Making sense: talking data management with researchers

Incremental is one of eight projects in the JISC Managing Research Data programme funded to identify institutional requirements for digital research data management and pilot relevant infrastructure. Our findings concur with those of other Managing Research Data projects, as well as with several previous studies. We found that many researchers: (i) organise their data in an ad hoc fashion, posing difficulties with retrieval and re-use; (ii) store their data on all kinds of media without always considering security and back-up; (iii) are positive about data sharing in principle though reluctant in practice; (iv) believe back-up is equivalent to preservation. <br></br><br></br> The key difference between our approach and that of other Managing Research Data projects is the type of infrastructure we are piloting. While the majority of these projects focus on developing technical solutions, we are focusing on the need for ‘soft’ infrastructure, such as one-to-one tailored support, training, and easy-to-find, concise guidance that breaks down some of the barriers information professionals have unintentionally built with their use of specialist terminology. <br></br><br></br> We are employing a bottom-up approach as we feel that to support the step-by-step development of sound research data management practices, you must first understand researchers’ needs and perspectives. Over the life of the project, Incremental staff will act as mediators, assisting researchers and local support staff to understand the data management requirements within which they are expect to work, and will determine how these can be addressed within research workflows and the existing technical infrastructure. <br></br> <br></br> Our primary goal is to build data management capacity within the Universities of Cambridge and Glasgow by raising awareness of basic principles so everyone can manage their data to a certain extent. We will ensure our lessons can be picked up and used by other institutions. Our affiliation with the Digital Curation Centre and Digital Preservation Coalition will assist in this and all outputs will be released under a Creative Commons licence. The key difference between our approach and that of other MRD projects is the type of ‘infrastructure’ we are piloting. While the majority of these projects focus on developing technical solutions, we are focusing on the need for ‘soft’ infrastructure, such as one-to-one tailored support, training, and easy-to-find, concise guidance that breaks down some of the barriers information professionals have unintentionally built with their use of specialist terminology. We are employing a bottom-up approach as we feel that to support the step-by-step development of sound research data management practices, you must first understand researchers’ needs and perspectives. Over the life of the project, Incremental staff will act as mediators, assisting researchers and local support staff to understand the data management requirements within which they are expect to work, and will determine how these can be addressed within research workflows and the existing technical infrastructure. Our primary goal is to build data management capacity within the Universities of Cambridge and Glasgow by raising awareness of basic principles so everyone can manage their data to a certain extent. We’re achieving this by: - re-positioning existing guidance so researchers can locate the advice they need; - connecting researchers with one-to-one advice, support and partnering; - offering practical training and a seminar series to address key data management topics. We will ensure our lessons can be picked up and used by other institutions. Our affiliation with the Digital Curation Centre and Digital Preservation Coalition will assist in this and all outputs will be released under a Creative Commons licence

Incremental scoping study and implementation plan

This report is one of the first deliverables from the Incremental project, which seeks to investigate and improve the research data management infrastructure at the universities of Glasgow and Cambridge and to learn lessons and develop resources of value to other institutions. Coming at the end of the project’s scoping study, this report identifies the key themes and issues that emerged and proposes a set of activities to address those needs. As its name suggests, Incremental deliberately adopts a stepped, pragmatic approach to supporting research data management. It recognises that solutions will vary across different departmental and institutional contexts; and that top-down, policy-driven or centralised solutions are unlikely to prove as effective as practical support delivered in a clear and timely manner where the benefits can be clearly understood and will justify any effort or resources required. The findings of the scoping study have confirmed the value of this approach and the main recommendations of this report are concerned with the development and delivery of suitable resources. Although some differences were observed between disciplines, these seemed to be as much a feature of different organisational cultures as the nature of the research being undertaken. Our study found that there were many common issues across the groups and that the responses to these issues need not be highly technical or expensive to implement. What is required is that these resources employ jargon-free language and use examples of relevance to researchers and that they can be accessed easily at the point of need. There are resources already available (institutionally and externally) that can address researchers’ data management needs but these are not being fully exploited. So in many cases Incremental will be enabling efficient and contextualised access, or tailoring resources to specific environments, rather than developing resources from scratch. While Incremental will concentrate on developing, repurposing and leveraging practical resources to support researchers in their management of data, it recognises that this will be best achieved within a supportive institutional context (both in terms of policy and provision). The need for institutional support is especially evident when long-term preservation and data sharing are considered – these activities are clearly more effective and sustainable if addressed at more aggregated levels (e.g. repositories) rather than left to individual researchers or groups. So in addition to its work in developing resources, the Incremental project will seek to inform the development of a more comprehensive data management infrastructure at each institution. In Cambridge, this will be connected with the library’s CUPID project (Cambridge University Preservation Development) and at Glasgow in conjunction with the Digital Preservation Advisory Board

Multisystem inflammatory syndrome in children (MIS-C) and neonates (MIS-N) associated with COVID-19: optimizing definition and management

During the SARS-CoV-2-associated infection (COVID-19), pandemic initial reports suggested relative sparing of children inversely related to their age. Children and neonates have a decreased incidence of SARS-CoV-2 infection, and if infected they manifested a less severe phenotype, in part due to enhanced innate immune response. However, a multisystem inflammatory syndrome in children (MIS-C) or paediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2 emerged involving coronary artery aneurysms, cardiac dysfunction, and multiorgan inflammatory manifestations. MIS-C has many similarities to Kawasaki disease and other inflammatory conditions and may fit within a spectrum of inflammatory conditions based on immunological results. More recently neonates born to mothers with SARS-CoV-2 infection during pregnancy demonstrated evidence of a multisystem inflammatory syndrome with raised inflammatory markers and multiorgan, especially cardiac dysfunction that has been described as multisystem inflammatory syndrome in neonates (MIS-N). However, there is a variation in definitions and management algorithms for MIS-C and MIS-N. Further understanding of baseline immunological responses to allow stratification of patient groups and accurate diagnosis will aid prognostication, and inform optimal immunomodulatory therapies. IMPACT: Multisystem inflammatory system in children and neonates (MIS-C and MIS-N) post COVID require an internationally recognized consensus definition and international datasets to improve management and plan future clinical trials. This review incorporates the latest review of pathophysiology, clinical information, and management of MIS-C and MIS-N. Further understanding of the pathophysiology of MIS-C and MIS-N will allow future targeted therapies to prevent and limit clinical sequelae

Predicting the size and probability of epidemics in a population with heterogeneous infectiousness and susceptibility

We analytically address disease outbreaks in large, random networks with heterogeneous infectivity and susceptibility. The transmissibility $T_{uv}$ (the probability that infection of $u$ causes infection of $v$) depends on the infectivity of $u$ and the susceptibility of $v$. Initially a single node is infected, following which a large-scale epidemic may or may not occur. We use a generating function approach to study how heterogeneity affects the probability that an epidemic occurs and, if one occurs, its attack rate (the fraction infected). For fixed average transmissibility, we find upper and lower bounds on these. An epidemic is most likely if infectivity is homogeneous and least likely if the variance of infectivity is maximized. Similarly, the attack rate is largest if susceptibility is homogeneous and smallest if the variance is maximized. We further show that heterogeneity in infectious period is important, contrary to assumptions of previous studies. We confirm our theoretical predictions by simulation. Our results have implications for control strategy design and identification of populations at higher risk from an epidemic.Comment: 5 pages, 3 figures. Submitted to Physical Review Letter

Firm‐specific human capital investments as a signal of general value: Revisiting assumptions about human capital and how it is managed

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136278/1/smj2521.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136278/2/smj2521_am.pd

Percolation in Hierarchical Scale-Free Nets

We study the percolation phase transition in hierarchical scale-free nets. Depending on the method of construction, the nets can be fractal or small-world (the diameter grows either algebraically or logarithmically with the net size), assortative or disassortative (a measure of the tendency of like-degree nodes to be connected to one another), or possess various degrees of clustering. The percolation phase transition can be analyzed exactly in all these cases, due to the self-similar structure of the hierarchical nets. We find different types of criticality, illustrating the crucial effect of other structural properties besides the scale-free degree distribution of the nets.Comment: 9 Pages, 11 figures. References added and minor corrections to manuscript. In pres

Clustering Phase Transitions and Hysteresis: Pitfalls in Constructing Network Ensembles

Ensembles of networks are used as null models in many applications. However, simple null models often show much less clustering than their real-world counterparts. In this paper, we study a model where clustering is enhanced by means of a fugacity term as in the Strauss (or "triangle") model, but where the degree sequence is strictly preserved -- thus maintaining the quenched heterogeneity of nodes found in the original degree sequence. Similar models had been proposed previously in [R. Milo et al., Science 298, 824 (2002)]. We find that our model exhibits phase transitions as the fugacity is changed. For regular graphs (identical degrees for all nodes) with degree k > 2 we find a single first order transition. For all non-regular networks that we studied (including Erdos - Renyi and scale-free networks) we find multiple jumps resembling first order transitions, together with strong hysteresis. The latter transitions are driven by the sudden emergence of "cluster cores": groups of highly interconnected nodes with higher than average degrees. To study these cluster cores visually, we introduce q-clique adjacency plots. We find that these cluster cores constitute distinct communities which emerge spontaneously from the triangle generating process. Finally, we point out that cluster cores produce pitfalls when using the present (and similar) models as null models for strongly clustered networks, due to the very strong hysteresis which effectively leads to broken ergodicity on realistic time scales.Comment: 13 pages, 11 figure

Sampling properties of random graphs: the degree distribution

We discuss two sampling schemes for selecting random subnets from a network: Random sampling and connectivity dependent sampling, and investigate how the degree distribution of a node in the network is affected by the two types of sampling. Here we derive a necessary and sufficient condition that guarantees that the degree distribution of the subnet and the true network belong to the same family of probability distributions. For completely random sampling of nodes we find that this condition is fulfilled by classical random graphs; for the vast majority of networks this condition will, however, not be met. We furthermore discuss the case where the probability of sampling a node depends on the degree of a node and we find that even classical random graphs are no longer closed under this sampling regime. We conclude by relating the results to real {\it E.coli} protein interaction network data.Comment: accepted for publication in Phys.Rev.

Spinal disease in myeloma: cohort analysis at a specialist spinal surgery centre indicates benefit of early surgical augmentation or bracing

BACKGROUND: Multiple myeloma osteolytic disease affecting the spine results in vertebral compression fractures. These are painful, result in kyphosis, and impact respiratory function and quality of life. We explore the impact of time to presentation on the efficacy of spinal treatment modalities. METHODS: We retrospectively reviewed 183 patients with spinal myeloma presenting to our service over a 2 year period. RESULTS: Median time from multiple myeloma diagnosis to presentation at our centre was 195 days. Eighty-four patients (45.9 %) were treated with balloon kyphoplasty and the remainder with a thoracolumbar-sacral orthosis as per our published protocol. Patients presenting earlier than 195 days from diagnosis had significant improvements in patient reported outcome measures: EuroQol 5-Dimensions (p < 0.001), Oswestry Disability Index (p < 0.001), and Visual Analogue Pain Score (p < 0.001) at follow-up, regardless of treatment. Patients presenting after 195 days, however, only experienced benefit following balloon kyphoplasty, with no significant benefit from non-operative management. CONCLUSION: Vertebral augmentation and thoracolumbar bracing improve patient reported outcome scores in patients with spinal myeloma. However, delay in treatment negatively impacts clinical outcome, particularly if managed non-operatively. It is important to screen and treat patients with MM and back pain early to prevent deformity and improve quality of life