’ll discuss how these reports contribute to a new liaison outreach project. The goals of this new project are (1) to re-affirm the value of the journal packages, (2) to prioritize them for continuing investment, and (3) to garner testimonials. Alongside ‘elevator speech’ versions of our reports, these testimonials can be shared with our university administration in order to drive home the importance of an adequate budget to support the curriculum and student success

The mechanisms and processes of connection: developing a causal chain model capturing impacts of receiving recorded mental health recovery narratives.

BACKGROUND: Mental health recovery narratives are a core component of recovery-oriented interventions such as peer support and anti-stigma campaigns. A substantial number of recorded recovery narratives are now publicly available online in different modalities and in published books. Whilst the benefits of telling one's story have been investigated, much less is known about how recorded narratives of differing modalities impact on recipients. A previous qualitative study identified connection to the narrator and/or to events in the narrative to be a core mechanism of change. The factors that influence how individuals connect with a recorded narrative are unknown. The aim of the current study was to characterise the immediate effects of receiving recovery narratives presented in a range of modalities (text, video and audio), by establishing the mechanisms of connection and the processes by which connection leads to outcomes. METHOD: A study involving 40 mental health service users in England was conducted. Participants were presented with up to 10 randomly-selected recovery narratives and were interviewed on the immediate impact of each narrative. Thematic analysis was used to identify the mechanisms of connection and how connection leads to outcome. RESULTS: Receiving a recovery narrative led participants to reflect upon their own experiences or those of others, which then led to connection through three mechanisms: comparing oneself with the narrative and narrator; learning about other's experiences; and experiencing empathy. These mechanisms led to outcomes through three processes: the identification of change (through attending to narrative structure); the interpretation of change (through attending to narrative content); and the internalisation of interpretations. CONCLUSIONS: This is the first study to identify mechanisms and processes of connection with recorded recovery narratives. The empirically-based causal chain model developed in this study describes the immediate effects on recipients. This model can inform selection of narratives for use in interventions, and be used to support peer support workers in recounting their own recovery narratives in ways which are maximally beneficial to others

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives