Collaborative Data Management

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Building the New England Collaborative Data Management Curriculum

Building the New England Collaborative Data Management Curriculum

and practices. (Qin and D’Ignazio 2010; Whitmire 2013). Adding to these challenges is the reality that many STEM librarians have non-STEM edu- cational backgrounds, as Creamer et al. found in a 2009 assessment of the educa- tional backgrounds of New England health sciences and STEM librarians (Creamer 2011). Librarians lacking a science back- ground and who are unfamiliar with scientific research environments (e.g. research per- sonnel, project design, workflows, instru- mentation, protocols) are concerned about their competence for delivering contextual research data management instruction to their researchers. The literature asserts that research libraries need reliable information on researchers’ data needs and data cura- tion practices to develop services that sup- port outreach and teaching, and that these services need to be developed with the com- bined efforts of subject liaisons. (Scaramozzino et al. 2012; Gabridge 2009). These findings highlight the need for com- prehensive educational tools that librarians can use for teaching data management to researchers in diverse STEM fields. Recog- nizing this need, three initiatives, DataONE, the Data Information Literacy Project, and the New England e-Science Collaboration each developed research data management curricula. The DataONE Education Modules are geared for environmental sciences and the University of Minnesota’s Data Manage- ment Course (developed as part of the Data Information Literacy project) is intended for engineering students. The New England Collaborative Data Management Curriculum (NECDMC) stands apart for its subject ag- nostic approach. Authored by a coalition of STEM, metadata, and repository librarians, NECDMC addresses the need for data man- agement educational materials that librarians can use to teach research data management to diverse STEM disciplines. Composed of seven instructional modules that address key components of the National Science Foundation’s data management plan recom- mendations, the modules include lecture
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The New England Collaborative Data Management Curriculum Pilot at the University of Manitoba: A Canadian Experience

The New England Collaborative Data Management Curriculum Pilot at the University of Manitoba: A Canadian Experience

Requirements of Canada’s Federal Fund- ing Agencies Canada has three federal funding agencies: the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineer- ing Research Council of Canada (NSERC), and the Social Sciences and Humanities Re- search Council of Canada (SSHRC). To- gether, they are referred as the Tri- Agencies, and currently none of them re- quires a DMP in their grant application pro- cesses. However, CIHR and SSHRC do have policies about depositing and archiving data sets in the reporting process. CIHR requires open access to bioinformatics, atomic, and molecular coordinate data from CIHR-funded projects at the publication of research findings (Canadian Institutes of Health Research 2012, 5.1.2). SSHRC re- quires that datasets from SSHRC-funded projects be preserved and made available to others within two years after the project com- pletion (Social Sciences and Humanities Re- search Council of Canada 2012).
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Initiating Data Management Instruction to Graduate Students at the University of Houston Using the New England Collaborative Data Management Curriculum

Initiating Data Management Instruction to Graduate Students at the University of Houston Using the New England Collaborative Data Management Curriculum

Data services for students and faculty in the social sciences have existed in research li- braries for decades, but it was the rise of computational research in the sciences and engineering -- and the data deluge that fol- lowed -- that led to the development of re- search data management services, defined here as the storage, curation, preservation, and provision for continuing access to digital research data (Hey and Trefethen 2003, Lewis 2010). Computational research in the social sciences has developed more slowly, although it is beginning to make progress, due in no small part to access and privacy restrictions that are inherent in social sci- ence research and the infrastructure require- ments of distributed monitoring, permission seeking, and encryption (Lazer et al. 2009). Digital scholarship is still emergent in the humanities, but the increasing availability of various materials in digital format and the use of a variety of data analytics are ena- bling humanists to interrogate sources in new ways (Borgman 2009). The American Council of Learned Societies recognizes the need in the humanities and social sciences for infrastructure similar to the cyberinfra- structure utilized in the sciences, but one developed more specifically for the research needs of scholars in those fields (American Council of Learned Societies 2006). When data is defined simply as the output of any systematic investigation that results in the production of new knowledge, it is clear that scientists, social scientists, and humanists all ‘do data’ and will benefit from the devel- opment of research data management ser- vices (Pryor 2012).
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Collaborative Data Sharing with Mappings and Provenance

Collaborative Data Sharing with Mappings and Provenance

One of the most elusive goals of the data integration field has been supporting sharing across large, heterogeneous populations. While data integration and its variants (e.g., data exchange [ 45 ] and warehousing) are being adopted in corporations or small confederations, little progress has been made in integrating broader communities. Yet the need for sharing data across large commu- nities is increasing: most of the physical and life sciences, especially those related to biology and astronomy, have become data-driven as they have attempted to tackle larger questions. The field of bioinformatics, for instance, has a plethora of different databases, each providing a different perspective on a collection of organisms, genes, proteins, diseases, and so on. Associations exist between the different databases’ data (e.g., links between genes and proteins, or gene homologs between species). Unfortunately, data in this domain is surprisingly difficult to integrate, primar- ily because conventional data integration techniques require the development of a single global schema and complete global data consistency. Designing one schema for an entire community like systems biology is arduous, involves many revisions, and requires a central administrator 1
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Collaborative Framework for Scientific Data Analysis

Collaborative Framework for Scientific Data Analysis

High Energy Physics (HEP) data analysis is an example of composable data analysis. Typically, the aim of such data analysis is to identify a particular type of events within the millions of events, generated by HEP experiments. The result of such analysis would be a histogram of possible events. The task of the analysis is to go through all the events available, and identify a particular set of events. We can easily break down the initial data set into smaller sub- sets, and run the same analysis software on these subsets concurrently. The resulting histograms can then be com- bined to produce the final result. In a collaborative ses- sion, each participant will receive these histograms (under a shared event model) and see them being merged in real- time. Please note that the term “event” in “shared event” has no correlation to the events in the HEP data analysis. As explained above, the scientists participating in a collab- orative session will not be from only one organization or a single administrative domain. It will be a global partic- ipation. Therefore, supporting such collaboration requires careful attention to the security aspects of the framework for collaboration. How to authenticate various participants and how to authorize them to perform various activities are some of the questions that should be answered by the col- laborative framework.
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A Collaborative Knowledge Management Tool for Product Innovation Management

A Collaborative Knowledge Management Tool for Product Innovation Management

In this figure an idealised balance between both approaches is illustrated and termed minimum critical codification and team based personalisation. The centre arrow indicates a growth in knowledge management based on a balance between two possible extremes. With this in mind, it is important that the right balance be found between appropriate codification of knowledge (such as logging problems) and personalisation (such as working in teams). It is imperative to have a certain minimum critical codification of knowledge and information. In other words, it is important to represent or codify information and knowledge, which refers to the process of putting knowledge into various forms that can be accessed, leveraged and transferred [1, 2, 3]. It is also imperative to effectively connect team members to one another in order to facilitate knowledge generation and innovation [7, 11, 14, 15, 26]. Effective communication structures are essential to integrate the knowledge and skills required to design, develop and deploy successful products and services. Moreover, closely connected networks of people are lauded to generate more knowledge of a higher quality than any individual can [14, 27].
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Wikis as a Tool for Collaborative Course Management

Wikis as a Tool for Collaborative Course Management

“When  contributing  to  a  wiki  project,  students  are  not  just  writing  for  the  teacher,  as  is  the  case  in  traditional classroom environments, but for and with their peers.” (Guth, 2007. p. 62) Students used the  class wiki to create a collaborative study sheet from which the instructor would select potential questions  for  an  examination.    A  template  wiki  page  contained  descriptions  of  possible  question  topics,  with  question  placeholders  instructing  students  to  “Type  your  question  here”  or  “type  your  answer  here.”  Students also posted their names following each question.  Each student had to post one original exam  question  (and  answer)  in  one  of  the  open  placeholders.  Students  quickly  experience  the  benefits  of  collaboration,  as  each  contributes  one  question,  and  has  the  benefit  of  everyone  else’s  questions.  Students  who  posted  earlier  had their  choice  of  open  topics  on  which  to  post  their  questions;  students  who  posted  later  had  to  read  through  all  of  the  questions  that  their  classmates  had  already  posted  in  order to be sure not to repeat one of them.  At the same time, students who posted earlier had to return  to  the  wiki  as  their  classmates  added  to  it,  in  order  to  gain  the  benefit  of  their  contributions.    The  upcoming exam would draw from questions  based  on those that the class had developed. In this  way,  students not only helped to make up the exam, but they potentially had a copy of it even before it was  administered. 
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Collaborative Data Analysis on Wall Displays

Collaborative Data Analysis on Wall Displays

Users Multiple backgrounds, work styles, preferences, … Tasks Centered around collaborative work. Cognition Collaborative foraging & sensemaking Analysis Results Consensus, sha[r]

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Master Data Management For Collaborative Service Processes

Master Data Management For Collaborative Service Processes

Keywords: Master Data Management, Service Processes , Architectures 1. INTRODUCTION Master data form the basis for business processes. In the context of business data processing, master data denote a company’ s essential basic data which remain unchanged over a specific period of time [1]. These will include, for example, customer, material, employee and supplier data. Inconsistent master data cause process errors and thus higher costs. In practice, however, master data frequently lack not only consistency but also immediacy as many companies use various applications to support their service processes [2]. Against this background, special solutions and standards are emerging for managing master data across system and corporate boundaries. Various software vendors such as SAP, Siebel and Oracle as well as providers of global data pools like SINFOS are currently developing innovative solutions for cross-system and integrated master data management. This article describes the specific challenges of managing master data in service processes and outlines possible benefit potentials. In addition, architecture alternatives for the distribution of master data are explained and illustrated by means of examples. Finally, the example of Asea Brown Boveri (ABB) is used to show that consistent master data lead to major process improvements. The article concludes with a summary and outlook. 1
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Collaborative Customer Relationship Management

Collaborative Customer Relationship Management

The next part of the book, part 4, looks into the relationship between CCRM and logistics. Collaborative Planning, Forecasting and Replenishment (CPFR), a new strategy for joint planning and supply management is introduced here via two contributions from industry experts. In chapter 10 Peter Hambuch of Procter & Gamble reveals how CPFR is employed by a large consumer goods manufacturer. In chapter 11, Georg Engler of Accenture discusses the progression from a pilot project to broad based use.

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Supporting Collaborative Task Management in

Supporting Collaborative Task Management in

Prioritization, Scheduling, Urgency and Importance of Tasks. Determining task importance and priority is another critical issue for task management. In TeleNotes and ContactMap, users exploit spatial cues to signal relative task importance, e.g. by placing key tasks or contacts where they will definitely be seen. Another approach is to use temporal information, where users associate dates with tasks - with urgency being more clearly signaled as the deadline approaches (Bellotti et al., 2003). One problem with this approach is that not all tasks have specific deadlines, forcing users to estimate when those tasks must be done. If these estimates are inaccurate this can undermine the effectiveness of the priorization system. Other research uses machine learning to infer priority. The Priorities project uses classification learning algorithms to help assign the relative expected importance of incoming email messages, and incorporates this information into the user interface (Horvitz et al., 1999; Horvitz et al., 2002). Early investigations suggest this is a promising approach. Again, however, algorithm development needs to be informed by empirical studies of how people determine and evaluate importance. We also need design work to effectively integrate the outputs of these algorithms into the interface, along with hypothesis confirmation to overcome users’ suspicions of automatic methods (Whittaker et al., 2002b).
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Multiparty Access Control Model for Collaborative Management of Shared Data in Online Social Networks

Multiparty Access Control Model for Collaborative Management of Shared Data in Online Social Networks

ABSTRACT: Online systems (OSNs) have perceived astounding advancement as of late and turn into a true webpage for toxic joined with online clients. These sorts of OSNs offer excellent opportunity for computerized friendly connections and additionally subtle elements uncovering, additionally hoist various insurance and also protection concerns. Despite the fact that OSNs let clients so as to minimize utilization of imparted data, these individuals at present tend not to supply essentially any system so as to put in power security contemplations around information identified with numerous clients. To this specific end, huge numbers of us propose a system for empower the genuine protection joined with imparted insight identified with various clients in OSNs. We deliver the induction administration demonstrate so as to seize the genuine quality joined with multiparty assent details, and additionally a multiparty scope principles framework in addition to a scope implementation component. Other than, a considerable lot of us present a practical representation of our own induction administration model which we can leverage the genuine top peculiarities of late reason solvers to execute various examination obligations with your model. We besides discuss a verification of idea model of our own methodology as a major aspect of a credit application in MySpace and still give ease of use research and system assess of our own strategy.
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PERFORMANCE MEASUREMENT AND MANAGEMENT IN A COLLABORATIVE NETWORK

PERFORMANCE MEASUREMENT AND MANAGEMENT IN A COLLABORATIVE NETWORK

First, the main reason for the successful design process was that both the resellers and the key persons from the main company were involved in the initial interviews, development sessions and feedback session. The participatory style was seen to increase the trust, openness and commitment among the different network members. The opportunity to participate in the early stage of the process enabled the participants to voice their own opinions and ideas, and ask questions about issues concerning the performance measurement system. Their participation also enabled a learning process concerning performance measurement, target setting and managing performance in general. This was considered important because most of the resellers did not have any kind of management or financial education. Many researchers have highlighted the following challenges regarding the performance management and measurement of collaborative networks: complexity of the network, relationships among the members, lack of trust and commitment, quality of communication, and common knowledge regarding performance management (Kulmala et al., 2002; Kulmala, 2003; Tenhunen, 2006; Busi and Bititci, 2006; Bititci et al. 2007). The results of the study indicated that participation in the early stages of a carefully designed and structured design process could address most of the concerns listed above. This result is consistent with the findings of Mahama (2006) and Cousins et al. (2008). The results of these studies reveal that the use of performance measurement systems improves communication among the network members, which in turn improves socialisation.
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DOCUMENT MANAGEMENT IN CONTEXT OF COLLABORATIVE SYSTEMS

DOCUMENT MANAGEMENT IN CONTEXT OF COLLABORATIVE SYSTEMS

understanding the concept of document management ideas began to emerge. Applications evolve from one to other providing more specialized functions, upgrading, and offering ease of working processes with documents. But during evolution specialist found not only solutions for specific tasks but also other larger needs. This is why in theory appears the idea of prototyping document management systems and providing permanent upgrades. Our proposed application, as it will be seen later on this article, was projected as a dynamical system, that is suitable for a large amount of organizations, and that could be continuously upgraded to provide facilities needed for specific tasks.
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Landscaping the future for collaborative collection management

Landscaping the future for collaborative collection management

collection management (Heaney, 2000). Thus the catalogue of the library of an institution can be used to infer the existence of a corresponding collection of items described by that catalogue: the institutional library collection. The same reasoning can be applied to union catalogues of all types; there is a corresponding collection, even though its physical items will be distributed across multiple libraries and locations (Dunsire, 2005). It is further assumed that not all collections will contain items relevant to every specific information need of the user, and that it will save time if such collections are excluded from the Discover stage. For example, it is a waste of time to search for items about the Internet in a collection of classical Greek and Roman texts, or to identify items for same-day use when they are located a thousand kilometres away.
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Management of Interdependencies in Collaborative Software Development

Management of Interdependencies in Collaborative Software Development

As an intern with the MVP team, the first author was able to make observations and collect information about several aspects of the team. Additional material was col- lected by reading manuals for the MVP tools, manuals for the software development tools used, formal documents (such as the description of the software development process and the ISO 9001 procedures), training documen- tation for new developers, problem reports, and so on, as well as talking to colleagues. Some of the team mem- bers—the documentation expert, V&V members, testers, process leaders, and process developers—agreed to let the intern shadow them for a few days to better learn about their functions and responsibilities. A representative sub- set of the MVP group was interviewed. Interviews lasted between 45 to 120 minutes. A total of seven interviews [15] were used to find out about the usage patterns of various tools. The data has been analyzed by using grounded theory [28].
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Belmont Forum E- Infrastructures and Data Management Collaborative Research Action. Interim Report, version 1.1

Belmont Forum E- Infrastructures and Data Management Collaborative Research Action. Interim Report, version 1.1

In addition to metadata management, (big) data services as such require attention. Traditionally, metadata are considered small, smart, and queryable while data are seen as large, only for simple download, and not queryable. While this subdivision historically has been motivated by technology limitations, modern Big Data technology today enables re-integration achieving integrated querying on all kinds of data - strings, numbers, graphs, datacubes, etc. Recently activities have started in this direction. One example is the spatio-temporal geo raster query language, WCPS [3], another one is the ISO effort - launched in June 2014 - of extending the SQL standard with multi-dimensional arrays leading to ISO 9075 Part 15: SQL/MDA [1], acknowledging the promising results achieved [2] with Array Databases on massive earth, space, and life science and engineering data (such as sensor, image, simulation, statistics data). The EarthServer initiative has shown, on data holdings up to 130 TB per service, that such standards provide significant benefits in flexibility, scalability, and information integration. After its end in Fall 2014, follow- on activities are recommended for funding to sustain the pace of progress achieved.
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Collaborative Emergency Management with Multimodal GIS

Collaborative Emergency Management with Multimodal GIS

The Human Collaboration and Dialog Management Module receives the recognized gesture descriptions and verbal utterances from the reception control components. It coordinates the execution of these commands through two components: the collaboration manager and the dialog manager. The collaboration manager is involved in the conflict management if two or more persons are interacting with DAVE_G. In this prototype it is assumed that the persons involved in the task work collaboratively and their interactions are interpreted through one semantic frame representing the collective intentions in the formation and execution of one GIS command. The dialog manager checks the received information on consistency and establishes a dialog with the users if the information is not consistent. When sufficient information is collected on a task, the information is passed to the Information Handling Module for the formation and execution of a corresponding GIS query. Information returned from the Information Handling Module can be maps and textual messages (successful query) or error messages. This information is passed to the display control.
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Tourism Destination Management: A Collaborative Approach

Tourism Destination Management: A Collaborative Approach

The aim of this paper is to describe how the organizational paradigm of collaborative networks applied to the tourism sector, when correctly managed and supported by ICTs, can be the right means for the sustainable development of local areas. The paper is structured as follows. In section 2, a characterization of a tourism destination, highlighting the key factors for its development in contrast with the traditional supply chain, is proposed, reporting also main advantages for adopting a CN model for the tourism destination management. In section 3, the concept of the tourism 2.0 lifecycle is introduced, related to the tourist’s needs for an augmented tourism experience, giving the motivations why the adoption of a CN model is an effective way to answer the tourist’s needs. Section 4 reports the operationalization of the concept of CN in tourism. Conclusions of the study are reported in section 5.
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On Services for Collaborative Project Management

On Services for Collaborative Project Management

• Challenge: how to differentiate meaningful and re-usable knowledge from other content • Blogs, wikis, etc useful for sharing unstructured information on active projects and processes • Not good for structured informational retrieval • Business processes often rely on access to structured but distributed data and documents

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