scholarly journals Reading Faculty’s Research Publications Helps to Determine Which Professors to Target for Data Services

2014 ◽  
Vol 9 (1) ◽  
pp. 54
Author(s):  
Giovanna Badia
Keyword(s):  
Data Sharing ◽  
Journal Article ◽  
Faculty Members ◽  
Data Sources ◽  
Data Sets ◽  
Data Repositories ◽  
Data Services ◽  
Data Practices ◽  
Rich Data ◽  
Bibliographic Study

Objective – The research project examined university faculty’s publications in order to find professors with previous data experiences. The professors could then be approached with an offer of the library’s data services. Design – Bibliographic study. Setting – Department of Crop Sciences in the College of Agricultural, Consumer, and Environmental Sciences at the University of Illinois at Urbana-Champaign. Subjects – A total of 62 assistant, associate, and full professors. Methods – The author searched Web of Science and faculty web pages to find each of the subjects’ two most recent research or review articles. Altogether, 124 articles were read to check whether data sources were used and shared. Data sources were defined as sources other than traditional citations to literature for information or ideas, such as data repositories, supplementary files, and weather stations. Data sharing was defined as publicly sharing data beyond that published in the journal article, such as providing supplementary files with the article or submitting data sets to a disciplinary repository (p. 205). Main Results – Thirty of the 124 articles, which were written by 20 different professors, referred to additional data that was made openly accessible. The analysis of the articles uncovered a variety of data experiences, such as faculty who utilized repository data, published supplementary files, submitted their own data to repositories, or posted data on their university’s website. These 20 faculty members were contacted and asked for a meeting “to discuss their data sharing thoughts and experiences and to ask whether they [saw] a role for the library in facilitating data sharing” (p. 206). The author received a positive response from seven of the faculty members and had a successful meeting with each of them. Conclusion – A bibliographic study can be employed to select which professors to target for data services. While this method is time-consuming, it allows librarians to gather rich data about faculty research that will help them to create customized, relevant messages to professors about the library’s data services. It also allows them to become more knowledgeable about data practices and resources in a particular discipline.

scholarly journals Incentivising research data sharing: a scoping review

2021 ◽  
Vol 6 ◽  
pp. 355
Author(s):  
Helen Buckley Woods ◽  
Stephen Pinfield
Keyword(s):  
Data Sharing ◽  
Cultural Change ◽  
Scoping Review ◽  
Good Practice ◽  
Open Data ◽  
Open Science ◽  
Research Data ◽  
Data Sets ◽  
Data Repositories ◽  
Data Practices

Background: Numerous mechanisms exist to incentivise researchers to share their data. This scoping review aims to identify and summarise evidence of the efficacy of different interventions to promote open data practices and provide an overview of current research. Methods: This scoping review is based on data identified from Web of Science and LISTA, limited from 2016 to 2021. A total of 1128 papers were screened, with 38 items being included. Items were selected if they focused on designing or evaluating an intervention or presenting an initiative to incentivise sharing. Items comprised a mixture of research papers, opinion pieces and descriptive articles. Results: Seven major themes in the literature were identified: publisher/journal data sharing policies, metrics, software solutions, research data sharing agreements in general, open science ‘badges’, funder mandates, and initiatives. Conclusions: A number of key messages for data sharing include: the need to build on existing cultures and practices, meeting people where they are and tailoring interventions to support them; the importance of publicising and explaining the policy/service widely; the need to have disciplinary data champions to model good practice and drive cultural change; the requirement to resource interventions properly; and the imperative to provide robust technical infrastructure and protocols, such as labelling of data sets, use of DOIs, data standards and use of data repositories.

Techniques for Building Multijurisdictional Geographic Information System Platforms for Transportation Analysis

2002 ◽  
Vol 1804 (1) ◽  
pp. 187-195
Author(s):  
Kai Han ◽  
Jeannette Montufar ◽  
Scott Minty ◽  
Alan Clayton
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Data Sharing ◽  
Spatial Data ◽  
Geographic Information ◽  
Data Sources ◽  
Data Sets ◽  
Data Interoperability ◽  
Gis Data ◽  
Transportation Applications

Transportation analysis involving multiple jurisdictions requires data sharing and spatial data interoperability among geographic information system (GIS) data sets. Data sharing and spatial data interoperability issues related to multijurisdictional transportation analysis are discussed. Specific techniques based on practical data-sharing, problem-solving experience are developed. To further enhance the data-sharing process, a conceptual framework is established to guide technique implementations. Regional GIS transportation (GIS-T) platforms integrated from various data sources by applying the framework and the associated techniques are also presented. To better support different transportation applications, an open GIS-T platform is proposed, consisting of a series of customized base maps, each tailored to suit individual applications and, as a whole, linked together by inherently established interoperability.

scholarly journals Big Data, Big Changes? A Survey of K-12 Science Teachers in the United States on Which Data Sources and Tools They Use in the Classroom

2021 ◽  
Author(s):  
Joshua Rosenberg ◽  
Elizabeth Schultheis ◽  
Melissa Kjelvik ◽  
Aaron Reedy ◽  
Omiya Sultana
Keyword(s):  
United States ◽  
Science Teachers ◽  
The United States ◽  
Data Sources ◽  
Practice Research ◽  
Small Data ◽  
Data Sets ◽  
Data Practices ◽  
Teachers And Students ◽  
K 12

The tools that scientists and engineers analyze data are changing—and at the same time, science education standards have shifted to focus on science practices that articulate multiple ways for teachers to support students to make sense of data in science classrooms. Moreover, the types of data and technologies available to teachers and students to support their work with data have advanced. While these changes and features point to the importance of data, practices that relate to data, and the roles of technology, little research has offered a portrait of what teachers presently use. We report on findings from a survey conducted in the United States of 330 science teachers on the data sources, practices, and technologies common to their classroom. We found that teachers predominantly involve their students in analyzing relatively small data sets that they collect. In support of this work, teachers tend to use the technologies that are available to them—namely, calculators and spreadsheets. We discuss what these findings suggest for practice, research, and policy, with an emphasis on supporting teachers based on their needs.

scholarly journals State of Data Guidance in Journal Policies: A Case Study in Oncology

2016 ◽  
Vol 10 (2) ◽  
pp. 136-156
Author(s):  
Deborah H. Charbonneau ◽  
Joan E. Beaudoin
Keyword(s):  
Data Sharing ◽  
Vital Role ◽  
Research Data ◽  
Data Repositories ◽  
Data Practices ◽  
Address Data ◽  
Journal Policies ◽  
Meet Data ◽  
Practical Implications

This article reports the results of a study examining the state of data guidance provided to authors by 50 oncology journals. The purpose of the study was the identification of data practices addressed in the journals’ policies. While a number of studies have examined data sharing practices among researchers, little is known about how journals address data sharing. Thus, what was discovered through this study has practical implications for journal publishers, editors, and researchers. The findings indicate that journal publishers should provide more meaningful and comprehensive data guidance to prospective authors. More specifically, journal policies requiring data sharing, should direct researchers to relevant data repositories, and offer better metadata consultation to strengthen existing journal policies. By providing adequate guidance for authors, and helping investigators to meet data sharing mandates, scholarly journal publishers can play a vital role in advancing access to research data.

scholarly journals Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem

2021 ◽  
pp. 089443932110122
Author(s):  
Dennis Assenmacher ◽  
Derek Weber ◽  
Mike Preuss ◽  
André Calero Valdez ◽  
Alison Bradshaw ◽  
...  
Keyword(s):  
Social Media ◽  
Data Sharing ◽  
Algorithm Design ◽  
Computational Social Science ◽  
Evaluation Framework ◽  
Social Media Analytics ◽  
Data Sets ◽  
The Public ◽  
Research Areas ◽  
Media Data

Computational social science uses computational and statistical methods in order to evaluate social interaction. The public availability of data sets is thus a necessary precondition for reliable and replicable research. These data allow researchers to benchmark the computational methods they develop, test the generalizability of their findings, and build confidence in their results. When social media data are concerned, data sharing is often restricted for legal or privacy reasons, which makes the comparison of methods and the replicability of research results infeasible. Social media analytics research, consequently, faces an integrity crisis. How is it possible to create trust in computational or statistical analyses, when they cannot be validated by third parties? In this work, we explore this well-known, yet little discussed, problem for social media analytics. We investigate how this problem can be solved by looking at related computational research areas. Moreover, we propose and implement a prototype to address the problem in the form of a new evaluation framework that enables the comparison of algorithms without the need to exchange data directly, while maintaining flexibility for the algorithm design.

Improvements for research data repositories: The case of text spam

2021 ◽  
pp. 016555152199863
Author(s):  
Ismael Vázquez ◽  
María Novo-Lourés ◽  
Reyes Pavón ◽  
Rosalía Laza ◽  
José Ramón Méndez ◽  
...  
Keyword(s):  
Web Application ◽  
Research Data ◽  
Data Sets ◽  
Data Repositories ◽  
Software Applications ◽  
Public Data ◽  
Protection Mechanisms ◽  
Experimental Protocols ◽  
Learning Research ◽  
Processing Steps

Current research has evolved in such a way scientists must not only adequately describe the algorithms they introduce and the results of their application, but also ensure the possibility of reproducing the results and comparing them with those obtained through other approximations. In this context, public data sets (sometimes shared through repositories) are one of the most important elements for the development of experimental protocols and test benches. This study has analysed a significant number of CS/ML ( Computer Science/ Machine Learning) research data repositories and data sets and detected some limitations that hamper their utility. Particularly, we identify and discuss the following demanding functionalities for repositories: (1) building customised data sets for specific research tasks, (2) facilitating the comparison of different techniques using dissimilar pre-processing methods, (3) ensuring the availability of software applications to reproduce the pre-processing steps without using the repository functionalities and (4) providing protection mechanisms for licencing issues and user rights. To show the introduced functionality, we created STRep (Spam Text Repository) web application which implements our recommendations adapted to the field of spam text repositories. In addition, we launched an instance of STRep in the URL https://rdata.4spam.group to facilitate understanding of this study.

scholarly journals Getting Started Creating Data Dictionaries: How to Create a Shareable Data Set

2021 ◽  
Vol 4 (1) ◽  
pp. 251524592092800
Author(s):  
Erin M. Buchanan ◽  
Sarah E. Crain ◽  
Ari L. Cunningham ◽  
Hannah R. Johnson ◽  
Hannah Stash ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Sharing ◽  
Search Engine ◽  
Web Applications ◽  
Data Sets ◽  
Data Dictionary ◽  
Data Set ◽  
Entire Process ◽  
Shared Data ◽  
Source Data

As researchers embrace open and transparent data sharing, they will need to provide information about their data that effectively helps others understand their data sets’ contents. Without proper documentation, data stored in online repositories such as OSF will often be rendered unfindable and unreadable by other researchers and indexing search engines. Data dictionaries and codebooks provide a wealth of information about variables, data collection, and other important facets of a data set. This information, called metadata, provides key insights into how the data might be further used in research and facilitates search-engine indexing to reach a broader audience of interested parties. This Tutorial first explains terminology and standards relevant to data dictionaries and codebooks. Accompanying information on OSF presents a guided workflow of the entire process from source data (e.g., survey answers on Qualtrics) to an openly shared data set accompanied by a data dictionary or codebook that follows an agreed-upon standard. Finally, we discuss freely available Web applications to assist this process of ensuring that psychology data are findable, accessible, interoperable, and reusable.

scholarly journals A Mine of Information: Can Sports Analytics Provide Wisdom From Your Data?

2017 ◽  
Vol 12 (7) ◽  
pp. 851-855 ◽  
Author(s):  
Louis Passfield ◽  
James G. Hopker
Keyword(s):  
Web Sites ◽  
Large Data ◽  
Objective Evaluation ◽  
Large Data Sets ◽  
Data Sets ◽  
Competitive Strategies ◽  
Data Repositories ◽  
Sports Analytics ◽  
Sport And Exercise ◽  
New Research

This paper explores the notion that the availability and analysis of large data sets have the capacity to improve practice and change the nature of science in the sport and exercise setting. The increasing use of data and information technology in sport is giving rise to this change. Web sites hold large data repositories, and the development of wearable technology, mobile phone applications, and related instruments for monitoring physical activity, training, and competition provide large data sets of extensive and detailed measurements. Innovative approaches conceived to more fully exploit these large data sets could provide a basis for more objective evaluation of coaching strategies and new approaches to how science is conducted. An emerging discipline, sports analytics, could help overcome some of the challenges involved in obtaining knowledge and wisdom from these large data sets. Examples of where large data sets have been analyzed, to evaluate the career development of elite cyclists and to characterize and optimize the training load of well-trained runners, are discussed. Careful verification of large data sets is time consuming and imperative before useful conclusions can be drawn. Consequently, it is recommended that prospective studies be preferred over retrospective analyses of data. It is concluded that rigorous analysis of large data sets could enhance our knowledge in the sport and exercise sciences, inform competitive strategies, and allow innovative new research and findings.

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