scholarly journals Where do our graduates go? A toolkit for retrospective and ongoing career outcomes data collection for biomedical PhD students and postdoctoral scholars

2019 ◽  
Author(s):  
Elizabeth A. Silva ◽  
Alicia B. Mejía ◽  
Elizabeth S. Watkins
Keyword(s):  
Data Collection ◽  
Best Practices ◽  
Graduate Education ◽  
Life Science ◽  
Retrospective Studies ◽  
Collection Management ◽  
Annual Data ◽  
Career Outcomes ◽  
Call To Action ◽  
Postdoctoral Training

Universities are at long last undertaking efforts to collect and disseminate information about student career outcomes, after decades of calls to action. Organizations such as Rescuing Biomedical Research and Future of Research brought this issue to the forefront of graduate education, and the second Future of Biomedical Graduate and Postdoctoral Training conference (FOBGAPT2) featured the collection of career outcomes data in its final recommendations, published in this journal (Hitchcock et al., 2017). More recently, 26 institutions assembled as the Coalition for Next Generation Life Science, committing to ongoing collection and dissemination of career data for both graduate and postdoc alumni. A few individual institutions have shared snapshots of the data in peer-reviewed publications (Mathur et al., 2018; Silva, des Jarlais, Lindstaedt, Rotman, Watkins, 2016) and on websites. As more and more institutions take up this call to action, they will now be looking for tools, protocols, and best practices for ongoing career outcomes data collection, management, and dissemination. Here, we describe UCSF's experiences in conducting a retrospective study, and in institutionalizing a methodology for annual data collection and dissemination. We describe and share all tools we have developed, and we provide calculations of the time and resources required to accomplish both retrospective studies and annual updates. We also include broader recommendations for implementation at your own institutions, increasing the feasibility of this endeavor.

California Tool Works: Assessing the Impact of Life Science Incubators and Accelerators

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Matt Gardner
Keyword(s):  
Public Sector ◽  
Best Practices ◽  
Economic Impact ◽  
Business Models ◽  
Life Science ◽  
Business Incubation ◽  
The Public ◽  
Corporate Innovation ◽  
Landscape Survey ◽  
The Impact

With the proliferation of types and business models in incubation and acceleration, a landscape survey commenced nearly a decade ago with innovation professionals running accelerators, incubators, corporate innovation teams, venture studios, and maker spaces. The benchmarking continues under the auspices of the California Business Incubation Alliance. For this paper, a selected set of findings specific to biotechnology have been detailed, including best practices, success measures, outcomes, and economic impact. The perspective of entrepreneurs, innovation executives, investors, and the public sector have been taken into account throughout this exercise.

Social Implications of Big Data and Fog Computing

2020 ◽  
pp. 1564-1619
Author(s):  
Jeremy Horne
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Social Complexity ◽  
Fog Computing ◽  
Point Of View ◽  
Social Implications ◽  
Collection Management ◽  
Half Century ◽  
The Past ◽  
Artificial Brain

In the last half century, we have gone from storing data on 5¼ inch floppy diskettes to the cloud and now use fog computing. But one should ask why so much data is being collected. Part of the answer is simple in light of scientific projects, but why is there so much data on us? Then, we ask about its “interface” through fog computing. Such questions prompt this article on the philosophy of big data and fog computing. After some background on definitions, origins and contemporary applications, the main discussion begins with thinking about modern data collection, management, and applications from a complexity standpoint. Big data is turned into knowledge, but knowledge is extrapolated from the past and used to manage the future. Yet it is questionable whether humans have the capacity to manage contemporary technological and social complexity evidenced by our world in crisis and possibly on the brink of extinction. Such calls for a new way of studying societies from a scientific point of view. We are at the center of the observation from which big data emerge and are manipulated, the overall human project being not only to create an artificial brain with an attendant mind, but a society that might be able to survive what “natural” humans cannot.

Data collection, management, and score reporting

2020 ◽  
pp. 158-180
Author(s):  
Slobodanka Dimova ◽  
Xun Yan ◽  
April Ginther
Keyword(s):  
Data Collection ◽  
Collection Management ◽  
Score Reporting

scholarly journals Emotional Intelligence a Critical Factor in Organizational Performance

2020 ◽  
Vol 1 (4) ◽  
pp. 10-39
Author(s):  
Neeta Baporikar
Keyword(s):  
Data Collection ◽  
Best Practices ◽  
Organizational Performance ◽  
Case Study Research ◽  
Critical Factor ◽  
Individual Performance ◽  
Qualitative Approach ◽  
Research Paradigm ◽  
Interpretative Research

The objective of this study is to understand how employee EI is a critical factor for improved organizational performance. Adopting a qualitative approach with a case study research design, a sample of 40 employees was selected from the organization; data collection was done through a questionnaire after pilot testing. An analysis is done using SPSS, within an interpretative research paradigm using thematic analyses. Findings reflect that understanding and knowledge on EI and its influence on the performance may add value to the organization as employees become aware of the best practices and contribute better for organizational performance. Further, it was also noted that EI is a significant predictor of job and organizational performance only if it is advocated and spearheaded through individual performance classified into the task and contextual performance.

Best Practices

2013 ◽  
Author(s):  
Edward J. Mullen ◽  
Jennifer L. Bellamy ◽  
Sarah E. Bledsoe
Keyword(s):  
Quality Improvement ◽  
Social Work ◽  
Organizational Learning ◽  
Data Collection ◽  
Best Practices ◽  
Organizational Performance ◽  
Organizational Management ◽  
Quality Data ◽  
Management Literature ◽  
Systems Quality

This entry describes best practices as these are used in social work. The term best practices originated in the organizational management literature in the context of performance measurement and quality improvement where best practices are defined as the preferred technique or approach for achieving a valued outcome. Identification of best practices requires measurement, benchmarking, and identification of processes that result in better outcomes. The identification of best practices requires that organizations put in place quality data collection systems, quality improvement processes, and methods for analyzing and benchmarking pooled provider data. Through this process, organizational learning and organizational performance can be improved.

Journal publication ethics and implications for life science researchers: a COPE perspective

2018 ◽  
Vol 2 (6) ◽  
pp. 763-767
Author(s):  
Trevor Lane
Keyword(s):  
Best Practices ◽  
Knowledge Creation ◽  
Best Practice ◽  
Life Science ◽  
Publication Ethics ◽  
Ethical Practice ◽  
Concerted Effort ◽  
Research Publication ◽  
Journal Publication

The quality of the research record in the form of peer-reviewed journal archives is a reflection of not only the quality of the research publication and correction process, but also the quality of the underlying knowledge creation process. Key to the integrity of the research record are honesty and accountability from all parties involved in governing, performing, and publishing scholarly work. A concerted effort is needed to nurture an ethical research publishing culture by promoting ethical practice, relevant training, and effective systems for responding to allegations of research or publication misconduct. The Committee on Publication Ethics (COPE) is a membership organisation that aims to promote integrity in research publishing, for example, by developing and encouraging best practices to ensure that research is reported ethically, completely, and transparently. COPE uses the Principles of Transparency and Best Practice in Scholarly Publishing as part of its criteria when evaluating publishers and journals as members. Researchers can also make use of these guidelines to assess a journal's quality and to gain insights into what peer-reviewed journals expect from authors. The present article outlines and discusses these guidelines to help life science researchers publish ethically, as well as to identify ethical journals as readers, authors, and reviewers.

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