scholarly journals How many ways can we teach data literacy?

2020 ◽  
Vol 43 (4) ◽  
pp. 1-11
Author(s):  
Yun Dai
Keyword(s):  
New York ◽  
Undergraduate Students ◽  
Information Literacy ◽  
Data Science ◽  
New York University ◽  
Data Literacy ◽  
New Information ◽  
Class Instruction ◽  
Data Lifecycle ◽  
Different Levels

Academic Libraries are ideally positioned to teach data literacy. What is ‘data literacy’ in the first place? Is it the new information literacy? Will the ways we teach information literacy limit imaginative ways to teach data literacy? With those questions in mind, the Library of New York University Shanghai has explored multiple ways to teach data literacy to undergraduate students through university events, ‘for-class’ instruction and workshops, and online casebooks. (1) We initiated the yearlong series of events titled ‘Lying with Data’, inviting faculty across disciplines to each address one core data literacy question that students of data science may elude. (2) We offered workshops and in-class instruction that are up-to-date with the latest technology and that fit with the curriculum. (3) We created online casebooks on various topics in the data lifecycle, tackling user needs at different levels. Essential to our teaching activities are two core values: ‘let the quality speak for itself’, and ‘outreach by teaching’. 

scholarly journals Teaching Foundational Data Skills in the Library

2017 ◽  
Author(s):  
Adam Beauchamp ◽  
Christine Murray
Keyword(s):  
Undergraduate Students ◽  
Information Literacy ◽  
Teaching Strategies ◽  
Literacy Instruction ◽  
Literacy Skills ◽  
Theory And Practice ◽  
Data Literacy ◽  
Research Libraries ◽  
Reference Services ◽  
Effective Teaching Strategies

In Databrarianship: The Academic Data Librarian in Theory and Practice, edited by Linda Kellam and Kristi Thompson. Chicago: Association of College and Research Libraries, 2015.Undergraduate students often struggle when asked to locate, evaluate, and use data in their research, and librarians have an opportunity to support them as they learn data literacy skills. Much of the literature on data librarianship in this area focuses on data reference services, but there is a lack of scholarship and guidance on how to translate data reference expertise into effective teaching strategies. In this chapter, the authors will bridge that gap between data reference and information literacy instruction.

scholarly journals Reproducibility Librarianship

2017 ◽  
Author(s):  
Vicky Steeves
Keyword(s):  
New York ◽  
Data Management ◽  
Data Science ◽  
Research Data ◽  
Reproducible Research ◽  
New York University ◽  
Research Data Management ◽  
Digital Scholarship ◽  
The Many

This is a self-archived version of an article published in Collaborative Librarianship. The content of this article is not different from what is in the journal (found here: http://digitalcommons.du.edu/collaborativelibrarianship/vol9/iss2/4)Recommended CitationSteeves, Vicky (2017) "Reproducibility Librarianship," Collaborative Librarianship: Vol. 9 : Iss. 2 , Article 4. Available at: https://digitalcommons.du.edu/collaborativelibrarianship/vol9/iss2/4Over the past few years, research reproducibility has been increasingly highlighted as a multifaceted challenge across many disciplines. There are socio-cultural obstacles as well as a constantly changing technical landscape that make replicating and reproducing research extremely difficult. Researchers face challenges in reproducing research across different operating systems and different versions of software, to name just a few of the many technical barriers. The prioritization of citation counts and journal prestige has undermined incentives to make research reproducible.While libraries have been building support around research data management and digital scholarship, reproducibility is an emerging area that has yet to be systematically addressed. To respond to this, New York University (NYU) created the position of Librarian for Research Data Management and Reproducibility (RDM & R), a dual appointment between the Center for Data Science (CDS) and the Division of Libraries. This report will outline the role of the RDM & R librarian, paying close attention to the collaboration between the CDS and Libraries to bring reproducible research practices into the norm.

scholarly journals PUBLICATION OF RESEARCH DATA MANAGEMENT IN OPEN ACCESS JOURNAL ANALYSIS BASED ON SCOPUS DATA

2020 ◽  
Vol 41 (2) ◽  
pp. 215
Author(s):  
Tupan Tupan ◽  
Kamaludin Kamaludin
Keyword(s):  
New York ◽  
Open Access ◽  
Data Management ◽  
Data Science ◽  
The United States ◽  
Open Access Journal ◽  
Research Data ◽  
New York University ◽  
Research Data Management ◽  
Open Access Resources

The study aims to determine: (1) the number of open access resources for research data management publications indexed by Scopus, including the year of publication, source of publication, authors, institutions, countries, types of documents and funding agencies; (2) mapping research data management based on keywords. The results of the study showed that the number of open access resources for research data management publications has started since 1981 and the number has continued to increase starting in 2014 and the highest number occurred in 2019, namely 49 publications. The most publicized journals that open access to research data management was the Data Science Journal, which was 11 publications. The most productive author of conducting research data management publications was Cox, A.M. and Pinfield, S. The largest institutions contributing to the publication of open access research data management were the University of Toronto and New York University. The countries that contributed the most were the United States with 50 publications, then China with 38 publications. The most open access research data management in the form of articles as many as 107 and 37 conference paper publications. The institutions that provided the most funding sponsors were the Deutsche Forschungsgemeinschaft and the National Science Foundation. The results of keyword mapping with VOSViewer showed that big data, research data management, information management, data management, medical research topics, software, information processing, and metadata were the most researched topics.

scholarly journals Preparing the Next Generation of Data-Driven Scientists through the Biodiversity Literacy in Undergraduate Education – Data Initiative (BLUE Data)

2018 ◽  
Vol 2 ◽  
pp. e27162
Author(s):  
Anna Monfils ◽  
Elizabeth Ellwood ◽  
Debra Linton ◽  
Molly Phillips ◽  
Lisa White ◽  
...  
Keyword(s):  
Undergraduate Students ◽  
Undergraduate Education ◽  
Data Science ◽  
Literacy Skills ◽  
Biodiversity Data ◽  
Undergraduate Biology ◽  
Data Literacy ◽  
Education Data ◽  
One Year ◽  
Scale Integration

The Biodiversity Literacy in Undergraduate Education - Data Initiative (BLUE Data) is a US National Science Foundation-funded Research Coordination Network in Undergraduate Biology (RCN-UBE) working to generate community consensus around biodiversity data literacy skills. This diverse network brings together biodiversity, data, and education specialists to identify core biodiversity data competencies for undergraduate students, develop strategies for integrating these competencies into the introductory biology curriculum, and build capacity for sustained development and implementation of biodiversity and data literacy education. Since the start of funding one year ago, BLUE Data has been working to review the current landscape of data literacy competencies from primary to graduate education in biodiversity data science, identify gaps in student learning related to data and biodiversity science core skills, and generate community consensus on defined biodiversity data literacy standards. At a recent BLUE Data workshop associated with the Emerging Innovations for Biodiversity Data conference in Berkeley, California, participants worked together to define competencies and identify strategies to facilitate broad-scale integration of transferrable data literacy skills and knowledge to improve undergraduate biology training and meet increasing workforce demands in both data and biodiversity sciences. This discussion also identified current efforts and explored existing resources in order to identify gaps that should be targeted in our efforts moving forward. In this presentation, we will introduce the SPNHC and TDWG communities to BLUE Data, and describe our vision and goals, partners, and educational modules. We will share results from our recent activities, including the outcomes of the Emerging Innovations for Biodiversity Data workshop. BLUE Data welcomes new partnerships with those also interested in defining the undergraduate biodiversity data literacy landscape and charting future efforts of this network.

scholarly journals Incremental and Multi-Task Learning Strategies for Coarse-To-Fine Semantic Segmentation

Technologies ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 1 ◽  
Author(s):  
Mazen Mel ◽  
Umberto Michieli ◽  
Pietro Zanuttigh
Keyword(s):  
Neural Network ◽  
New York ◽  
Learning Strategies ◽  
Deep Neural Network ◽  
Semantic Segmentation ◽  
New York University ◽  
Multi Level ◽  
Segmentation Task ◽  
Coarse To Fine ◽  
Different Levels

The semantic understanding of a scene is a key problem in the computer vision field. In this work, we address the multi-level semantic segmentation task where a deep neural network is first trained to recognize an initial, coarse, set of a few classes. Then, in an incremental-like approach, it is adapted to segment and label new objects’ categories hierarchically derived from subdividing the classes of the initial set. We propose a set of strategies where the output of coarse classifiers is fed to the architectures performing the finer classification. Furthermore, we investigate the possibility to predict the different levels of semantic understanding together, which also helps achieve higher accuracy. Experimental results on the New York University Depth v2 (NYUDv2) dataset show promising insights on the multi-level scene understanding.

Patricia H. Labalme, ed., Beyond their sex New York and London : New York University Press, 1980. xx-188 pp. $ 21.90

Moreana ◽  
1982 ◽  
Vol 19 (Number 74) (2) ◽  
pp. 105-106
Author(s):  
Patricia Delendick ◽  
Germain Marc’hadour
Keyword(s):  
New York ◽  
New York University

Children's Perceived Value: Conceptualization, Scale Development, and Validation

2020 ◽  
Author(s):  
Janine Williams ◽  
A Gazley ◽  
N Ashill
Keyword(s):  
New York ◽  
Perceived Value ◽  
Internal Consistency Reliability ◽  
Future Research ◽  
New York University ◽  
Consumer Decisions ◽  
Managerial Implications ◽  
Stage Process ◽  
Development And Validation ◽  
Value Measure

© 2020 New York University Perceived value among children is an important concept in consumer decisions, yet surprisingly no research has operationalized value for this consumer group. To address this omission, and following the guidelines of DeVellis (2016), this investigation reports the findings of a seven-stage process to develop a valid and reliable instrument for measuring perceived value among children aged 8–14 years. Value for children is conceptualized as a multidimensional construct capturing perceptions of what is received and what is given up, which differs from adult measures in terms of its composition and complexity. A 24-item scale is developed that shows internal consistency, reliability, construct validity, and nomological validity. We also demonstrate the validity of the new scale beyond an existing adult perceived value measure. Directions for future research and managerial implications of the new scale for studying children's consumer behavior are discussed.

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