Optimizing Institutional Approaches to Enable Research


“Optimizing Institutional Approaches to Enable Research” is now available in the Journal of Research Administration Volume XLV, No. 2.:


From the editor:

In “Optimizing Institutional Approaches to Enable Research”, Grieb and co-authors focus on a key requirement of research administrators, that of ensuring there is adequate infrastructure to create the backbone for cutting edge research. Within the constraints of a university budget, core facilities must be sustained and replaced in order to compete for extramural funding. “The historic high-end, self-sufficient laboratories have been mostly replaced by laboratories that rely on institutionally supported infrastructure (i.e. core facilities).” Decision making about what to support, the cost of the support and the replacement of the core facilities is often not well managed. An institutional approach for enhancing the effectiveness of core infrastructure operations by implementing process improvements, managing the lifecycle of core facilities, and monitoring key core facilities’ metrics is described. In doing so, it addresses one of the key concerns raised in the article by Derrick and Nickson, that strategies that engage researchers, promote communication between administrators and researchers, and lead to a collaborative approach to streamline bureaucratic processes engenders success.

University, Industry, and Government Partnership: A Science and Technology Roadmap to Drive Innovation


From the Association of Public and Land-Grant Universities (APLU) Annual Meeting 2014, Orlando, Florida:

Abstract:

The Illinois Science & Technology Coalition (ISTC) in September 2014 issued the Illinois Science & Technology Roadmap (S&T Roadmap), an innovative, data-driven report that identifies key technology areas where Illinois has a comparative advantage in innovation...  ISTC... analyzed indicators of research output and citation impact, cross-sector collaboration, patent citations, and research usage to determine what are Illinois’s main competitive research strengths vis-à-vis other peer states and the US as a whole.

You will see results from the S&T Roadmap and learn how universities’ research offices, tech transfer and commercialization offices, and corporate relations as well as those from industry (tech/company incubators, VC funders) and government (national labs, Department of Commerce, etc.) can work together to impact innovation at the state level.

Speaker: Jeff Horon, Consultant

Emerging Methods and Tools for Sparking New Global Creative Networks


From the Proceedings of the 5th International Conference on Collaborative Innovation Networks (COINs15):

Emerging Methods and Tools for Sparking New Global Creative Networks

Jeff Horon

Full text:

Abstract

Emerging methods and tools are changing the ways participants in global creative networks become aware of each other and proceed to interact.  These methods and tools are beginning to influence the collaboration opportunities available to network participants.
Some web-based resources intended to spark new collaborations in creative networks have been plagued by dependence on fragmented or out-of-date information, having shallow recall (e.g. by being limited to a list of manually curated keywords), offering poor interconnectivity with other systems, and/or obtaining low end-user adoption.

Increased availability of information about creative network participants’ activities and outputs (such as completed sponsored research projects and published results, aggregated into global databases), coupled with advancement in information processing techniques like Natural Language Processing (NLP), enables new web-based technologies for discovering subject matter experts, facilities, and networks of current and potential collaborators.  Large-scale data resources and NLP allow modern versions of these tools to avoid the problems of having sparse/fragmented data and also provide for deep recall, sometimes within and across many disciplinary vocabularies.  These tools are known as “passive” technologies, from the perspective of the creative network participant, because the agent must undertake an action to use the information resources placed at his or her disposal.

Emerging “active” methods and tools utilize the same types of information and technologies, but actively intervene in the formation of the creative network by suggesting connections and arranging virtual or physical interactions.  Active approaches can achieve very high end-user adoption rates.

Both active and passive methods strive to use data-driven approaches to form better-than-chance awareness among networks of potential collaborators.  Modern instances of both types of systems generally support interconnectivity with other systems, and therefore expand the size of participants’ networks, resulting in a larger pool of potential collaborators from which to draw upon, within the system and additionally wherever the data is repurposed (e.g. into federated searches and customized applications).

Examples and Case Studies

“Passive” Networking Applications

The most widely deployed applications (providers) are: the Pure Experts portal (Elsevier), VIVO (DuraSpace), and Harvard Profiles (Harvard Medical School).  Each of these applications facilitates search and discovery of subject matter experts and their research activities and outputs.  These systems are generally organized and supported at the university level.  These applications are also federated into multi-institutional search frameworks including Direct2Experts and CTSAsearch – both of which are open to all three of the networking applications above, as well as other less widely deployed applications.

“Active” Networking Applications

Efforts toward active networking interventions are sometimes made with ‘researcher speed dating’ activities, but these generally rely on an audience with some mutual interests being gathered together (e.g. at a conference or symposium) and pairings are typically random.  Despite the existence of predictive factors for propensity to collaborate and likelihood of achieving team goals (e.g. obtaining external funding for research projects)[i], data-driven active networking methods are comparatively rarely used.  Prior case studies in active networking include:

Team design for large center and team science proposals

The University of Michigan Medical School assisted a principal investigator applicant for a large center grant with team formation, based on identifying potential participants publishing or having sponsored projects in subject matter related to the center.  This allowed for discovery of related expertise by analyzing term co-occurrence, and then discovery of the subject matter experts working with those concepts.  Multiple rounds of iteration resulted in a list of keywords, stemmed to related key terms, such that the list was both inclusive of the desired family of concepts and exclusive of ‘false positive’ matches.

Suggested casual interactions at a physical event

At an institute launch event, the University of Michigan employed search methods similar to those above for objective detection of researchers working in related topic areas, to supplement institute founders’ knowledge of researchers working in relevant topic areas with information about previously-unknown researchers also working in these topic areas.  Objective detection allowed for increased inclusiveness and comprehensiveness of the launch conference invitee list.

Launch event organizers solicited survey responses from participants concerning areas of methodological expertise, methodological needs for upcoming projects, and areas of interest within several pre-identified areas related to the institute.

Attendees were matched based upon expressing strong mutual interest in a topic and/or by study method, in situations where one researcher expressed a need for expertise in a method and another research expressed the ability to share methodological expertise in the same method.  Reciprocal methodological need/provision matches were considered especially strong matches (Figure 1):



Figure 1:   A generalized example of an especially strong match

Existing collaboration data covering co-authored publications and co-participation on sponsored projects were used to rule out matches who had collaborated in the past.

To maximize the chances strong matches would interact, the seating chart was also arranged to place strong matches at the same tables.  This event also included conversation-provoking material, including a visualization of attendees arranged in a social networking diagram according to indicated areas of strong interest.

The matching process proved to be very flexible and was used to support a novel approach to bridging mentorship gaps in pediatric research[ii].

Scheduled interactions at a physical event

The University of Texas System M.D. Anderson Cancer Center has in recent years built into a key global cancer conference activities for scheduled networking interactions.  The survey mechanism is similar to the University of Michigan example above, as are the recommendations, but there is also accommodation for arranging meetings including generally a mix of online meeting coordination, dedicated meeting time available, and dedicated meeting spaces available.  Given rotating global locations and varied attendees from year-to-year, priority is given to matches from different institutions as there may only be one time they are physically co-located.

In addition to the meetings booked during a specific speed dating event window in the conference program, the project team also noted a number of off-hours and informal meetings taking place, driven in part by the recommended matches.

Conclusion

These emerging methods and tools suggest the existence of repeatable strategies for facilitating data-driven matching and better-than-chance interactions designed to spark new global creative networks.  As these methods become further systematized and see wider adoption, they are poised to influence larger numbers of creative networks and their participants.

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[i] Lungeanu, A., Huang, Y., and Contractor, N.S. (2014) “Understanding the assembly of interdisciplinary teams and its impact on performance.” Journal of Informetrics.  8(1):59-70.

[ii] Nigrovic, P.A., Muscal, E., Riebschleger, M., et. al. (2014) “AMIGO: A Novel Approach to the Mentorship Gap in Pediatric Rheumatology” Journal of Pediatrics 164(2):226-7.e1-3.




Optimizing Institutional Approaches to Enable Research


From the NORDP 2015 Conference:

Optimizing Institutional Approaches to Core Facility Investment to Enable Research
Jeff Horon, Consultant

Abstract:

In “Optimizing Institutional Approaches to Enable Research,” authors Grieb, Horon, Wong, Durkin, and Kunkel present a comprehensive set of best practices for providing leading-edge core facilities that contribute to the successful execution of research and increase competitiveness for external sponsorship. The authors conclude:

“…. This approach has created a number of standardized, transparent processes to effectively manage central infrastructure that enables enterprise-wide research, including a process for capital equipment planning, a procedure to evaluate new cores, a method for reviewing and managing the lifecycle of existing cores (invest, maintain, or sun-down), an investment in the administration and operational efficiencies of the cores, and support for the development and implementation of new methodologies for our investigators. The execution of these processes has provided faculty with forward-looking technologies to facilitate innovative research and provide a competitive edge for extramural support.”

Therefore the mechanisms for improvement of core facility management and the tangible benefits thereof are understood, but it is often initially not understood how to identify and diagnose sub-optimal funds flows and investment decisions. Funds flows, particularly those related to capital equipment depreciation, can have significant effects on core facility fees to investigators, indirect cost recovery, and availability of funds for equipment replacement/upgrades and provision of new services. Increased understanding of these funds flows can lead to better investment decisions involving strategic allocation of funds to urgent equipment and facility needs as identified by scientific advisory (versus haphazard or ‘hat in hand’ voluntary fundraising models) and periodic review, both to elicit new services investigators would benefit from and to phase out services that have become inefficient or commoditized.

Understanding Funds Flows

Capital equipment ‘on core facility books’ vs…

Capital equipment costs may:

-be factored into investigator-facing costs, reducing the need for subsidization and providing automatic return of funds to repair, replace, and upgrade equipment; however, higher investigator-facing costs may also reduce perceived competitiveness and/or utilization

-fall into capped cost pools, reducing overall indirect cost recovery to the institution

…. ‘on university books’

Capital equipment costs may:

-be factored out of investigator-facing costs, increasing perceived competitiveness and/or utilization; however, funds flows need to be understood and managed such that there are funds to repair, replace, and upgrade equipment; increased subsidization may be required, and some of the benefits may accrue to users external to the institution

-fall into uncapped cost pools, increasing overall indirect cost recovery to the institution

Investment Decision Framework


(adapted from Grieb, et. al., [i] Fig. 1)

By understanding funds flows, institutions can enable strategic decision-making, such as the core facility investment decision framework presented in Grieb, et. al.

In particular, the existence of designated funds for equipment repair, replacement, upgrades, and new equipment purchases implies that there will be input from a scientific advisory board (“What sorts of new equipment and services do our investigators require?”) and/or executive leadership, determining how funds will be allocated from a strategic perspective.

This comprehensive view may lead to further improvements in business processes, e.g. phasing out services that have been commoditized.

Excel Chart Templates


It’s easier to communicate when your data is the most prominent feature of your chart.  Start from good templates.

Basic Excel charts draw focus to themselves instead of the data at hand, by defaulting to include dark gridlines, dark lines and tick marks on each axis, a dark border, color-coded series, and indirect labeling. However, visualization master Edward Tufte and others have taught us that less is often more. By avoiding ‘non-data ink,’ chartjunk, and formatting ‘gloss,’ we can improve the visual clarity of — and therefore the effectiveness of — our data visualizations.

Time is valuable. This means that we should use tools that are good by default. To that end, I have created a series of templates for the six basic Excel chart types.

The basic formatting choices that distinguish these charts from Excel defaults are: light gray gridlines, no axis lines, no tick marks, no borders, and no legend [if you need to describe multiple series, consider the technique of small multiples]. If color encoding (as was done for the pie chart) becomes necessary, you’ll have to do this manually.








(you’ll need to recolor your series manually to achieve the monochromatic blue effect, as shown in the pie chart)



To use these templates, save them to your template directory, which is probably:

C:\Documents and Settings\username\Application Data\Microsoft\Templates\Charts

Then, the next time you want to insert a chart, select ‘All Chart Types’ from the bottom of any ‘Insert’ –> ‘Chart’ menu and then ‘Templates.’ You should see any templates saved into your templates directory as options.

Simple Text Mining for a Known Lexicon in Excel/VBA





Title: Simple Text Mining

Technology # 4730

License: Free


Background

Currently, there is a lack of text/network mining software available to the typical analyst end-user. Generally available text mining algorithms require extensive programming to implement. Typically, these more complex algorithms have an extremely steep learning curve, requiring a long-term commitment of professional software developer resources. Such solutions usually cannot be implemented by the typical analyst or small business.

Technology Description

The University of Michigan has developed an Excel-based tool and algorithm for text mining that ‘reads’ blocks of unstructured text for each word in a lexicon (supplied by the user) and assembles the words found into a common network analysis data structure called an “edge list.” This analysis includes additional descriptive data concerning the weight of lexicon words found. This ‘weight’ output allows for analysis of terms found. The network output allows for analysis of term “adjacency,” i.e. appearing together in the same block of unstructured text, the computation of network analysis measures, and the production of network visualizations. Outputs include user-specified data dimensions, carried over from the text input, for easily cross-referenced and more descriptive output.

Applications
• Analysis of unstructured text for a large number of known lexical terms
• Analysis of occurrence and adjacency (co-occurrence) of terms in papers, abstracts, etc.

Advantages
• Approachability / ease-of-use (single-click processing of input text)
• Easy copy/paste of input/output data

Categories
Software and Copyright/Algorithms & Signal Processing
Software and Copyright/Opensource

Data-Enabled and Spontaneous Researcher Networking at an International Conference


From the Science of Team Science (SciTS) Conference 2014:

Abstract:

This case study explores the use of both data-enabled and spontaneous researcher networking activities among attendees at an international conference based in Seoul, South Korea, in an attempt to utilize knowledge from the Science of Team Science field and discern best practices in their application.

Data-enabled networking activities included an advance survey of all attendees to find participants willing to participate in networking activities, and then suggesting networking partners based upon methodological expertise, methodological needs, and common interests. Response rates and participant feedback will be discussed.

Spontaneous networking activities were also made available to attendees stopping by a physical networking space made available for the duration of the conference. Activities included live browsing of research networking tools and a system for making requests related to research and responding to the requests of others, based upon sociological theories of reciprocity. Participation rates and participant feedback will be discussed.