Ever need to help a researcher share and archive their research data? Would you know how to advise them on managing their data so it can be easily shared and re-used? This workshop will cover best practices for collecting and organizing research data related to the goal of data preservation and sharing. We will focus on best practices and tips for collecting data, including file naming, documentation/metadata, quality control, and versioning, as well as access and control/security, backup and storage, and licensing. We will discuss the library’s role in data management, and the opportunities and challenges around supporting data sharing efforts. Through case studies we will explore a typical research data scenario and propose solutions and services by the library and institutional partners. Finally, we discuss methods to stay up to date with data management related topics.

The Best Management Practices Statistical Estimator (BMPSE) version 1.2.0 was developed by the U.S. Geological Survey (USGS), in cooperation with the Federal Highway Administration (FHWA) Office of Project Delivery and Environmental Review to provide planning-level information about the performance of structural best management practices for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway and urban runoff on the Nation's receiving waters (Granato 2013, 2014; Granato and others, 2021). The BMPSE was assembled by using a Microsoft Access® database application to facilitate calculation of BMP performance statistics. Granato (2014) developed quantitative methods to estimate values of the trapezoidal-distribution statistics, correlation coefficients, and the minimum irreducible concentration (MIC) from available data. Granato (2014) developed the BMPSE to hold and process data from the International Stormwater Best Management ...

The Data Organisation ABC workshop is designed to raise awareness of good data organisation and documentation practices. The workshop covers file and folder naming practices, version control and other documentation-related concepts. The workshop is divided into two parts, the first of which (introduction + assignment) focuses on the basics of data organisation, such as folder structure, file naming, version control and readme files. The second part and the assignment will focus on organising tabular data and going through good practices. The examples and tasks in the workshop are based on the life sciences, but as the topic is generic, the materials can be used regardless of scientific discipline. By editing the topics of your tasks, you can easily adapt the material to be discipline-specific.The minimum time needed to run the workshop as a single session is 2 hours and 30 minutes. The workshop materials are available in Finnish and English. The workshop package includes: Instructions on how to conduct the workshop An introduction to the topic Two exercises

The vrr extension for CKAN likely intends to enhance data resource management, specifically potentially related to versioning, revisions, or related resources. Given the limited information available in the readme, it is difficult to determine precise functionalities; however, based on naming conventions typically used in software development, the extension likely adds capabilities to track changes or dependencies between data resources within a CKAN instance. This would potentially assist users in maintaining data integrity and understanding data provenance. Key Features (assumed based on name and common patterns): Resource Relationships: Likely enables the creation and management of relationships between different resources, such as associating a derived dataset with its source data. Revision Tracking: Possibly tracks changes made to datasets and resources over time, allowing users to revert to previous versions or compare different versions. Versioning Support: Might implement a formal versioning system for datasets, enabling users to clearly identify and access specific versions of a dataset. Dependency Management: Potentially manages dependencies between datasets and other resources, ensuring that changes to one dataset do not break dependent datasets. Technical Integration: Due to minimal documentation, assuming integration through CKAN's plugin interface via changes to metadata schemas associated to the database and providing new API endpoints for managing resource relationships and providing versioning information. Further investigation would be required to establish precisely how the 'vrr' extension affects data structures and functionality. Benefits & Impact (assumed): Improved data governance and understandability by explicitly defining data provenance and relationships, potentially reducing errors and better facilitating data analysis and reproducibility. The extension has the potential to provide robust version tracking features, enabling data managers to comply with data management best practices.

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Model Feature Store Market Outlook

According to our latest research, the global Model Feature Store market size is valued at USD 825 million in 2024 and is projected to reach USD 5.23 billion by 2033, expanding at a robust CAGR of 22.7% during the forecast period. The primary growth driver for this market is the exponential rise in adoption of machine learning (ML) and artificial intelligence (AI) across diverse industries, which has created an urgent need for efficient, scalable, and reliable feature management solutions. As organizations strive to operationalize AI and ML at scale, feature stores have emerged as a critical component in ensuring consistency, collaboration, and efficiency across the ML lifecycle.

The surge in data-driven decision-making is a key catalyst behind the growing demand for model feature stores. Enterprises are increasingly leveraging massive volumes of structured and unstructured data to fuel advanced analytics and predictive modeling. However, managing and reusing features across multiple ML projects remains a significant challenge. Model feature stores address this by centralizing feature storage, ensuring consistency, enabling version control, and facilitating feature sharing across teams. This not only accelerates model development cycles but also reduces duplication of effort, driving operational efficiency and cost savings. The growing awareness of these benefits among data science and engineering teams is propelling market adoption at a rapid pace.

Another pivotal growth factor is the proliferation of cloud-native ML platforms and the shift towards MLOps (Machine Learning Operations) best practices. As organizations transition their AI workloads to the cloud, they seek scalable and flexible feature store solutions that can seamlessly integrate with their existing data pipelines and ML infrastructure. Cloud-based model feature stores offer elasticity, high availability, and simplified management, making them an attractive choice for enterprises aiming to scale AI initiatives globally. Furthermore, the integration of feature stores with leading ML platforms and orchestration tools is enabling end-to-end automation, improved governance, and faster time-to-value for AI projects.

The increasing complexity of regulatory requirements, especially in sectors like BFSI and healthcare, is also fostering the adoption of model feature stores. These industries demand rigorous data governance, auditability, and compliance with standards such as GDPR and HIPAA. Feature stores provide robust lineage tracking, role-based access control, and detailed feature documentation, helping organizations meet stringent regulatory obligations while maintaining the agility required for innovation. As regulatory scrutiny around AI and data usage intensifies, the role of feature stores in ensuring responsible and compliant AI deployment is expected to become even more prominent.

From a regional perspective, North America currently dominates the model feature store market, accounting for over 38% of global revenue in 2024. This leadership is attributed to the early adoption of AI technologies, a mature data science ecosystem, and significant investments by tech giants and financial institutions. However, the Asia Pacific region is poised for the fastest growth, with a projected CAGR of 25.1% through 2033, driven by rapid digital transformation, burgeoning startup activity, and increasing AI investments in countries like China, India, and Japan. Europe follows closely, with strong momentum in sectors such as automotive, manufacturing, and healthcare. Latin America and the Middle East & Africa are also witnessing gradual uptake, supported by growing awareness and government initiatives promoting AI innovation.

Component Analysis

The model feature store market is segmented by component into platforms and services, each playing a distinct yet complementary role in the ecosystem. Platform solutions constitute the core of the market, enabling organizations to centralize, catalog, and manage features used in machine learning models. These platforms offer a wide array of functionalities, including feature discovery, versioning, lineage tracking, and integration with popular ML frameworks and data pipelines. The rapid evolution of open-source and commercial feature store platforms—such as Feast, Tecton, and AWS SageMaker Feature Store—has democratized access to advance

Open-Source Database Software Market size was valued at USD 10.00 Billion in 2024 and is projected to reach USD 35.83 Billion by 2032, growing at a CAGR of 20% during the forecast period 2026-2032.

Global Open-Source Database Software Market Drivers

The market drivers for the Open-Source Database Software Market can be influenced by various factors. These may include:

Cost-Effectiveness: Compared to proprietary systems, open-source databases frequently have lower initial expenses, which attracts organizations—especially startups and small to medium-sized enterprises (SMEs) with tight budgets. Flexibility and Customisation: Open-source databases provide more possibilities for customization and flexibility, enabling businesses to modify the database to suit their unique needs and grow as necessary. Collaboration and Community Support: Active developer communities that share best practices, support, and contribute to the continued development of open-source databases are beneficial. This cooperative setting can promote quicker problem solving and innovation. Performance and Scalability: A lot of open-source databases are made to scale horizontally across several nodes, which helps businesses manage expanding data volumes and keep up performance levels as their requirements change. Data Security and Sovereignty: Open-source databases provide businesses more control over their data and allow them to decide where to store and use it, which helps to allay worries about compliance and data sovereignty. Furthermore, open-source code openness can improve security by making it simpler to find and fix problems. Compatibility with Contemporary Technologies: Open-source databases are well-suited for contemporary application development and deployment techniques like microservices, containers, and cloud-native architectures since they frequently support a broad range of programming languages, frameworks, and platforms. Growing Cloud Computing Adoption: Open-source databases offer a flexible and affordable solution for managing data in cloud environments, whether through self-managed deployments or via managed database services provided by cloud providers. This is because more and more organizations are moving their workloads to the cloud. Escalating Need for Real-Time Insights and Analytics: Organizations are increasingly adopting open-source databases with integrated analytics capabilities, like NoSQL and NewSQL databases, as a means of instantly obtaining actionable insights from their data.

Introduction This data shows an aggregated view of our Data Best Practice Maturity Ratings against Ofgem's Data Best Practice Principles. The details of which, can be found here: https://www.ofgem.gov.uk/decision/decision-updates-data-best-practice-guidance-and-digitalisation-strategy-and-action-plan-guidance

This dataset feeds our Data Best Practice Maturity Framework page.

Methodological Approach This is an aggregated snapshot of our Data Best Practice Maturity Ratings against Ofgem's Data Best Practice Principles

Quality Control Statement The data is provided "as is".

Assurance Statement The Open Data team has checked the data against source to ensure data accuracy and consistency.

Other Download dataset information: Metadata (JSON)

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/

This repository contains material related to the analysis performed in the article "Best Practices for Your Exploratory Factor Analysis: a Factor Tutorial". The material includes the data used in the analyses in .dat format, the labels (.txt) of the variables used in the Factor software, the outputs (.txt) evaluated in the article, and videos (.mp4 with English subtitles) recorded for the purpose of explaining the article. The videos can also be accessed in the following playlist: https://youtube.com/playlist?list=PLln41V0OsLHbSlYcDszn2PoTSiAwV5Oda. Below is a summary of the article:

"Exploratory Factor Analysis (EFA) is one of the statistical methods most widely used in Administration, however, its current practice coexists with rules of thumb and heuristics given half a century ago. The purpose of this article is to present the best practices and recent recommendations for a typical EFA in Administration through a practical solution accessible to researchers. In this sense, in addition to discussing current practices versus recommended practices, a tutorial with real data on Factor is illustrated, a software that is still little known in the Administration area, but freeware, easy to use (point and click) and powerful. The step-by-step illustrated in the article, in addition to the discussions raised and an additional example, is also available in the format of tutorial videos. Through the proposed didactic methodology (article-tutorial + video-tutorial), we encourage researchers/methodologists who have mastered a particular technique to do the same. Specifically, about EFA, we hope that the presentation of the Factor software, as a first solution, can transcend the current outdated rules of thumb and heuristics, by making best practices accessible to Administration researchers".

The open-source project management software market is experiencing robust growth, driven by increasing demand for flexible, customizable, and cost-effective solutions among SMEs and large enterprises alike. The market size in 2025 is estimated at $2.5 billion, reflecting a substantial increase from previous years. This growth is fueled by several key factors, including the rising adoption of agile methodologies, the need for enhanced collaboration and transparency across project teams, and a preference for solutions that offer greater control and ownership over data and functionality compared to proprietary alternatives. The self-hosted deployment model continues to hold a significant market share, particularly among organizations with stringent data security and compliance requirements. However, SaaS-based solutions are experiencing rapid growth, appealing to businesses seeking ease of deployment and maintenance. The market is segmented by application (SMEs vs. Large Enterprises), with large enterprises exhibiting higher adoption rates due to their greater need for sophisticated project management capabilities. A projected Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033 suggests a substantial expansion of this market in the coming years. This growth trajectory is further supported by ongoing innovation within the open-source community, leading to the development of more feature-rich and user-friendly platforms. The competitive landscape is highly dynamic, with established players like GitHub and Odoo alongside a diverse range of specialized solutions such as Mattermost, OpenProject, and Taiga. These companies are continually improving their offerings to stay ahead of evolving user needs and technological advancements. Geographical expansion is another critical factor, with North America and Europe currently dominating the market. However, rapid growth is expected in Asia Pacific and other emerging markets driven by increasing digitalization and adoption of project management best practices. While the market faces some restraints, such as the perceived higher initial setup costs associated with self-hosted solutions and the need for in-house expertise, the advantages of open-source solutions in terms of cost-effectiveness, flexibility, and community support are overriding these concerns and driving sustained growth.