6.7 KiB
| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Collaboratory | 3/6 | https://en.wikipedia.org/wiki/Collaboratory | reference | science, encyclopedia | 2026-05-05T09:03:22.295395+00:00 | kb-cron |
== Design philosophy == Finholt (1995), based on the case studies of the Upper Atmospheric Research Collaboratory (UARC) and the Medical Collaboratory, establishes a design philosophy: a collaboratory project must be dedicated to a user-centered design (UCD) approach. This means a commitment to develop software in programming environments that allow rapid prototyping, rapid development cycles (Finholt, 1995). A consequence of the user-centered design in the collaboratory is that the system developers must be able to distinguish when a particular system or modification has positive impact on users’ work practices. An important part of obtaining this understanding is producing an accurate picture of how work is done prior to the introduction of technology. Finholt (1995) explains that behavioral scientists had the task of understanding the actual work settings for which new information technologies were developed. The goal of a user-centered design effort was to inject those observations back into the design process to provide a baseline for evaluating future changes and to illuminate productive directions for prototype development (Finholt, 1995). A similar viewpoint is expressed by Cogburn (2003) who relates the collaboratory to a globally distributed knowledge work, stating that human-computer interaction (HCI) and user-centered design (UCD) principles are critical for organizations to take advantage of the opportunities of globalization and the emergence of an Information society. He (Cogburn, 2003) refers to distributed knowledge work as being a set of “economic activities that produce intangible goods and services […], capable of being both developed and distributed around the world using the global information and communication networks” (Cogburn, 2003, p. 81). Through the use of these global information and communications networks, organizations are able to take part in globally disarticulated production, which means they can locate their research and development facilities almost anywhere in the world, and engineers can collaborate across time zones, institutions and national boundaries.
== Evaluation == Meeting expectations is a factor that influences adoption of innovations, including scientific collaboratories. Some of the collaboratories implemented thus far have not been entirely successful. The Mathematics and Computer Science Division of Argonne National Laboratory, Waterfall Glen collaboratory (Henline, 1998) is an illustrative example. This collaboratory had its shares of problems. There have been the occasional technical and social disasters, but most importantly it did not meet all of the collaboration and interaction requirements. The vast majority of the evaluations performed thus far are concentrating mainly on the usage statistics (e.g. total number of members, hours of use, amount of data communicated) or on the immediate role in the production of traditional scientific outcomes (e.g. publications and patents). Sonnenwald (2003), however, argues that we should rather look for longer-term and intangible measures such as new and continued relationship among scientists, and subsequent, longer-term creation of new knowledge. Regardless of the criteria used for evaluation, we must focus on understanding the expectations and requirements defined for a collaboratory. Without such understanding a collaboratory runs the risk of not being adopted.
== Success factors == Olson, Teasley, Bietz, and Cogburn (2002) ascertain some of the success factors of a collaboratory. They are: collaboration readiness, collaboration infrastructure readiness, and collaboration technology readiness. Collaboration readiness is the most basic pre-requisite for an effective collaboratory, according to Olson, Teasley, Bietz, and Cogburn (2002). Often the critical component to collaboration readiness is based on the concept of “working together in order to achieve a science goal” (Olson, Teasley, Bietz, & Cogburn, 2002, p. 46). Incentives to collaborate, shared principles of collaboration, and experience with the elements of collaboration are also crucial. Successful interaction between users requires a certain amount of common ground. Interactions require a high degree of trust or negotiation, especially when they involve areas where there is a cultural difference. “Ethical norms tend to be culturally specific, and negotiations about ethical issues require high levels of trust” (Olson, Teasley, Bietz, & Cogburn, 2002, p. 49). When analyzing the collaboration infrastructure readiness Olson, Teasley, Bietz, and Cogburn (2002) state that modern collaboration tools require adequate infrastructure to operate properly. Many off-the-shelf applications will run effectively only on state-of-the-art workstations. An important piece of the infrastructure is the technical support necessary to ensure version control, to get participants registered, and to recover in case of disaster. Communications cost is another element which can be critical for collaboration infrastructure readiness (Olson, Teasley, Bietz, & Cogburn, 2002). Pricing structures for network connectivity can affect the choices that users will make and therefore have an effect on the collaboratory's final design and implementation. Collaboration technology readiness, according to Olson, Teasley, Bietz, and Cogburn (2002), refers to the fact that collaboration does not involve only technology and infrastructure, but also requires a considerable investment in training. Thus, it is essential to assess the state of technology readiness in the community to ensure success. If the level is too primitive more training is required to bring the users’ knowledge up-to-date.
== Examples ==
=== Biological Sciences Collaboratory ===
A comprehensively described example of a collaboratory, the Biological Sciences Collaboratory (BSC) at the Pacific Northwest National Laboratory (Chin & Lansing, 2004), enables the sharing and analysis of biological data through metadata capture, electronic laboratory notebooks, data organization views, data provenance tracking, analysis notes, task management, and scientific workflow management. BSC supports various data formats, has data translation capabilities, and can interact and exchange data with other sources (external databases, for example). It offers subscription capabilities (to allow certain individuals to access data) and verification of identities, establishes and manages permissions and privileges, and has data encryption capabilities (to ensure secure data transmission) as part of its security package.