Existing Data Repositories Research Articles

RCDdb: A manually curated database and analysis platform for regulated cell death

Regulated cell death is a pivotal regulatory mechanism governing the development and homeostasis of multicellular organisms. A comprehensive understanding of RCD's regulatory mechanisms is crucial for developing novel therapeutic strategies against diseases associated with cell death, such as cancer and neurodegenerative diseases. However, existing data repositories support limited types of cell death data and lack comprehensive annotation and analytical functionalities. Thus, establishing an extensive cell death database is an urgent imperative. To address this gap, we developed the Regulated Cell Death Database (RCDdb, chenyclab.com/RCDdb), the first comprehensively manually annotated database designed to support annotations and analytical capabilities across all RCD types. We compiled 3090 marker gene annotations associated with 15 RCD types from 2180 relevant articles. The RCDdb includes annotation data on these marker genes concerning diseases, drugs, pathways, proteins, and gene expressions. Furthermore, it provides 49 diverse visualization methods to present this information. More importantly, the RCDdb features three online analysis tools for identifying and analyzing RCD-related features within user-submitted data. Furthermore, the RCDdb offers a user-friendly interface for querying, browsing, analysis, and visualization of detailed information associated with each RCD category. This resource promises to significantly aid researchers in better understanding the mechanisms of cell death, thereby accelerating progress in research and therapeutic strategies aimed at combating RCD-related diseases.

TGC-ARG: Anticipating Antibiotic Resistance via Transformer-Based Modeling and Contrastive Learning.

In various domains, including everyday activities, agricultural practices, and medical treatments, the escalating challenge of antibiotic resistance poses a significant concern. Traditional approaches to studying antibiotic resistance genes (ARGs) often require substantial time and effort and are limited in accuracy. Moreover, the decentralized nature of existing data repositories complicates comprehensive analysis of antibiotic resistance gene sequences. In this study, we introduce a novel computational framework named TGC-ARG designed to predict potential ARGs. This framework takes protein sequences as input, utilizes SCRATCH-1D for protein secondary structure prediction, and employs feature extraction techniques to derive distinctive features from both sequence and structural data. Subsequently, a Siamese network is employed to foster a contrastive learning environment, enhancing the model's ability to effectively represent the data. Finally, a multi-layer perceptron (MLP) integrates and processes sequence embeddings alongside predicted secondary structure embeddings to forecast ARG presence. To evaluate our approach, we curated a pioneering open dataset termed ARSS (Antibiotic Resistance Sequence Statistics). Comprehensive comparative experiments demonstrate that our method surpasses current state-of-the-art methodologies. Additionally, through detailed case studies, we illustrate the efficacy of our approach in predicting potential ARGs.

Assessment of Epidemiological Data and Surveillance in Korea Substance Use Research: Insights and Future Directions.

Effective data collection and surveillance of epidemiological trends are essential in confronting the growing challenges associated with substance use (SU), especially in light of emerging trends and underreporting of cases. However, research and data are scarce regarding SU and substance use disorder (SUD) in Korea. We conducted a scoping review to identify data sources and surveillance methods used in SU research in Korea up to December 2023. This review was complemented by semi-structured consultations with experts in this area in Korea, whose feedback led to revisions of previously identified data sources and assessments. Our review identified 32 publications conducting secondary analyses on existing data to examine the epidemiology of SU and SUD in Korea. Of these, 14 studies utilized clinical databases to explore the prescription patterns of addictive substances, particularly opioids. Eleven data sources showed promise for advancing SU research; however, they face substantial limitations, including a lack of available data, missing data, the absence of key variables, the exclusion of marginalized populations not captured within the clinical system, and complexities in matching individual-level data across time points and datasets. Current surveillance methods for SU in Korea face considerable challenges in accessibility, usability, and standardization. Moreover, existing data repositories may fail to capture information on populations not served by clinical or judicial systems. To systematically improve surveillance approaches, it is necessary to develop a robust and nationally representative survey, refine the use of existing clinical data, and ensure the availability of data on treatment facilities.

GlyComb: A novel glycoconjugate data repository that bridges glycomics and proteomics

The glycosylation of proteins and lipids is known to be closely related to the mechanisms of various diseases such as influenza, cancer, and muscular dystrophy. Therefore, it has become clear that the analysis of post-translational modifications of proteins, including glycosylation, is important to accurately understand the functions of each protein molecule and the interactions among them. In order to conduct large-scale analyses more efficiently, it is essential to promote the accumulation, sharing, and reuse of experimental and analytical data in accordance with the FAIR (Findability, Accessibility, Interoperability, and Re-usability) data principles. However, a FAIR data repository for storing and sharing glycoconjugate information, including glycopeptides and glycoproteins, in a standardized format did not exist. Therefore, we have developed GlyComb (https://glycomb.glycosmos.org) as a new standardized data repository for glycoconjugate data. Currently, GlyComb can assign a unique identifier to a set of glycosylation information associated with a specific peptide sequence or UniProt ID. By standardizing glycoconjugate data via GlyComb identifiers and coordinating with existing web resources such as GlyTouCan and GlycoPOST, a comprehensive system for data submission and data sharing among researchers can be established. Here we introduce how GlyComb is able to integrate the variety of glycoconjugate data already registered in existing data repositories to obtain a better understanding of the available glycopeptides and glycoproteins, and their glycosylation patterns. We also explain how this system can serve as a foundation for a better understanding of glycan function.

Postmortem Brain Imaging in Alzheimer's Disease and Related Dementias: The South Texas Alzheimer's Disease Research Center Repository.

Neuroimaging bears the promise of providing new biomarkers that could refine the diagnosis of dementia. Still, obtaining the pathology data required to validate the relationship between neuroimaging markers and neurological changes is challenging. Existing data repositories are focused on a single pathology, are too small, or do not precisely match neuroimaging and pathology findings. The new data repository introduced in this work, the South Texas Alzheimer's Disease research center repository, was designed to address these limitations. Our repository covers a broad diversity of dementias, spans a wide age range, and was specifically designed to draw exact correspondences between neuroimaging and pathology data. Using four different MRI sequences, we are reaching a sample size that allows for validating multimodal neuroimaging biomarkers and studying comorbid conditions. Our imaging protocol was designed to capture markers of cerebrovascular disease and related lesions. Quantification of these lesions is currently underway with MRI-guided histopathological examination. A total of 139 postmortem brains (70 females) with mean age of 77.9 years were collected, with 71 brains fully analyzed. Of these, only 3% showed evidence of AD-only pathology and 76% had high prevalence of multiple pathologies contributing to clinical diagnosis. This repository has a significant (and increasing) sample size consisting of a wide range of neurodegenerative disorders and employs advanced imaging protocols and MRI-guided histopathological analysis to help disentangle the effects of comorbid disorders to refine diagnosis, prognosis and better understand neurodegenerative disorders.

Statistical and topological summaries aid disease detection for segmented retinal vascular images.

Disease complications can alter vascular network morphology and disrupt tissue functioning. Microvascular diseases of the retina are assessed by visual inspection of retinal images, but this can be challenging when diseases exhibit silent symptoms or patients cannot attend in-person meetings. We examine the performance of machine learning algorithms in detecting microvascular disease when trained on statistical and topological summaries of segmented retinal vascular images. We compute 13 separate descriptor vectors (5 statistical, 8 topological) to summarize the morphology of retinal vessel segmentation images and train support vector machines to predict each image's disease classification from the summary vectors. We assess the performance of each descriptor vector, using five-fold cross validation to estimate their accuracy. We apply these methods to four datasets that were assembled from four existing data repositories; three datasets contain segmented retinal vascular images from one of the repositories, whereas the fourth "All" dataset combines images from four repositories. Among the 13 total descriptor vectors considered, either a statistical Box-counting descriptor vector or a topological Flooding descriptor vector achieves the highest accuracy levels. On the combined "All" dataset, the Box-counting vector outperforms all other descriptors, including the topological Flooding vector which is sensitive to differences in the annotation styles between the different datasets. Our work represents a first step to establishing which computational methods are most suitable for identifying microvascular disease and assessing their current limitations. These methods could be incorporated into automated disease assessment tools.

Validation of a neighborhood sentiment and safety index derived from existing data repositories.

The communities we live in are central to our health. Neighborhood disadvantage is associated with worse physical and mental health and even early mortality, while resident sense of safety and positive neighborhood sentiment has been repeatedly linked to better physical and mental health outcomes. Therefore, understanding where negative neighborhood sentiment and safety are salient concerns can help inform public health interventions and as a result, improve health outcomes. To date, fear of crime and neighborhood sentiment data or indices have largely been based on the administration of time consuming and costly standardized surveys. The current study aims to develop a Neighborhood Sentiment and Safety Index (NSSI) at the census tract level, building on publicly available data repositories, including the US Census and ACS surveys, Data Axle, and ESRI repositories. The NSSI was created using Principal Component Analysis. Mineigen and minimum loading values were 1 and 0.3, respectively. Throughout the step-wise PCA process, variables were excluded if their loading value was below 0.3 or if variables loaded into multiple components. The novel index was validated against standardized survey items from a longitudinal cohort study in the Northeastern United States characterizing experiences of (1) Neighborhood Characteristics with a Pearson correlation of -0.34 (p < 0.001) and, (2) Neighborhood Behavior Impact with a Pearson correlation of -0.33 (p < 0.001). It also accurately predicted the Share Care Community Well Being Index (Spearman correlation = 0.46) and the neighborhood deprivation index (NDI) (Spearman correlation = -0.75). Our NSSI can serve as a predictor of neighborhood experience where data is either unavailable or too resource consuming to practically implement in planned studies. To date, fear of crime and neighborhood sentiment data or indices have largely been based on the administration of time consuming and costly standardized surveys. The current study aims to develop a Neighborhood Sentiment and Safety Index (NSSI) at the census tract level, building on publicly available data repositories, including the US Census and ACS surveys, Data Axle, and ESRI repositories. The NSSI was validated against four separate measures and can serve as a predictor of neighborhood experience where data is either unavailable or too resource consuming to practically implement in planned studies.

Agile Research Data Management with FDOs using LinkAhead

Agile Research Data Management with FDOs using LinkAhead

Signed Citations: Making citations of digital scientific content persistent

Digital data are a foundation of 21st century science. In order to maintain a stable foundation, the FAIR Guiding Principles (Wilkinson et al. 2016) were proposed to keep data findable, accessible, interoperable, and reusable (FAIR). However, commonly used data citation practices rely on unverifiable retrieval methods that do not always enable access to the cited data. Without verifiability, retrieval methods are susceptible to undetected “content drift”, which occurs when the data associated with an identifier have been allowed to change. In the presence of content drift, cited data may lose their findability. We propose signed citations, i.e., customary data citations extended to include a standards-based, secure, unique, and fixed-length digital content signature. A content signature is a code that is unique to the data it identifies and can be reliably recovered from the data. For example, the signature of a dataset could be the SHA-256 hash (Dang 2015) of its content. We show that the inclusion of content signatures in citations not only enables independent verification of the cited content, but also can improve the reliability and availability of the citation, allowing the cited data to remain findable for longer periods of time and across changing online infrastructures. If a content signature registry is available which links content signatures to one or more (possibly temporary) known content locations, then content signatures can themselves be used to find identified data. That is, registries make content signatures “resolvable” just like URLs and DOIs. Additionally, signed citations are location- and storage-medium-agnostic, allowing the making of as many copies of cited data as necessary to ensure content persistence across current and future storage media and data networks. As a result, content signatures can be leveraged to help scalably store, locate, access, and independently verify content across new and existing data repositories, search engines, and registries (such as those that exist within services offered by Zenodo, DataOne, and the Software Heritage archive) without requiring any time-sensitive information (e.g. URLs or references to specific infrastructures) to be baked into the citation. Signed citations can also be used to reliably identify complex data networks and knowledge graphs. By embedding content signatures inside content and then citing that content with a signed citation, a secure (unforgeable, irrevocable, self-verifying) link is formed between the cited content and those identified by embedded content signatures. Such links create secure data graphs that are annotatable and machine-traversable, acting as a mechanism for manual and automated discovery, which are vital to findability according to the FAIR guidelines (Wilkinson et al. 2016). Additionally, entire knowledge graphs can be similarly securely cited using a single signed citation. Our proposal originates from our earlier work on reliable dataset identifiers (Elliott et al. 2020). In addition to further discussing signed citations as stated above, we expand upon our previous work by describing real-world examples of the use of content signatures, including signed citations of a corpus of digitized images of bee specimens from natural history collections, datasets which collectively contain over a billion records available through global biodiversity data networks, and a corpus of taxonomic name resources. Our use of signed citations in these real-world examples offers a starting point for the development of community standards on how to build, use, and support independent yet interoperable signature-based services such as content registries, repositories, and search indexes.

EchidnaCSI – Improving monitoring of a cryptic species at continental scale using Citizen Science

Short-beaked echidna (Tachyglossus aculeatus) are a cryptic and iconic monotreme found throughout the continent of Australia. Despite observational records spanning many years aggregated in national and state biodiversity databases, the spatial and temporal intensity of sightings is limited. Although the species is of least conservation concern at the global level, a subspecies has been declared endangered on Kangaroo Island in South Australia. We need better population data over the whole continent to inform this species’ conservation management. To increase the temporal and spatial resolution of observations which may be used for more accurate population assessments, we developed a mobile app for citizen scientists to easily record echidna sightings and improve the quantity, quality and distribution of data collected for monitoring this species. EchidnaCSI is a free, cross-platform (Android & iOS), open-source app that we developed to collect echidna observational data around Australia. EchidnaCSI has been in use since September 2017 and uses mobile phone sensors to transparently and automatically record metadata, such as species observation location and time and GPS location precision. We examine differences in spatial coverage between these observations and those in existing data repositories in the Atlas of Living Australia and state biodiversity databases, especially in relation to observations in protected areas and to an index of remoteness and accessibility. EchidnaCSI has contributed over 8000 echidna observations from around Australia, more than recorded in all state systems combined, with similar spatial distribution. Although coverage was more limited in some protected areas than the reference data sources, numbers of observations in all remote areas were greater than the reference scientific data except for very remote regions. EchidnaCSI has improved the spatial and temporal intensity of observations for this iconic species and provides a complement to scientific surveys, which might usefully focus on highly protected areas and very remote regions.

NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany

The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation. This overarching goal is achieved by working towards a number of key objectives: Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories. Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack. Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula. Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers. Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI. Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM.

Understanding Australia’s national feral cat control effort

Abstract Context. Feral cats (Felis catus) pose a significant threat to Australia’s native species and feral cat control is, therefore, an important component of threatened species management and policy. Australia’s Threatened Species Strategy articulates defined targets for feral cat control. Yet, currently, little is known about who is engaged in feral cat control in Australia, what motivates them, and at what rate they are removing feral cats from the environment. Aims. We aim to document who is engaging in feral cat control in Australia, how many cats they remove and to estimate the number of feral cats killed in a single year. Furthermore, we seek to better understand attitudes towards feral cat control in Australia. Methods. We used a mixed methods approach combining quantitative and qualitative techniques. Feral cat control data were obtained from existing data repositories and via surveys targeting relevant organisations and individuals. A bounded national estimate of the number of feral cats killed was produced by combining estimates obtained from data repositories and surveys with modelled predictions for key audience segments. Attitudes towards feral cat control were assessed by exploring qualitative responses to relevant survey questions. Key results. We received information on feral cat control from three central repositories, 134 organisations and 2618 individuals, together removing more than 35000 feral cats per year. When including projections to national populations of key groups, the estimated number of feral cats removed from the environment in the 2017–2018 financial year was 316030 (95% CI: 297742–334318). Conclusions. Individuals and organisations make a significant, and largely unrecorded, contribution to feral cat control. Among individuals, there is a strong awareness of the impact of feral cats on Australia’s biodiversity. Opposition to feral cat control focussed largely on ethical concerns and doubts about its efficacy. Implications. There is significant interest in, and commitment to, feral cat control among some groups of Australian society, beyond the traditional conservation community. Yet more information is needed about control methods and their effectiveness to better understand how these efforts are linked to threatened species outcomes.

Towards the automated economic assessment of newborn screening for rare diseases.

Economic assessments of newborn screening programs for rare diseases involve the use of models and require huge efforts to synthesize information from different sources. Sharing and automatically or semi-automatically reusing this information for new assessments would be desirable, but it is not possible nowadays due to the lack of suitable tools. We designed and implemented the Rare Diseases Ontology for Simulation (RaDiOS) after performing two reviews, and critically appraising the existing data repositories on rare diseases. The first review involved previous published economic assessments, and served to identify the main parameters required to model newborn screening. The second review aimed at locating existing data repositories potentially available to inform these parameters. We found key model parameters on epidemiology, screening methods, diagnose methods, pathogenesis, treatment and follow-up tests. We also identified seven data repositories directly related to rare diseases. None of such repositories was well-suited for the automated generation of simulation models. We incorporated the identified parameters as structured classes and properties of the new ontology (RaDiOS). We carefully set the relationships among the parameters so to allow automated inference from the ontology. RaDiOS is an ontology that serves as a data repository to automatically build simulation models for the economic assessment of newborn screening for rare diseases.

Abstract B71: New directions in gynecologic cancer research utilizing Text Information Extraction System (TIES) Cancer Research Network

Abstract Gynecologic cancers pose significant morbidity and mortality burden for US women. Endometrial cancer is the most common gynecologic malignancy in the US, while ovarian cancer is one of the deadliest. Since research funding for some of the gynecologic malignancies has been limited, novel research on the existing datasets needs to be harnessed to improve the health of the women affected by these conditions. We aim to evaluate if Text Information Extraction System (TIES) Cancer Research Network, a federated network that facilitates data and biospecimen sharing among member institutions, has capabilities for conducting research in the area of gynecologic cancers. TIES system provides effective tools for searching deidentified structured and unstructured data for gynecologic cancers with capabilities to explore pathology and radiology records. We used TIES system to identify records of patients with ovarian, endometrial, and cervical cancers. We used temporal search features to identify patients who developed ovarian tumors after being diagnosed with endometrial tumors. We examined records for completeness of information on diagnosis, age, and procedure year. We found that TIES system has 13,762 records for endometrial cancer, 7,692 records for ovarian cancer, and 10,826 records for cervical cancer for Pittsburgh site. 306 records have been identified for patients who had both ovarian and endometrial malignancies. Nearly 100% of the records had information on age of the patient, year of the procedure, and exact diagnosis. TIES is a promising tool for conducting research in the area of gynecologic malignancies, especially for rare diseases such as concurrent endometrial and ovarian cancers. Existing data repositories like TIES need to be further explored for novel research questions in the area of gynecologic cancer. Citation Format: Faina Y. Linkov, Melissa Schwenk, Michael Davis, Robert Edwards, Michael Becich. New directions in gynecologic cancer research utilizing Text Information Extraction System (TIES) Cancer Research Network. [abstract]. In: Proceedings of the AACR Conference: Addressing Critical Questions in Ovarian Cancer Research and Treatment; Oct 1-4, 2017; Pittsburgh, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(15_Suppl):Abstract nr B71.

An Interactive Data Repository with Visual Analytics

Scientific data repositories have historically made data widely accessible to the scientific community, and have led to better research through comparisons, reproducibility, as well as further discoveries and insights. Despite the growing importance and utilization of data repositories in many scientific disciplines, the design of existing data repositories has not changed for decades. In this paper, we revisit the current design and envision interactive data repositories, which not only make data accessible, but also provide techniques for interactive data exploration, mining, and visualization in an easy, intuitive, and free-flowing manner.

Big data analytics as a service infrastructure: challenges, desired properties and solutions

CERN's accelerator complex generates a very large amount of data. A large volumen of heterogeneous data is constantly generated from control equipment and monitoring agents. These data must be stored and analysed. Over the decades, CERN's researching and engineering teams have applied different approaches, techniques and technologies for this purpose. This situation has minimised the necessary collaboration and, more relevantly, the cross data analytics over different domains. These two factors are essential to unlock hidden insights and correlations between the underlying processes, which enable better and more efficient daily-based accelerator operations and more informed decisions. The proposed Big Data Analytics as a Service Infrastructure aims to: (1) integrate the existing developments; (2) centralise and standardise the complex data analytics needs for CERN's research and engineering community; (3) deliver real-time, batch data analytics and information discovery capabilities; and (4) provide transparent access and Extract, Transform and Load (ETL), mechanisms to the various and mission-critical existing data repositories. This paper presents the desired objectives and properties resulting from the analysis of CERN's data analytics requirements; the main challenges: technological, collaborative and educational and; potential solutions.

The changing nature of nursing work in rural and small community hospitals

The nursing literature includes descriptions of rural nursing workforces in Canada, the United States of America and Australia. However, inconsistent definitions of rural demography, diverse employment conditions and health care system reorganization make comparisons of these data difficult. In 2007, the Ministry of Health and Long-term Care in Ontario, Canada, transferred responsibility for decision-making and funding to 14 regional governing bodies known as Local Health Integration Networks (LHINs). Little is known about rural-urban variations in the nursing workforces in the LHINs because existing data repositories do not describe them. This study investigated the influence of demographic characteristics, provincial policies, organizational changes and emerging practice challenges on nursing work in a geographically unique rural region. The purpose was to describe the nature of nursing work from the perspective of rural nurse executives and frontline nurses. The study was conducted in 7 small rural and community hospitals in the Hamilton Niagara Haldimand Brant LHIN. Data collection occurred between August and November 2007. A qualitative descriptive study design was chosen to facilitate exploration of nursing in the rural setting. Study participants were identified through purposive snowball sampling. All nurses, nurse managers and nurse executives currently employed in the 7 study hospitals were eligible to participate. Data collection included the use of questionnaires and semi-structured interviews. Memos were also created to describe the relevance and applicability of concepts, categories and properties emerging from the data. Themes were compared across interviews to determine relevance and value. Twenty-one nurses from 7 different hospitals participated. The nurses reflect the aging trend in the provincial and regional workforces of Ontario. All study participants anticipate a substantial increase in retirements during the next decade, which will alter the structure and capacity of the rural workforce. Rural nursing practice is generalist in nature, requiring personal flexibility and a broad knowledge base. The nurses in the study preferred this type of practice. However, they felt that new nurses have different values and goals and are more likely to choose the specialized practice opportunities available in urban tertiary centres. Structural changes to the health system influenced relationships between hospitals and altered the internal organization of individual hospitals. Nurse executives were positive about new opportunities for cost savings, sharing best practices and continuing education. Yet they also felt that organizational changes significantly increased their administrative responsibilities and limited their opportunities for communication with frontline nurses. The nurses thought that the changing organizational structures increased opportunities to seek multiple employers to augment the lack of full-time positions in the region. Many reported that part-time and casual nurses often seek employment in other hospitals and long-term care homes to supplement their income. However, multi-site employment within and across healthcare organizations contributes to scheduling issues because casual nurses are unavailable to fill vacant shifts. Patient transports, the implementation of e-technology and emerging disease patterns in the patient population were identified as additional practice challenges. This study has implications for health human resource planning in rural and small community hospitals. The findings indicate that demographic trends pose an immediate threat to the sustainability of the nursing workforce in the rural setting. Many nurses are nearing retirement, but the lack of opportunities for full-time positions as well as specialized and expanded nursing practice are attracting younger nurses to urban centres. Government policies focussing on the retention of clinical expertise, the recruitment of new graduates and expanding the role of registered practical nurses have been more difficult to implement in the rural setting. Implications for future research include the need to address data gaps to facilitate workforce planning and to evaluate the effectiveness of provincial recruitment and retention strategies in the rural context.

Data Mining as a Tool for Research and Knowledge Development in Nursing

The ability to collect and store data has grown at a dramatic rate in all disciplines over the past two decades. Healthcare has been no exception. The shift toward evidence-based practice and outcomes research presents significant opportunities and challenges to extract meaningful information from massive amounts of clinical data to transform it into the best available knowledge to guide nursing practice. Data mining, a step in the process of Knowledge Discovery in Databases, is a method of unearthing information from large data sets. Built upon statistical analysis, artificial intelligence, and machine learning technologies, data mining can analyze massive amounts of data and provide useful and interesting information about patterns and relationships that exist within the data that might otherwise be missed. As domain experts, nurse researchers are in ideal positions to use this proven technology to transform the information that is available in existing data repositories into useful and understandable knowledge to guide nursing practice and for active interdisciplinary collaboration and research.

Automating intellectual capital ? integrating measures for intangibles into corporate performance management applications

All organisations are measuring aspects of their performance and today they increasingly automate their metrics management using software applications. These range from simple spreadsheets to sophisticated performance management software solutions. Many of these application import data from existing electronic sources. However, as this research shows, this could be a barrier to including the important information about intangible performance drivers, as this information is often not readily available in existing data repositories. In the first part of this paper we develop ten key requirements for software applications in order to cope with intangibles; in the second part, these are then contrasted with empirical evidence collected from the 5000 largest firms in the USA.