Data management routines for reproducible research using the G-Node Python Client library

Structured, efficient, and secure storage of experimental data and associated meta-information constitutes one of the most pressing technical challenges in modern neuroscience, and does so particularly in electrophysiology. The German INCF Node aims to provide open-source solutions for this domain that support the scientific data management and analysis workflow, and thus facilitate future data access and reproducible research. G-Node provides a data management system, accessible through an application interface, that is based on a combination of standardized data representation and flexible data annotation to account for the variety of experimental paradigms in electrophysiology. The G-Node Python Library exposes these services to the Python environment, enabling researchers to organize and access their experimental data using their familiar tools while gaining the advantages that a centralized storage entails. The library provides powerful query features, including data slicing and selection by metadata, as well as fine-grained permission control for collaboration and data sharing. Here we demonstrate key actions in working with experimental neuroscience data, such as building a metadata structure, organizing recorded data in datasets, annotating data, or selecting data regions of interest, that can be automated to large degree using the library. Compliant with existing de-facto standards, the G-Node Python Library is compatible with many Python tools in the field of neurophysiology and thus enables seamless integration of data organization into the scientific data workflow

The message and the messenger : identifying and communicating a high performance “HRM philosophy”

Purpose The purpose of this paper is to develop understanding of the “HRM process” as defined by Bowen and Ostroff (2004). The authors clarify the construct of “HRM philosophy” and suggest it is communicated to employees through “HRM messages”. Interrelationships between these concepts and other elements of the HRM-performance relationship are explored. The study identifies commonalities in the HRM philosophy and messages underscoring high-performing HRM systems, and highlights the function of a “messenger” in delivering messages to staff. Design/methodology/approach Case study of eight Australian hospitals with top performing HRM systems. Combines primary interview data with independent healthcare accreditor reports. Findings All cases share an HRM philosophy of achieving high-performance outcomes through the HRM system and employees are provided with messages about continuous improvement, best practice and innovation. The philosophy was instilled primarily by executive-level managers, whereby distinctiveness, consensus and consistency of communications were important characteristics. Research limitations/implications The research is limited by: omission of low or average performers; a single industry and country design; and exclusion of employee perspectives. Practical implications The findings reinforce the importance of identifying the HRM philosophy and its key communicators within the organisation, and ensuring it is aligned with strategy, climate and the HRM system, particularly during periods of organisational change. Originality/value The authors expand Bowen and Ostroff’s seminal work and develop the concepts of HRM philosophy and messages, offering the model to clarify key relationships. The findings underscore problems associated with a best practice approach that disregards HRM process elements essential for optimising performance

Efficient Passive ICS Device Discovery and Identification by MAC Address Correlation

Owing to a growing number of attacks, the assessment of Industrial Control Systems (ICSs) has gained in importance. An integral part of an assessment is the creation of a detailed inventory of all connected devices, enabling vulnerability evaluations. For this purpose, scans of networks are crucial. Active scanning, which generates irregular traffic, is a method to get an overview of connected and active devices. Since such additional traffic may lead to an unexpected behavior of devices, active scanning methods should be avoided in critical infrastructure networks. In such cases, passive network monitoring offers an alternative, which is often used in conjunction with complex deep-packet inspection techniques. There are very few publications on lightweight passive scanning methodologies for industrial networks. In this paper, we propose a lightweight passive network monitoring technique using an efficient Media Access Control (MAC) address-based identification of industrial devices. Based on an incomplete set of known MAC address to device associations, the presented method can guess correct device and vendor information. Proving the feasibility of the method, an implementation is also introduced and evaluated regarding its efficiency. The feasibility of predicting a specific device/vendor combination is demonstrated by having similar devices in the database. In our ICS testbed, we reached a host discovery rate of 100% at an identification rate of more than 66%, outperforming the results of existing tools.Comment: http://dx.doi.org/10.14236/ewic/ICS2018.

Macrosystems ecology: Understanding ecological patterns and processes at continental scales

Macrosystems ecology is the study of diverse ecological phenomena at the scale of regions to continents and their interactions with phenomena at other scales. This emerging subdiscipline addresses ecological questions and environmental problems at these broad scales. Here, we describe this new field, show how it relates to modern ecological study, and highlight opportunities that stem from taking a macrosystems perspective. We present a hierarchical framework for investigating macrosystems at any level of ecological organization and in relation to broader and finer scales. Building on well-established theory and concepts from other subdisciplines of ecology, we identify feedbacks, linkages among distant regions, and interactions that cross scales of space and time as the most likely sources of unexpected and novel behaviors in macrosystems. We present three examples that highlight the importance of this multiscaled systems perspective for understanding the ecology of regions to continents

Interseasonal RSV infections in Switzerland - rapid establishment of a clinician-led national reporting system (RSV EpiCH).

In anticipation of an interseasonal respiratory syncytial virus (RSV) epidemic, a clinician-led reporting system was rapidly established to capture RSV infections in Swiss hospitals, starting in January 2021. Here, we present details of the reporting system and first results to June 2021. An unusual epidemiology was observed with an interseasonal surge of RSV infections associated with COVID-19-related non-pharmacological interventions. These data allowed real-time adjustment of RSV prophylaxis guidelines and consequently underscore the need for and continuation of systematic nationwide RSV surveillance

Public preferences for vaccination campaigns in the COVID-19 endemic phase: insights from the VaxPref database

Objective Despite widespread perceptions that SARS-CoV-2 (COVID-19) is no longer a significant threat, the virus continues to loom, and new variants may require renewed efforts to control its spread. Understanding how individual preferences and attitudes influence vaccination behaviour and policy compliance in light of the endemic phase is crucial in preparation for this scenario. Method This paper presents descriptive data from a global stated choice survey conducted in 22 countries across 6 different continents between July 2022 and August 2023, and reports the methodological work developed to address the need for comparable data. Results This study included 50,242 respondents. Findings indicated significant heterogeneity across countries in terms of vaccination status and willingness to accept boosters. Vaccine hesitancy and refusal were driven by lower trust in public health bodies, younger age, and lower educational levels. Refusers and hesitant people reported lower willingness to take risks compared to those fully vaccinated (p<0.05). Lower mental health levels were found for the hesitant cohort (p<0.05). Conclusions Insights from this database can help public health authorities to gain a new understanding of the vaccine hesitancy phenomenon, support them in managing the transition from the pandemic to the endemic phase, and favour a new stream of research to maximise behavioural response to vaccination programs in preparation of future pandemics

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

Integrated platform and API for electrophysiological data

Recent advancements in technology and methodology have led to growing amounts of increasingly complex neuroscience data recorded from various species, modalities, and levels of study. The rapid data growth has made efficient data access and flexible, machine-readable data annotation a crucial requisite for neuroscientists. Clear and consistent annotation and organization of data is not only an important ingredient for reproducibility of results and re-use of data, but also essential for collaborative research and data sharing. In particular, efficient data management and interoperability requires a unified approach that integrates data and metadata and provides a common way of accessing this information. In this paper we describe GNData, a data management platform for neurophysiological data. GNData provides a storage system based on a data representation that is suitable to organize data and metadata from any electrophysiological experiment, with a functionality exposed via a common application programming interface (API). Data representation and API structure are compatible with existing approaches for data and metadata representation in neurophysiology. The API implementation is based on the Representational State Transfer (REST) pattern, which enables data access integration in software applications and facilitates the development of tools that communicate with the service. Client libraries that interact with the API provide direct data access from computing environments like Matlab or Python, enabling integration of data management into the scientist's experimental or analysis routines