Organisations are facing challenges with data management as traditional solutions struggle to handle the complexity and volume of data. Three key stakeholders are involved: data producers who create data, data consumers who rely on its quality and data teams tasked with maintaining it. Generally, there are more producers and consumers than data teams, leading to strain on the data teams when issues arise. Often, data producers feel responsible for the data, making it harder to address quality problems that affect consumers. A new approach known as data mesh aims to improve this situation by emphasising decentralisation and domain-driven design. In a data mesh, responsibility for data is distributed among domain teams, not centralised in a single team. These teams are accountable for delivering high-quality data to others, treating it like a product. Additionally, a self-serve data infrastructure platform is proposed to support these teams by offering necessary tools and systems. The scalability, improved collaboration and agility offered by the data mesh paradigm, may contribute largely to the success of projects and programmes within an organisation. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
This study introduces a novel approach to evaluating research universities in developing countries, using Türkiye as a case study within the broader context of global higher education trends. By combining the national University Ranking by Academic Performance (URAP-TR) metrics with K-means clustering analysis, we address the limitations of international ranking systems in assessing institutions outside the Global North. Our comparative analysis of 23 Turkish research universities, implemented using Python and scikit-learn, resulted in three distinct clusters that reflect diverse patterns of institutional development. This clustering approach allows for a nuanced comparison of university performance within Turkey's higher education landscape, while also connecting to global debates on university rankings and performance metrics. A focused examination of Istanbul University-Cerrahpasa illustrates how this method can inform targeted improvement strategies, offering insights applicable to institutions in similar contexts worldwide. By moving beyond traditional rankings, this approach facilitates data-driven decision-making in higher education policy and institutional strategy.
An expensive American gunshot detection system claims it's necessary because humans don't always call the police to report gunfire. But opponents say it's fatally flawed. To investigate, Bailey Passmore and Larry Barrett analysed data on emergencies within the city of Chicago
This is still a work in progress and only presented here for the purposes of receiving feedback. This message will be removed when the work been officially published. An introductory guide to biological data standards, developed as a companion to the ESIP Biological Data Standards Primer. This resource helps researchers organize and share biological data—information about living organisms, their traits, distributions, and ecosystem functions—using community standards. Covers direct observations, trait measurements, and indirect biological signals relevant to Earth systems science. Created by the ESIP Biological Data Standards Cluster with ongoing community input through GitHub.
Timber harvesting tends to generate controversy in society. Some emphasize the benefits of obtaining renewable resources, while others lament the loss of forest carbon stocks and the resulting emissions. However, as long as the wood does not decompose, it continues to store carbon. Consequently, buildings and goods made of wood become carbon sinks. To illustrate this carbon storage potential, we developed an application that calculates the carbon footprint of mechanized timber harvesting using the production reporting input files »harvested production-hpr« and »machine operating monitor-mom« in the StanForD format generated by harvesters and forwarders. The »HarvestCO2-App« is a free, user-friendly web application for forest owners, machine operators/owners, and policymakers, programmed in R with an R Shiny user interface. The app aims to raise awareness of the carbon storage potential of wood use by providing a quantitative basis for discussion. The app also inquires about the conditions under which timber was harvested. By evaluating this information alongside the calculated carbon footprint, it will be possible in the medium term to conduct a sensitivity analysis of the impact of individual factors on emissions.
