Data Management Strategies Research Articles

A practical guide to data management and sharing for biomedical laboratory researchers

Effective data management and sharing have become increasingly crucial in biomedical research; however, many laboratory researchers lack the necessary tools and knowledge to address this challenge. This article provides an introductory guide into research data management (RDM), and the importance of FAIR (Findable, Accessible, Interoperable, and Reusable) data-sharing principles for laboratory researchers produced by practicing scientists. We explore the advantages of implementing organized data management strategies and introduce key concepts such as data standards, data documentation, and the distinction between machine and human-readable data formats. Furthermore, we offer practical guidance for creating a data management plan and establishing efficient data workflows within the laboratory setting, suitable for labs of all sizes. This includes an examination of requirements analysis, the development of a data dictionary for routine data elements, the implementation of unique subject identifiers, and the formulation of standard operating procedures (SOPs) for seamless data flow. To aid researchers in implementing these practices, we present a simple organizational system as an illustrative example, which can be tailored to suit individual needs and research requirements.By presenting a user-friendly approach, this guide serves as an introduction to the field of RDM and offers practical tips to help researchers effortlessly meet the common data management and sharing mandates rapidly becoming prevalent in biomedical research.

Neurophotonics: a comprehensive review, current challenges and future trends.

The human brain, with its vast network of billions of neurons and trillions of synapses (connections) between diverse cell types, remains one of the greatest mysteries in science and medicine. Despite extensive research, an understanding of the underlying mechanisms that drive normal behaviors and response to disease states is still limited. Advancement in the Neuroscience field and development of therapeutics for related pathologies requires innovative technologies that can provide a dynamic and systematic understanding of the interactions between neurons and neural circuits. In this work, we provide an up-to-date overview of the evolution of neurophotonic approaches in the last 10 years through a multi-source, literature analysis. From an initial corpus of 243 papers retrieved from Scopus, PubMed and WoS databases, we have followed the PRISMA approach to select 56 papers in the area. Following a full-text evaluation of these 56 scientific articles, six main areas of applied research were identified and discussed: (1) Advanced optogenetics, (2) Multimodal neural interfaces, (3) Innovative therapeutics, (4) Imaging devices and probes, (5) Remote operations, and (6) Microfluidic platforms. For each area, the main technologies selected are discussed according to the photonic principles applied, the neuroscience application evaluated and the more indicative results of efficiency and scientific potential. This detailed analysis is followed by an outlook of the main challenges tackled over the last 10 years in the Neurophotonics field, as well as the main technological advances regarding specificity, light delivery, multimodality, imaging, materials and system designs. We conclude with a discussion of considerable challenges for future innovation and translation in Neurophotonics, from light delivery within the brain to physical constraints and data management strategies.

Design and Implementation of an Automated Disaster-Recovery System for a Kubernetes Cluster Using LSTM

With the increasing importance of data in modern business environments, effective data management and protection strategies are gaining increasing research attention. Data protection in a cloud environment is crucial for safeguarding information assets and maintaining sustainable services. This study introduces a system structure that integrates Kubernetes management platforms with backup and restoration tools. This system is designed to immediately detect disasters and automatically recover applications from another Kubernetes cluster. The experimental results show that this system executes the restoration process within 15 s without human intervention, enabling rapid recovery. This, in turn, significantly reduces the potential for delays and errors compared to manual recovery processes, thereby enhancing data management and recovery efficiency in cloud environments. Moreover, our research model predicts the CPU utilization of the cluster using Long Short-Term Memory (LSTM). The necessity of scheduling through this predict is made clearer through comparison with experiments without scheduling, demonstrating its ability to prevent performance degradation. This research highlights the efficiency and necessity of automatic recovery systems in cloud environments, setting a new direction for future research.

ADVANCING DATA SECURITY IN GLOBAL BANKING: INNOVATIVE BIG DATA MANAGEMENT TECHNIQUES

In the context of the rapidly evolving digital landscape, the banking sector faces unprecedented challenges in data security due to the massive volumes of sensitive information they manage. This paper examines the implementation and efficacy of innovative big data management techniques within global banking institutions to enhance data security. It discusses the integration of predictive analytics, the impact of regulatory changes, and the adoption of emerging technologies like blockchain and advanced encryption, which collectively redefine data security strategies. The study utilises a qualitative case study approach focusing on three central banks—JPMorgan Chase & Co., HSBC Holdings plc, and the Industrial and Commercial Bank of China—highlighting how each bank utilises big data techniques to address specific security challenges, comply with regulations, and enhance customer trust. The findings underscore the crucial role of innovative data management strategies in mitigating risks and safeguarding data against cyber threats, suggesting that these technologies fulfil security needs and offer competitive advantages in customer trust and regulatory compliance. The paper concludes with strategic recommendations for banks to enhance their data security measures and suggests directions for future research in data security within the banking industry.

IoT applications in asset management: A review of accounting and tracking techniques

In the digital age, the confluence of Internet of Things (IoT) technologies with asset management practices heralds a transformative era, promising unprecedented efficiency and strategic advantage. This paper delves into the intricacies of this integration, exploring the evolution, challenges, and potential of IoT in reshaping asset management paradigms. With a keen aim to elucidate the transformative impact of IoT and navigate the complexities of its integration into asset management systems, the study adopts a qualitative research methodology, underpinned by a systematic literature review. This approach enables a comprehensive exploration of the subject matter, ensuring a nuanced understanding of the IoT's potential and pitfalls within the asset management domain. The findings illuminate a landscape where IoT applications significantly enhance asset utilization rates, underscored by empirical success stories and quantifiable improvements in operational efficiency. However, the journey reveals barriers—technical, organizational, and cultural—that impede seamless IoT adoption. Addressing these challenges, the paper offers strategic recommendations, emphasizing the need for strategic alignment, inter-organisational collaboration, and robust data management strategies to overcome integration hurdles. Conclusively, the study not only achieves its aim of highlighting the potential of IoT in asset management but also charts a course for future endeavors in this field. It posits that the strategic integration of IoT technologies stands as a cornerstone for the next generation of asset management practices, promising a future marked by enhanced efficiency, economic viability, and strategic agility. The recommendations provided serve as a compass for organizations embarking on the IoT integration journey, guiding them towards a horizon of transformative potential.

Online Education Big Data Management and Mining Based on Intelligent Technology

The advent of online education platforms has revolutionized traditional learning paradigms, providing unprecedented access to educational resources worldwide. However, the vast amounts of data generated by these platforms present significant challenges in terms of effective management and utilization. This paper proposes a comprehensive approach to address these challenges through the integration of big data management and mining techniques, supported by intelligent technologies. By harnessing the power of advanced data management strategies and cutting-edge machine learning algorithms, our approach aims to unlock valuable insights from online education data, thereby enhancing the learning experience and improving educational results. This paper presents a systematic framework for online education big data management and mining, encompassing data collection, storage, preprocessing, analysis, and visualization. Furthermore, we discuss the role of intelligent technologies such as artificial intelligence, natural language processing, and predictive analytics in optimizing the process of data mining and knowledge discovery. Through case studies and experimental evaluations, we demonstrate the effectiveness and applicability of our approach in real-world online education scenarios. Our research contributes to the growing body of knowledge in the field of educational data mining and underscores the potential of intelligent technology in shaping the future of online learning.

ARTIFICIAL INTELLIGENCE ADOPTION IN INVESTMENT MANAGEMENT COMPANIES

This analysis delves into the evolving landscape of artificial intelligence (AI) adoption within the financial services industry, juxtaposed against broader market trends. Drawing insights from industry experts and research findings, it examines key challenges and opportunities faced by financial institutions in leveraging AI technologies to drive innovation and competitive advantage. The study highlights the critical importance of data management strategies, cultural transformation, and talent development in facilitating successful AI implementation. It underscores the significance of striking a balance between centralization and federation in data management approaches, alongside the imperative of strengthening ethics and bias management practices. Furthermore, the analysis delves into the pivotal role of multidisciplinary AI teams, emphasizing the necessity of integrating diverse skill sets, including data scientists, business experts, and senior executives, to maximize the efficacy of AI initiatives. It also sheds light on regulatory developments, such as the Canadian government's Algorithmic Impact Assessment (AIA), aimed at fostering transparency and accountability in automated decision-making systems. Overall, this study provides valuable insights into the challenges and opportunities inherent in AI adoption within the financial services sector, offering recommendations to guide firms towards sustainable AI-driven growth and innovation.

ФУНКЦІОНАЛЬНІ МОЖЛИВОСТІ СИСТЕМ КЕРУВАННЯ БАЗАМИ ДАНИХ ЧАСОВИХ РЯДІВ

This work provides a comprehensive overview of the capabilities of time series databases, with a special emphasis on InfluxDB –a leading time series database known for its efficiency and support for complex analyses through the Flux query language, including data aggregation, filtering, trend detection, and forecasting. The practical application of InfluxDB is demonstrated through various examples, showcasing its versatility in performing complex data operations, such as data filtering, identifying significant changes in data, and conducting stochastic analysis. These functionalities highlight the potential of InfluxDB across a broad range of applications, from IoT telemetry to financial analysis, underscoring the critical role of time series databases in modern data management and analysis strategies.

Redactable Blockchain-Based Secure and Accountable Data Management

Blockchain provides trustworthy properties such as decentralization, traceability, and transparency, and has been applied in various fields. Given the complexity of guaranteeing the accuracy of the input data, erroneous data may be stored on the blockchain, making it hard to modify. As an effective solution, redactable blockchain allows on-chain data to be modified by introducing a chameleon hash function with a trapdoor, which enables on-chain erroneous data to be corrected. However, existing redactable blockchain solutions often require trusted third parties, have huge overhead, or lack effective accountability mechanisms to meet data security needs. Therefore, this paper proposes a secure, efficient and accountable data management scheme based on redactable blockchain. To cope with the possible frequent editing needs, we design an efficient and accountable distributed trapdoor recovery mechanism that reliably maintains data security while avoiding the security risks associated with centralized management. Various information during the trapdoor management process is also recorded on the chain as the basis for implementing accountability mechanisms. In addition, the one-time trapdoor technology allows the blockchain system to reduce the maintenance complexity of the system by eliminating the need to frequently update the system parameters when partial trapdoor information needs to be published. Theoretical and experimental results show that our scheme can achieve an efficient accountability mechanism while modifying the data on the chain at low cost.

Utilization of Data Analytics to Enhance Operational Efficiency in Manufacturing Companies

In the digital era, manufacturing industries confront challenges like heightened global competition and intricate production processes, urging them to boost efficiency and productivity. Amidst these circumstances, Big Data emerges as a pivotal opportunity to enhance manufacturing performance. Big Data, characterized by vast volumes of data, utilizes advanced data mining to machine learning techniques for analysis. Data analytics, an interdisciplinary field, profoundly impacts manufacturing operations, enabling deeper insights into production processes. By analyzing production data, companies identify inefficiencies, streamline workflows, and enhance operational efficiency and productivity. Predictive maintenance through sensor data analysis prevents machine failures, while logistics data analysis optimizes supply chains and inventory management, reducing costs and enhancing competitiveness. However, implementing Big Data analytics presents challenges such as rapid data growth, diverse data sources, real-time insights, skill shortages, and data fragmentation. Overcoming these hurdles requires robust technology, skilled personnel, and effective data management strategies. Examples of Big Data analytics applications include customer behavior analysis by Amazon and Netflix, fraud detection in insurance, and urban mobility optimization. Success factors in data analytics implementation include effective data-driven communication, technology integration, and skill enhancement. In conclusion, implementing Big Data Analytics in manufacturing promises significant benefits in operational efficiency, product quality, and competitiveness. Overcoming challenges necessitates robust strategies and consideration of ethical and security issues, ensuring responsible data usage. With a deep understanding of Big Data Analytics, manufacturing companies can leverage this technology to achieve higher efficiency and competitiveness in the global market.

Unlocking Secrets: Bioinformatics' Impact on Forensic Bio-Examinations

Bioinformatics (BI) is a diverse and interdisciplinary domain that studies biological data through computational techniques. Its rapid technological progress has led to a significant increase in biological data production, sparking a notable expansion in BI research and application. In recent years, the convergence between BI and forensic science has become increasingly evident, with the growing use of BI tools and techniques in forensic investigations. BI is not only extensively used in biomedical research to analyse biological data and forecast gene regulation but also in the development of drugs targeting disorders linked to epigenetic alterations. It merges principles from various fields like biology, computer science, and statistics to process extensive omics data, create databases, predict structures, and model metabolic processes. The impact of BI on biomedical research is substantial, aiding in data processing, prediction, and visualisation. Moreover, BI is increasingly important in analysing data generated through Sanger-based electrophoresis methods and massively parallel sequencing in forensic DNA analysis, demonstrating its practical application in the field. Therefore, BI plays a critical role in forensic identification, searching databases side by side, forensic DNA phenotyping and biographic ancestry, forensic genetic genealogy, and forensic epigenomics. An overview of different bioinformatic methodologies employed in forensic applications, exploring approaches for managing data and investigating trend analysis strategies within forensic contexts, is discussed. Additionally, it investigates data management strategies. The primary objective was to explore how BI facilitates forensic BI's use, integration, and impact. Utilising a systematic literature review and content analysis, the study examined peer-reviewed articles, conference proceedings, and reports from critical databases like Google Scholar and Scopus, focusing on publications from 2010 to 2024. Key findings highlight the significant advancements in genomic sequencing technologies and their application in personalised therapeutics. Forensic scientists can improve forensic exams' accuracy, reliability, and efficiency by using BI tools, metrics, and efficient data management procedures.

Abstract 1287: The ACCESS consortium: Advancing childhood cancer experience, science and survivorship in Canada

Abstract In Canada, >4,300 children, adolescents and young adults are diagnosed with cancer each year. 1/3 have refractory/metastatic disease or will relapse with an expected 5-year survival rate of <15%. The prognosis for most pediatric cancers has stagnated in the past 30 years and Canada trails the US and Europe in providing accessible clinical trials and improved outcomes. Advocacy for change by patients and families led to a federal government investment of $30M in pediatric cancer research. $23M was allocated to establishing a national pediatric cancer consortium. “ACCESS: advancing childhood cancer experience, science and survivorship” is the new multi-stakeholder network created with a vision that every Canadian child with cancer has access to the latest scientific advances, diagnostic tools, therapies and supportive care leading to better outcomes and quality of life. ACCESS will connect the Canadian pediatric cancer community, build partnerships, leverage research and clinical expertise across cancer types and ages, implement transformative change and create a unified national strategy. To date, >350 clinicians, researchers, policymakers, advocates, people with lived experience (PWLE; patients, families and survivors) and other stakeholders have been engaged, with a goal of representing Canada’s ethno-cultural, socioeconomic and geographic diversity. ACCESS is building partnerships between children, families, professionals, and existing programs to collaborate in coordinated research and knowledge mobilization. The goal is to implement transformative change in the delivery of fair, fast, effective and safe cancer care for all Canadian children. ACCESS has developed a statement of its values that includes patient/survivor and family centred; collaborative; anti-oppressive; innovation and excellence; accountable. Uniquely, ACCESS is an interdisciplinary and inclusive organization that directly engages and collaborates with PWLE at all levels of research and governance. ACCESS is organized into 7 themes that will address gaps, advance research and educate through all phases of the pediatric cancer journey: cancer biology; clinical trials; access to innovative therapies and optimal care; regulation, policy and economics; education and training; ethical, legal, societal implications and implementation science; and psychosocial and survivorship. In addition, there are two cross cutting groups focusing on Knowledge mobilization and Social justice, Indigenization and Inclusion. Collaborations with local, provincial, national and international initiatives are being established through the development of a partnership strategy. Cross-consortium priorities are being implemented, and a data management strategy is in development. The publication of a strategic and sustainability plan for ACCESS is anticipated in 2024. Citation Format: Christine J. Williams, Stephanie A. Grover, Adrienne Co-Dyre, David Malkin, James A. Whitlock, on behalf of the ACCESS consortium. The ACCESS consortium: Advancing childhood cancer experience, science and survivorship in Canada [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1287.

A Redactable Blockchain-Based Data Management Scheme for Agricultural Product Traceability.

With the development of agricultural information technology, the Internet of Things and blockchain have become important in the traceability of agricultural products. Sensors collect real-time data in agricultural production and a blockchain provides a secure and transparent storage medium for these data, which improves the transparency and credibility of agricultural product traceability. However, existing agricultural product traceability solutions are limited by the immutability of the blockchain, making it difficult to delete erroneous data and modify the scope of data sharing. This damages the credibility of traceability data and is not conducive to the exchange and sharing of information among enterprises. In this article, we propose an agricultural product traceability data management scheme based on a redactable blockchain. This scheme allows agricultural enterprises to encrypt data to protect privacy. In order to facilitate the maintenance and sharing of data, we introduce a chameleon hash function to provide data modification capabilities. Enterprises can fix erroneous data and update the access permissions of the data. To improve the efficiency of block editing, our scheme adopts a distributed block editing method. This method supports threshold editing operations, avoiding single-point-of-failure issues. We save records of data modifications on the blockchain and establish accountability mechanisms to identify malicious entities. Finally, in this paper we provide a security analysis of our proposed solution and verify its effectiveness through experiments. Compared with the existing scheme, the block generating speed is improved by 42% and the block editing speed is improved by 29.3% at 125 nodes.

A prototype field-to-publication data system for a multi-variable permafrost observation network

Analysis and prediction of permafrost change are hampered by lack of observational data. We implement a permafrost data management system to support multi-variable permafrost observation networks. It addresses five key challenges we identified for permafrost data management and publication: (1) existing data management strategies do not scale well, (2) data users have different skills and needs, (3) permafrost data are varied, (4) resources for permafrost data management are limited, and (5) existing permafrost data sources are difficult to integrate. This prototype system supports a workflow from observation to the distribution of interoperable data. It comprises a database with auxiliary software, a set of practices, and an ERDDAP server. We find the system simplifies data publication, hurdles in adapting CF conventions and ERDDAP for permafrost data remain, and technology cannot replace good practice. Our learning can inform organizations who collect, manage, or distribute permafrost data or those who manage large observation networks.

Automatic Scoring of Rhizoctonia Crown and Root Rot Affected Sugar Beet Fields from Orthorectified UAV Images Using Machine Learning.

Rhizoctonia crown and root rot (RCRR), caused by Rhizoctonia solani, can cause severe yield and quality losses in sugar beet. The most common strategy to control the disease is the development of resistant varieties. In the breeding process, field experiments with artificial inoculation are carried out to evaluate the performance of genotypes and varieties. The phenotyping process in breeding trials requires constant monitoring and scoring by skilled experts. This work is time demanding and shows bias and heterogeneity according to the experience and capacity of each individual person. Optical sensors and artificial intelligence have demonstrated great potential to achieve higher accuracy than human raters and the possibility to standardize phenotyping applications. A workflow combining red-green-blue and multispectral imagery coupled to an unmanned aerial vehicle (UAV), as well as machine learning techniques, was applied to score diseased plants and plots affected by RCRR. Georeferenced annotation of UAV-orthorectified images was carried out. With the annotated images, five convolutional neural networks were trained to score individual plants. The training was carried out with different image analysis strategies and data augmentation. The custom convolutional neural network trained from scratch together with pretrained MobileNet showed the best precision in scoring RCRR (0.73 to 0.85). The average per plot of spectral information was used to score the plots, and the benefit of adding the information obtained from the score of individual plants was compared. For this purpose, machine learning models were trained together with data management strategies, and the best-performing model was chosen. A combined pipeline of random forest and k-nearest neighbors has shown the best weighted precision (0.67). This research provides a reliable workflow for detecting and scoring RCRR based on aerial imagery. RCRR is often distributed heterogeneously in trial plots; therefore, considering the information from individual plants of the plots showed a significant improvement in UAV-based automated monitoring routines.

AI in Test Automation: Overcoming Challenges, Embracing Imperatives

This scholarly article delves into the intersection of Artificial Intelligence (AI) and Test Automation, thoroughly examining the challenges inherent in implementing AI methodologies and elucidating imperatives critical for successful integration within contemporary software testing frameworks. The research entails a comprehensive exploration of challenges, ranging from intricacies in data quality to algorithmic biases, tool complexities, and integration challenges, drawing on empirical evidence from case studies and real-world scenarios. The paper articulates imperatives essential for overcoming challenges and ensuring the efficacy of AI in test automation. It emphasizes the significance of structured training programs, meticulous data management strategies, and the cultivation of an organizational culture conducive to the seamless integration of AI technologies. Through a rigorous analysis of successful case studies, the article provides a scholarly basis for the formulation of strategies and solutions to surmount challenges faced by organizations adopting AI in testing practices. A visual matrix aligning challenges with corresponding imperatives adds scholarly rigor to the article, offering a comprehensive framework for understanding the intricate relationships between challenges and the imperative strategies required for resolution. Furthermore, the exploration of emerging trends and innovations anticipates the future trajectory of AI-driven test automation, contributing valuable insights for strategic planning in the realm of software testing. This scholarly work underscores the importance of a systematic and informed approach to AI in Test Automation. By addressing challenges with academic rigor and embracing imperative strategies grounded in empirical evidence, organizations can position themselves at the forefront of AIdriven testing practices, advancing the field with a scholarly foundation for continued exploration and innovation.

Mental Health and Addiction Data Use Cases: Macro Perspectives in Ontario.

The opioid crisis in Ontario has led to a surge in preventable overdose deaths. Challenges in the mental health and addiction system, along with various contributing factors, have amplified this crisis. Underutilization of data exacerbates service gaps and hinders innovative solutions. Through stakeholder engagement, interrelated problems emerged, emphasizing the pervasive data underutilization. This research explores data usage in mental health and addictions, focusing on the opioid epidemic in Ontario and comparative jurisdictions. To improve service quality, Ontario should implement a comprehensive data management strategy. Two key recommendations include increased investment in exploring additional data use cases and evaluating policy initiatives using dynamic models throughout a patient's journey.

Equity, Diversity, and Inclusion in Institutional Research Data Management Strategies

Research data management (RDM) is a field of emerging concern for academic librarians. As funder agencies increasingly mandate institutions and researchers to ethically and responsibly manage their research data, academic librarians are frequently tasked with creating institutional strategies and services to support researchers. This article explores how a racialized librarian at a medium-sized, teaching-focused Canadian university created an institutional research data management strategy through a process informed by critical librarianship research and contributive justice (Gomberg, 2016; Honma & Chu, 2018). It examines the lack of equity, diversity, and inclusion (EDI) principles in both funder directives and RDM research literature and proposes an approach to do institutional RDM work in an EDI-centered way.

Managing Data from Maritime Archaeology Investigations: AIR at Gribshunden

Abstract Maritime archaeology excavations, even in a more complex environment, have the same objectives and requirements as terrestrial ones: documenting digging operations to analyze data in the best way, interpreting it, and producing new knowledge for the benefit of multiple communities. 3D documentation and management methodologies have spread widely among practitioners; however, 3D comprehensive tools for scholarly publications still require development. This article contributes to the discussion on archaeological data management and its need to encompass every aspect of the archaeological practice (Kansa & Kansa 2021). It presents the evolution of the documentation and data management strategies employed within the multidisciplinary project at the shipwreck site of Gribshunden (Rönnby 2021), profoundly conditioned by the project’s goals to optimize collection and analysis of its multidisciplinary nature. The use of the Archaeological Interactive Report (AIR) represents the most suitable solution for achieving such goals, as it addresses the issue of scattered data and allows for maintaining the connection between archaeological datasets and their interpretation and publication, which is crucial for performing archaeological Digital Data Curation (Dallas 2015a).

Dealing with sampling bias and inferring absence data to improve distribution models of a widely distributed vulnerable marsupial

AbstractSpecies distribution models are widely used to identify potential and high‐quality habitat of endangered species to inform conservation decisions. However, their usefulness is constrained by the amount and quality of biodiversity data and the approaches for dealing with data deficiencies. Presence‐only data, used in presence/background modelling methods, are widely available but are often affected by sampling bias. Presence/absence modelling methods are less affected by biases, but data are less common. We modelled the distribution of a widely distributed, endangered species from Australia – the greater glider – and tested how predictions were influenced by data treatment and modelling framework. We collated available species data and fitted generalized linear models and boosted regression trees using presence/absence data, as well as using an augmented dataset that included additional presences alongside absences inferred from survey data. We also fitted presence/background models, adopting three common strategies for bias correction. We compared model performance quantitatively through evaluation metrics calculated internally and on held out data, and qualitatively by identifying areas of agreement of spatial predictions. We found that presence/background models with bias correction performed better than not corrected, though evaluation metrics did not favour a single strategy. Presence/absence models outperformed presence/background models in comparable metrics and delivered different spatial predictions. Importantly, differences in spatial predictions between models had the potential to substantially alter decisions about where to protect high‐quality habitat. The approach to inferring absences proved useful, as models fitted with these outperformed all other models. Dealing with sampling bias requires additional time and data management strategies, but we found that the time invested allowed improvement of models and more reliable predictions. Our results suggest that ancillary occurrence data and careful data handling can improve both presence/background and presence/absence models.