Two Years in the Making: Library Resources for Transgender Topics

Inspired by Reid Boehm’s presentation “Beyond Pronouns: Caring for Transgender Medical Research Data to Benefit All People,” at the Research Data Access and Preservation Summit (RDAP) in March 2018, four librarians from the University of Minnesota (UMN) set out to create a LibGuide to support research on transgender topics as a response to Boehm’s identification of insufficient traditional mechanisms for describing, securing, and accessing data on transgender people and topics. This commentary describes the process used to craft the LibGuide, Library Resources for Transgender Topics, including assembling a team of interested library staff, defining the scope of the project, interacting with stakeholders and community partners, establishing a workflow, and designing an ongoing process to incorporate user feedback

Engaging Researchers in Data Dialogues: Designing Collaborative Programming to Promote Research Data Sharing

A range of regulatory pressures emanating from funding agencies and scholarly journals increasingly encourage researchers to engage in formal data sharing practices. As academic libraries continue to refine their role in supporting researchers in this data sharing space, one particular challenge has been finding new ways to meaningfully engage with campus researchers. Libraries help shape norms and encourage data sharing through education and training, and there has been significant growth in the services these institutions are able to provide and the ways in which library staff are able to collaborate and communicate with researchers. Evidence also suggests that within disciplines, normative pressures and expectations around professional conduct have a significant impact on data sharing behaviors (Kim and Adler 2015; Sigit Sayogo and Pardo 2013; Zenk-Moltgen et al. 2018). Duke University Libraries\u27 Research Data Management program has recently centered part of its outreach strategy on leveraging peer networks and social modeling to encourage and normalize robust data sharing practices among campus researchers. The program has hosted two panel discussions on issues related to data management—specifically, data sharing and research reproducibility. This paper reflects on some lessons learned from these outreach efforts and outlines next steps

Lamar Soutter Library Annual Report FY2015

Annual report of the Lamar Soutter Library at the University of Massachusetts Medical School, covering fiscal year July 1, 2014-June 30, 2015.https://escholarship.umassmed.edu/library_annual_reports/1015/thumbnail.jp

Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier

As universities recognize the inherent value in the data they collect and hold, they encounter unforeseen challenges in stewarding those data in ways that balance accountability, transparency, and protection of privacy, academic freedom, and intellectual property. Two parallel developments in academic data collection are converging: (1) open access requirements, whereby researchers must provide access to their data as a condition of obtaining grant funding or publishing results in journals; and (2) the vast accumulation of 'grey data' about individuals in their daily activities of research, teaching, learning, services, and administration. The boundaries between research and grey data are blurring, making it more difficult to assess the risks and responsibilities associated with any data collection. Many sets of data, both research and grey, fall outside privacy regulations such as HIPAA, FERPA, and PII. Universities are exploiting these data for research, learning analytics, faculty evaluation, strategic decisions, and other sensitive matters. Commercial entities are besieging universities with requests for access to data or for partnerships to mine them. The privacy frontier facing research universities spans open access practices, uses and misuses of data, public records requests, cyber risk, and curating data for privacy protection. This paper explores the competing values inherent in data stewardship and makes recommendations for practice, drawing on the pioneering work of the University of California in privacy and information security, data governance, and cyber risk.Comment: Final published version, Sept 30, 201

Why do papers have many Mendeley readers but few Scopus-indexed citations and vice versa?

Counts of citations to academic articles are widely used as indicators of their scholarly impact. In addition, alternative indicators derived from social websites have been proposed to cover some of the shortcomings of citation counts. The most promising such indicator is counts of readers of an article in the social reference sharing site Mendeley. Although Mendeley reader counts tend to correlate strongly and positively with citation counts within scientific fields, an understanding of causes of citation-reader anomalies is needed before Mendeley reader counts can be used with confidence as indicators. In response, this article proposes a list reasons for anomalies based upon an analysis of articles that are highly cited but have few Mendeley readers, or vice versa. The results show that there are both technical and legitimate reasons for differences, with the latter including communities that use research but do not cite it in Scopus-indexed publications or do not use Mendeley. The results also suggest that the lower of the two values (citation counts, reader counts) tends to underestimate of the impact of an article and so taking the maximum is a reasonable strategy for a combined impact indicator

Lamar Soutter Library Annual Report FY2016

Annual report of the Lamar Soutter Library at the University of Massachusetts Medical School, covering fiscal year July 1, 2015-June 30, 2016.https://escholarship.umassmed.edu/library_annual_reports/1016/thumbnail.jp

Nudging lifestyles for better health outcomes: crowdsourced data and persuasive technologies for behavioural change

For at least three decades, a Tsunami of preventable poor health has continued to threaten the future prosperity of our nations. Despite its effective destructive power, our collective predictive and preventive capacity remains remarkably under-developed This Tsunami is almost entirely mediated through the passive and unintended consequences of modernisation. The malignant spread of obesity in genetically stable populations dictates that gene disposition is not a significant contributor as populations, crowds or cohorts are all incapable of experiencing a new shipment of genes in only 2-3 decades. The authors elaborate on why a supply-side approach: advancing health care delivery cannot be expected to impact health outcomes effectively. Better care sets the stage for more care yet remains largely impotent in returning individuals to disease-free states. The authors urge an expedited paradigmatic shift in policy selection criterion towards using data intensive crowd-based evidence integrating insights from system thinking, networks and nudging. Collectively these will support emerging potentialities of ICT used in proactive policy modelling. Against this background the authors proposes a solution that stated in a most compact form consists of: the provision of mundane yet high yield data through light instrumentation of crowds enabling participative sensing, real time living epidemiology separating the per unit co-occurrences which are health promoting from those which are not, nudging through persuasive technologies, serious gaming to sustain individual health behaviour change and intuitive visualisation with reliable simulation to evaluate and direct public health investments and policies in evidence-based waysJRC.DDG.J.4-Information Societ

WASIS - Identificação bioacústica de espécies baseada em múltiplos algoritmos de extração de descritores e de classificação

Orientador: Claudia Maria Bauzer MedeirosDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A identificação automática de animais por meio de seus sons é um dos meios para realizar pesquisa em bioacústica. Este domínio de pesquisa fornece, por exemplo, métodos para o monitoramento de espécies raras e ameaçadas, análises de mudanças em comunidades ecológicas, ou meios para o estudo da função social de vocalizações no contexto comportamental. Mecanismos de identificação são tipicamente executados em dois estágios: extração de descritores e classificação. Ambos estágios apresentam desafios, tanto em ciência da computação quanto na bioacústica. A escolha de algoritmos de extração de descritores e técnicas de classificação eficientes é um desafio em qualquer sistema de reconhecimento de áudio, especialmente no domínio da bioacústica. Dada a grande variedade de grupos de animais estudados, algoritmos são adaptados a grupos específicos. Técnicas de classificação de áudio também são sensíveis aos descritores extraídos e condições associadas às gravações. Como resultado, muitos sistemas computacionais para bioacústica não são expansíveis, limitando os tipos de experimentos de reconhecimento que possam ser conduzidos. Baseado neste cenário, esta dissertação propõe uma arquitetura de software que acomode múltiplos algoritmos de extração de descritores, fusão entre descritores e algoritmos de classificação para auxiliar cientistas e o grande público na identificação de animais através de seus sons. Esta arquitetura foi implementada no software WASIS, gratuitamente disponível na Internet. Diversos algoritmos foram implementados, servindo como base para um estudo comparativo que recomenda conjuntos de algoritmos de extração de descritores e de classificação para três grupos de animaisAbstract: Automatic identification of animal species based on their sounds is one of the means to conduct research in bioacoustics. This research domain provides, for instance, ways to monitor rare and endangered species, to analyze changes in ecological communities, or ways to study the social meaning of the animal calls in the behavior context. Identification mechanisms are typically executed in two stages: feature extraction and classification. Both stages present challenges, in computer science and in bioacoustics. The choice of effective feature extraction and classification algorithms is a challenge on any audio recognition system, especially in bioacoustics. Considering the wide variety of animal groups studied, algorithms are tailored to specific groups. Classification techniques are also sensitive to the extracted features, and conditions surrounding the recordings. As a results, most bioacoustic softwares are not extensible, therefore limiting the kinds of recognition experiments that can be conducted. Given this scenario, this dissertation proposes a software architecture that allows multiple feature extraction, feature fusion and classification algorithms to support scientists and the general public on the identification of animal species through their recorded sounds. This architecture was implemented by the WASIS software, freely available on the Web. A number of algorithms were implemented, serving as the basis for a comparative study that recommends sets of feature extraction and classification algorithms for three animal groupsMestradoCiência da ComputaçãoMestre em Ciência da Computação132849/2015-12013/02219-0CNPQFAPES

Scalability, memory issues and challenges in mining large data sets

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Data mining is an active field of research and development aiming to automatically extract "knowledge" from analyzing data sets. Knowledge can be defined in different ways such as discovering (structured, frequent, approximate, etc.) patterns in data, grouping/clustering/bi-clustering data according to one or more criteria, finding association rules, etc. Such knowledge is then fed-back to decision support systems enabling end-users (actors) to make more informed decisions, which in economic terms could lead to advantages as compared to traditional decision support systems. It should be noted however, that data mining algorithms and frameworks have been proposed prior to the "Big Data" explosion. While data mining algorithms have considered efficiency and computational complexity as an important requirement, they did not take into account features of Big Data such as very large size, velocity with which data is generated, variety, etc. On the other hand, these features are indeed posing issues and challenges to data mining algorithms and frameworks. In this paper we analyse some of the issues in mining large data sets such as scalability and in-memory needs. We also show some computational results pointing out to such issues.Peer ReviewedPostprint (author's final draft

Measuring the nationalism index of Malaysian nation in less developed states

Generally, Malaysia must reached the minimum level outlined by world bodies such as the OECD, World Bank and United Nations to be classified as a developed country.Therefore, states that target to become developed state status also needs to reach the minimum level stipulated by the world bodies for inline recognized as a developed country. The progress to be achieved by Malaysia is not limited to economic and social, but also includes spiritual, psychological and cultural community. Malaysia target was stated in the 9 challenges of Vision 2020, which to create a united Malaysian nation.Therefore, the objective of this study to measure the nationalism index of Malaysian people towards the establishment of 'bangsa Malaysia' as set out in Vision 2020.The study involved 504 respondents from five less developed states in Peninsular Malaysia, namely Kedah, Perlis, Pahang, Terengganu and Kelantan.A structured interview technique using a questionnaire was conducted to qualitatively determine the public perception of the formation of the Malaysian nation.Data was analyzed using fuzzy sets approach.The results showed that the index of nationalism and unity of Perlis residents higher than other states