Manual of tabular presentation; an outline of theory and practice in the presentation of statistical data in tables for publication,

Includes bibliographical footnotes and index.

Laser-derived particle size data from CRP-3, Victoria Land Basin, Antarctica: Implications for sequence and seismic stratigraphy

Seven hundred and nineteen samples from throughout the Cainozoic section in CRP-3 were analysed by a Malvern Mastersizer laser particle analyser, in order to derive a stratigraphic distribution of grain-size parameters downhole. Entropy analysis of these data (using the method of Woolfe and Michibayashi, 1995) allowed recognition of four groups of samples, each group characterised by a distinctive grain-size distribution. Group 1, which shows a multi-modal distribution, corresponds to mudrocks, interbedded mudrock/sandstone facies, muddy sandstones and diamictites. Group 2, with a sand-grade mode but showing wide dispersion of particle size, corresponds to muddy sandstones, a few cleaner sandstones and some conglomerates. Group 3 and Group 4 are also sand-dominated, with better grain-size sorting, and correspond to clean, well-washed sandstones of varying mean grain-size (medium and fine modes, respectively). The downhole disappearance of Group 1, and dominance of Groups 3 and 4 reflect a concomitant change from mudrock- and diamictite-rich lithology to a section dominated by clean, well-washed sandstones with minor conglomerates. Progressive downhole increases in percentage sand and principal mode also reflect these changes. Significant shifts in grain-size parameters and entropy group membership were noted across sequence boundaries and seismic reflectors, as recognised in others studies.

The validity of publication and citation counts for Sociology and other selected disciplines

The use of bibliometric data is a means of comparing. research productivity and scholarly. impact for individuals, work groups, institutions and nations within and between disciplines. Central to this debate is the notion that disciplines differ in the ways in which,they exchange ideas and disseminate information and therefore have diverse publishing and citation patterns. In this article we use two different approaches to compiling bibliometric data to compare publishing patterns of five different disciplines that encompass Molecular Biology; Administration/Political Science, Psychology,. Philosophy and Sociology/Anthropology. We find that the social sciences differ from each other as well as from the physical sciences in their publication and citation patterns. Further, while the different ways of organizing the data produce somewhat different results, the substantive findings for the general patterning of publications and citations of disciplines are consistent for both data sets. Sociology/Anthropology, when compared with the other disciplines, shows substantial differences across universities.

Participatory mapping in the design process of a spatial data infrastructure SDI: a case study in the Biosphere Reserve Rio Platano (Honduras)

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

Combining data mining and evolutionary computation for multi-criteria optimization of earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

Reconstructing transcriptional regulatory networks using data integration and text mining

Transcriptional Regulatory Networks (TRNs) are powerful tool for representing several interactions that occur within a cell. Recent studies have provided information to help researchers in the tasks of building and understanding these networks. One of the major sources of information to build TRNs is biomedical literature. However, due to the rapidly increasing number of scientific papers, it is quite difficult to analyse the large amount of papers that have been published about this subject. This fact has heightened the importance of Biomedical Text Mining approaches in this task. Also, owing to the lack of adequate standards, as the number of databases increases, several inconsistencies concerning gene and protein names and identifiers are common. In this work, we developed an integrated approach for the reconstruction of TRNs that retrieve the relevant information from important biological databases and insert it into a unique repository, named KREN. Also, we applied text mining techniques over this integrated repository to build TRNs. However, was necessary to create a dictionary of names and synonyms associated with these entities and also develop an approach that retrieves all the abstracts from the related scientific papers stored on PubMed, in order to create a corpora of data about genes. Furthermore, these tasks were integrated into @Note, a software system that allows to use some methods from the Biomedical Text Mining field, including an algorithms for Named Entity Recognition (NER), extraction of all relevant terms from publication abstracts, extraction relationships between biological entities (genes, proteins and transcription factors). And finally, extended this tool to allow the reconstruction Transcriptional Regulatory Networks through using scientific literature.

Nonparametric estimation of the survival function for ordered multivariate failure time data: a comparative study

In longitudinal studies of disease, patients may experience several events through a follow-up period. In these studies, the sequentially ordered events are often of interest and lead to problems that have received much attention recently. Issues of interest include the estimation of bivariate survival, marginal distributions and the conditional distribution of gap times. In this work we consider the estimation of the survival function conditional to a previous event. Different nonparametric approaches will be considered for estimating these quantities, all based on the Kaplan-Meier estimator of the survival function. We explore the finite sample behavior of the estimators through simulations. The different methods proposed in this article are applied to a data set from a German Breast Cancer Study. The methods are used to obtain predictors for the conditional survival probabilities as well as to study the influence of recurrence in overall survival.

OpenAIRE guidelines: supporting interoperability for Data Archives

OpenAIRE supports the European Commission Open Access policy by providing an infrastructure for researchers to comply with the European Union Open Access mandate. The current OpenAIRE infrastructure and services, resulting from OpenAIRE and OpenAIREplus FP7 projects, builds on Open Access research results from a wide range of repositories and other data sources: institutional or thematic publication repositories, Open Access journals, data repositories, Current Research Information Systems and aggregators. (...)

The mzTab Data Exchange Format: Communicating Mass-spectrometry-based Proteomics and Metabolomics Experimental Results to a Wider Audience.

The HUPO Proteomics Standards Initiative has developed several standardized data formats to facilitate data sharing in mass spectrometry (MS)-based proteomics. These allow researchers to report their complete results in a unified way. However, at present, there is no format to describe the final qualitative and quantitative results for proteomics and metabolomics experiments in a simple tabular format. Many downstream analysis use cases are only concerned with the final results of an experiment and require an easily accessible format, compatible with tools such as Microsoft Excel or R. We developed the mzTab file format for MS-based proteomics and metabolomics results to meet this need. mzTab is intended as a lightweight supplement to the existing standard XML-based file formats (mzML, mzIdentML, mzQuantML), providing a comprehensive summary, similar in concept to the supplemental material of a scientific publication. mzTab files can contain protein, peptide, and small molecule identifications together with experimental metadata and basic quantitative information. The format is not intended to store the complete experimental evidence but provides mechanisms to report results at different levels of detail. These range from a simple summary of the final results to a representation of the results including the experimental design. This format is ideally suited to make MS-based proteomics and metabolomics results available to a wider biological community outside the field of MS. Several software tools for proteomics and metabolomics have already adapted the format as an output format. The comprehensive mzTab specification document and extensive additional documentation can be found online.

Cancer Publication March 2008 (PDF 232 KB)

This statistics release gives details of the waiting times for patients accessing cancer services at hospitals in Northern Ireland during the month of March 2008. The data contained within this release reports on the performance of all Health and Social Care Trusts in Northern Ireland, measured against the three Priorities for Action (PfA) targets for 2007/08 relating to waiting times for cancer services in Northern Ireland during March 2008. åÊ

Publication trends of shared decision making in 15 high impact medical journals: a full-text review with bibliometric analysis.

BACKGROUND: Shared Decision Making (SDM) is increasingly advocated as a model for medical decision making. However, there is still low use of SDM in clinical practice. High impact factor journals might represent an efficient way for its dissemination. We aimed to identify and characterize publication trends of SDM in 15 high impact medical journals. METHODS: We selected the 15 general and internal medicine journals with the highest impact factor publishing original articles, letters and editorials. We retrieved publications from 1996 to 2011 through the full-text search function on each journal website and abstracted bibliometric data. We included publications of any type containing the phrase "shared decision making" or five other variants in their abstract or full text. These were referred to as SDM publications. A polynomial Poisson regression model with logarithmic link function was used to assess the evolution across the period of the number of SDM publications according to publication characteristics. RESULTS: We identified 1285 SDM publications out of 229,179 publications in 15 journals from 1996 to 2011. The absolute number of SDM publications by journal ranged from 2 to 273 over 16 years. SDM publications increased both in absolute and relative numbers per year, from 46 (0.32% relative to all publications from the 15 journals) in 1996 to 165 (1.17%) in 2011. This growth was exponential (P < 0.01). We found fewer research publications (465, 36.2% of all SDM publications) than non-research publications, which included non-systematic reviews, letters, and editorials. The increase of research publications across time was linear. Full-text search retrieved ten times more SDM publications than a similar PubMed search (1285 vs. 119 respectively). CONCLUSION: This review in full-text showed that SDM publications increased exponentially in major medical journals from 1996 to 2011. This growth might reflect an increased dissemination of the SDM concept to the medical community.

Is hospital discharge administrative data an appropriate source of information for cancer registries purposes? Some insights from four Spanish registries

Background. The use of hospital discharge administrative data (HDAD) has been recommended for automating, improving, even substituting, population-based cancer registries. The frequency of false positive and false negative cases recommends local validation. Methods. The aim of this study was to detect newly diagnosed, false positive and false negative cases of cancer from hospital discharge claims, using four Spanish population-based cancer registries as the gold standard. Prostate cancer was used as a case study. Results. A total of 2286 incident cases of prostate cancer registered in 2000 were used for validation. In the most sensitive algorithm (that using five diagnostic codes), estimates for Sensitivity ranged from 14.5% (CI95% 10.3-19.6) to 45.7% (CI95% 41.4-50.1). In the most predictive algorithm (that using five diagnostic and five surgical codes) Positive Predictive Value estimates ranged from 55.9% (CI95% 42.4-68.8) to 74.3% (CI95% 67.0-80.6). The most frequent reason for false positive cases was the number of prevalent cases inadequately considered as newly diagnosed cancers, ranging from 61.1% to 82.3% of false positive cases. The most frequent reason for false negative cases was related to the number of cases not attended in hospital settings. In this case, figures ranged from 34.4% to 69.7% of false negative cases, in the most predictive algorithm. Conclusions. HDAD might be a helpful tool for cancer registries to reach their goals. The findings suggest that, for automating cancer registries, algorithms combining diagnoses and procedures are the best option. However, for cancer surveillance purposes, in those cancers like prostate cancer in which care is not only hospital-based, combining inpatient and outpatient information will be required.

Chemical products : basic guide on labelling and safety data sheets

Hazardous chemical products have to comply with, amongst others, the provisions of a correct classification of danger, labelling and compilation of the safety data sheets. The aim is to protect people's health and the environment from exposure to hazardous chemicals- especially the health and safety of direct users, professionals or not, and the general public, via environmental exposure. This publication is intended to contribute to the knowledge of the objectives and basic aspects of these legal provisions, and thereby increase their degree of compliance in Andalusia and other european regions. This Guide is directed toward those people who, in the development of their professional activities, are in one way or another in contact with dangerous chemical products.

Chemical products : basic guide on labelling and safety data sheets

Hazardous chemical products have to comply with, amongst others, the provisions of a correct classification of danger, labelling and compilation of the safety data sheets. The aim is to protect people's health and the environment from exposure to hazardous chemicals- especially the health and safety of direct users, professionals or not, and the general public, via environmental exposure. This publication is intended to contribute to the knowledge of the objectives and basic aspects of these legal provisions, and thereby increase their degree of compliance in Andalusia and other european regions. This Guide is directed toward those people who, in the development of their professional activities, are in one way or another in contact with dangerous chemical products.