Measuring anticipated satisfaction

When developing a software system, one of the early steps is to create a requirements specification. Validating this specification saves implementation effort which might be otherwise spent on building a system with the wrong features. Ideally, this validation should involve many stakeholders representing different groups, to ensure coverage of a variety of viewpoints. However, the usual requirements validation methods such as personal interviews only allow the involvement of a few stakeholders before the costs become prohibitive, so it is difficult to apply them at the needed scale. If the finished software system contains undesirable features, they are likely to be discovered during usability testing. Many usability methods can involve a high number of users at a low cost, for example satisfaction surveys and A/B testing in production. They can give high quality information about improving the system, but they require a completed system or at least an advanced prototype before they can be used. We create a method for measuring user satisfaction before building the system, which we call anticipated satisfaction to distinguish it from the actual satisfaction measured after the user has experienced the system. The method uses a questionnaire which contains short descriptions of the software system’s features, and asks the users to imagine how satisfied they would be when using a system with the described features. The method is flexible, as we do not create a single questionnaire to use. Instead, we give guidance on which variables can be measured with the questionnaire, and how to create questions for them. This allows the development team to tailor the questionnaire to the specific situation in their project. When we applied it in two validation studies, it discovered significant issues and was rated favorably by both the software development team and the users. Our method contributes to the discipline of software engineering by offering a new option for validating software requirements. It is more scalable than interviewing users, and can be employed before the implementation phase, allowing for early problem detection. The effort required to apply it is low, and the information gained is seen as useful by both developers and managers, which makes it a good candidate for use in commercial projects

BBMRI-ERIC Negotiator:Implementing Efficient Access to Biobanks

Various biological resources, such as biobanks and disease-specific registries, have become indispensable resources to better understand the epidemiology and biological mechanisms of disease and are fundamental for advancing medical research. Nevertheless, biobanks and similar resources still face significant challenges to become more findable and accessible by users on both national and global scales. One of the main challenges for users is to find relevant resources using cataloging and search services such as the BBMRI-ERIC Directory, operated by European Research Infrastructure on Biobanking and Biomolecular Resources (BBMRI-ERIC), as these often do not contain the information needed by the researchers to decide if the resource has relevant material/data; these resources are only weakly characterized. Hence, the researcher is typically left with too many resources to explore and investigate. In addition, resources often have complex procedures for accessing holdings, particularly for depletable biological materials. This article focuses on designing a system for effective negotiation of access to holdings, in which a researcher can approach many resources simultaneously, while giving each resource team the ability to implement their own mechanisms to check if the material/data are available and to decide if access should be provided. The BBMRI-ERIC has developed and implemented an access and negotiation tool called the BBMRI-ERIC Negotiator. The Negotiator enables access negotiation to more than 600 biobanks from the BBMRI-ERIC Directory and other discovery services such as GBA/BBMRI-ERIC Locator or RD-Connect Finder. This article summarizes the principles that guided the design of the tool, the terminology used and underlying data model, request workflows, authentication and authorization mechanism(s), and the mechanisms and monitoring processes to stimulate the desired behavior of the resources: to effectively deliver access to biological material and data

Extending the Minimum Information About BIobank Data Sharing Terminology to Describe Samples, Sample Donors, and Events

Introduction: The Minimum Information About BIobank data Sharing (MIABIS) was initiated in 2012. MIABIS aims to create a common biobank terminology to facilitate data sharing in biobanks and sample collections. The MIABIS Core terminology consists of three components describing biobanks, sample collections, and studies, in which information on samples and sample donors is provided at aggregated form. However, there is also a need to describe samples and sample donors at an individual level to allow more elaborate queries on available biobank samples and data. Therefore the MIABIS terminology has now been extended with components describing samples and sample donors at an individual level. Materials and Methods: The components were defined according to specific scope and use cases by a large group of experts, and through several cycles of reviews, according to the new MIABIS governance model of BBMRI-ERIC (Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium). The guiding principles applied in developing these components included the following terms: model should consider only samples of human origin, model should be applicable to all types of samples and all sample donors, and model should describe the current status of samples stored in a given biobank. Results: A minimal set of standard attributes for defining samples and sample donors is presented here. We added an "event" component to describe attributes that are not directly describing samples or sample donors but are tightly related to them. To better utilize the generic data model, we suggest a procedure by which interoperability can be promoted, using specific MIABIS profiles. Discussion: The MIABIS sample and donor component extensions and the new generic data model complement the existing MIABIS Core 2.0 components, and substantially increase the potential usability of this terminology for better describing biobank samples and sample donors. They also support the use of individual level data about samples and sample donors to obtain accurate and detailed biobank availability queries

A Decentralized IT Architecture for Locating and Negotiating Access to Biobank Samples

There is a need among researchers for the easy discoverability of biobank samples. Currently, there is no uniform way for finding samples and negotiate access. Instead, researchers have to communicate with each biobank separately. We present the architecture for the BBMRI-CS IT platform, whose goal is to facilitate sample location and access. We chose a decentral approach, which allows for strong data protection and provides the high flexibility needed in the highly heterogeneous landscape of European biobanks. This is the first implementation of a decentral search in the biobank field. With the addition of a Negotiator component, it also allows for easy communication and a follow-through of the lengthy approval process for accessing samples.</p

A Decentralized IT Architecture for Locating and Negotiating Access to Biobank Samples

There is a need among researchers for the easy discoverability of biobank samples. Currently, there is no uniform way for finding samples and negotiate access. Instead, researchers have to communicate with each biobank separately. We present the architecture for the BBMRI-CS IT platform, whose goal is to facilitate sample location and access. We chose a decentral approach, which allows for strong data protection and provides the high flexibility needed in the highly heterogeneous landscape of European biobanks. This is the first implementation of a decentral search in the biobank field. With the addition of a Negotiator component, it also allows for easy communication and a follow-through of the lengthy approval process for accessing samples

Clonal hematopoiesis with DNMT3A and PPM1D mutations impairs regeneration in autologous stem cell transplant recipients.

Clonal hematopoiesis (CH) is an age-related condition driven by stem- and progenitor cells harboring recurrent mutations linked to myeloid neoplasms. Currently, potential effects on hematopoiesis, stem cell function and regenerative potential under stress conditions are unknown. We performed targeted DNA sequencing of 457 hematopoietic stem cell grafts collected for autologous stem cell transplantation (ASCT) in myeloma patients and correlated our findings with high-dimensional longitudinal clinical and laboratory data (26,510 data points for blood cell counts/serum values in 25 days around transplantation). We detected CH-related mutations in 152 patients (33.3%). Since many patients (n = 54) harbored multiple CH mutations in one or more genes, we applied a non-negative matrix factorization (NMF) clustering algorithm to identify genes that are commonly co-mutated in an unbiased approach. Patients with CH were assigned to one of three clusters (C1-C3) and compared to patients without CH (C0) in a gene specific manner. To study the dynamics of blood cell regeneration following ASCT, we developed a time-dependent linear mixed effect model to validate differences in blood cell count trajectories amongst different clusters. The results demonstrated that C2, composed of patients with DNMT3A and PPM1D single and comutated CH, correlated with reduced stem cell yields and delayed platelet count recovery following ASCT. Also, the benefit of maintenance therapy was particularly strong in C2 patients. Taken together, these data indicate an impaired regenerative potential of hematopoietic stem cell grafts harboring CH with DNMT3A and PPM1D mutations