Baseline data requirements for assessing environmental impact /

Bibliography: p. 63-66.

Supplementary financial information, special data requirements, and agency functions and planning program at June 30... /

Auditors: Arthur Anderson, 1996 ; Geo S. Olive & Co., 1997 ; Olive, 1998-

Estimation of functional connectivity from electromagnetic signals and the amount of empirical data required

An increasing number of neuroimaging studies are concerned with the identification of interactions or statistical dependencies between brain areas. Dependencies between the activities of different brain regions can be quantified with functional connectivity measures such as the cross-correlation coefficient. An important factor limiting the accuracy of such measures is the amount of empirical data available. For event-related protocols, the amount of data also affects the temporal resolution of the analysis. We use analytical expressions to calculate the amount of empirical data needed to establish whether a certain level of dependency is significant when the time series are autocorrelated, as is the case for biological signals. These analytical results are then contrasted with estimates from simulations based on real data recorded with magnetoencephalography during a resting-state paradigm and during the presentation of visual stimuli. Results indicate that, for broadband signals, 50-100 s of data is required to detect a true underlying cross-correlations coefficient of 0.05. This corresponds to a resolution of a few hundred milliseconds for typical event-related recordings. The required time window increases for narrow band signals as frequency decreases. For instance, approximately 3 times as much data is necessary for signals in the alpha band. Important implications can be derived for the design and interpretation of experiments to characterize weak interactions, which are potentially important for brain processing.

Delegation of pricing authority to the sales force:an agency-theoretic perspective of its determinants and impact on performance

In industrial selling situations, the questions of what factors drive pricing authority delegation to salespeople and under what conditions price delegation is beneficial for the firm are often asked. To advance knowledge in this area, we (1) develop and empirically test a framework of important drivers of price delegation based on agency-theoretic research and (2) investigate the impact of price delegation on firm performance, taking into account agency theory variables as potential moderators. The study is based on data from a sample of 181 companies from the industrial machinery and electrical engineering industry in Germany. The results indicate that the degree of pricing delegation increases as information asymmetry between the salesperson and sales manager increases and as it becomes more difficult to monitor salespeople's efforts. Conversely, risk-aversion of salespeople is negatively related to the degree of price delegation. Furthermore, we find a positive effect of price delegation on firm performance, which is amplified when market-related uncertainty is high and when salespeople possess better customer-related information than their managers. Hence, our results clearly show that rigid, “one price fits all” policies are inappropriate in many B2B market situations. Instead, sales managers should grant their salespeople sufficient leeway to adapt prices to changing customer requirements and market conditions, especially in firms that operate in highly uncertain selling environments.

Open Access to Publications and Research Data in Horizon 2020: What Are the Requirements and How Can Institutional Repositories and OpenAIRE Help to Meet Them?

In the last decade the principle of Open Access to publicly funded research has been getting a growing support from policy makers and funders across Europe, both at national level and within the European Union context. At European level some of the first relevant steps taken by the European Research Council (ERC) with a statement supporting Open Access (2006), shortly followed by guidelines for researchers funded by the ERC (2007) stating that all peer-reviewed publications from ERC funded projects should be made openly accessible shortly after their publication. Those guidelines were revised in October 2013, reinforcing the mandatory character of the requirements and expanding them to monographs.

Guiding schools to improved performance using data envelopment analysis:an illustration with data from a local education authority

The educational process is characterised by multiple outcomes such as the achievement of academic results of various standards and non-academic achievements. This paper shows how data envelopment analysis (DEA) can be used to guide secondary schools to improved performance through role-model identification and target setting in a way which recognises the multi-outcome nature of the education process and reflects the relative desirability of improving individual outcomes. The approach presented in the paper draws from a DEA-based assessment of the schools of a local education authority carried out by the authors. Data from that assessment are used to illustrate the approach presented in the paper. (Key words: Data envelopment analysis, education, target setting.)

Design and implementation of a multisite data repository for pediatric epilepsy

This dissertation established a software-hardware integrated design for a multisite data repository in pediatric epilepsy. A total of 16 institutions formed a consortium for this web-based application. This innovative fully operational web application allows users to upload and retrieve information through a unique human-computer graphical interface that is remotely accessible to all users of the consortium. A solution based on a Linux platform with My-SQL and Personal Home Page scripts (PHP) has been selected. Research was conducted to evaluate mechanisms to electronically transfer diverse datasets from different hospitals and collect the clinical data in concert with their related functional magnetic resonance imaging (fMRI). What was unique in the approach considered is that all pertinent clinical information about patients is synthesized with input from clinical experts into 4 different forms, which were: Clinical, fMRI scoring, Image information, and Neuropsychological data entry forms. A first contribution of this dissertation was in proposing an integrated processing platform that was site and scanner independent in order to uniformly process the varied fMRI datasets and to generate comparative brain activation patterns. The data collection from the consortium complied with the IRB requirements and provides all the safeguards for security and confidentiality requirements. An 1-MR1-based software library was used to perform data processing and statistical analysis to obtain the brain activation maps. Lateralization Index (LI) of healthy control (HC) subjects in contrast to localization-related epilepsy (LRE) subjects were evaluated. Over 110 activation maps were generated, and their respective LIs were computed yielding the following groups: (a) strong right lateralization: (HC=0%, LRE=18%), (b) right lateralization: (HC=2%, LRE=10%), (c) bilateral: (HC=20%, LRE=15%), (d) left lateralization: (HC=42%, LRE=26%), e) strong left lateralization: (HC=36%, LRE=31%). Moreover, nonlinear-multidimensional decision functions were used to seek an optimal separation between typical and atypical brain activations on the basis of the demographics as well as the extent and intensity of these brain activations. The intent was not to seek the highest output measures given the inherent overlap of the data, but rather to assess which of the many dimensions were critical in the overall assessment of typical and atypical language activations with the freedom to select any number of dimensions and impose any degree of complexity in the nonlinearity of the decision space.

Data compilation on the biological response to ocean acidification: environmental and experimental context of data sets and related literature

The exponential growth of studies on the biological response to ocean acidification over the last few decades has generated a large amount of data. To facilitate data comparison, a data compilation hosted at the data publisher PANGAEA was initiated in 2008 and is updated on a regular basis (doi:10.1594/PANGAEA.149999). By January 2015, a total of 581 data sets (over 4 000 000 data points) from 539 papers had been archived. Here we present the developments of this data compilation five years since its first description by Nisumaa et al. (2010). Most of study sites from which data archived are still in the Northern Hemisphere and the number of archived data from studies from the Southern Hemisphere and polar oceans are still relatively low. Data from 60 studies that investigated the response of a mix of organisms or natural communities were all added after 2010, indicating a welcomed shift from the study of individual organisms to communities and ecosystems. The initial imbalance of considerably more data archived on calcification and primary production than on other processes has improved. There is also a clear tendency towards more data archived from multifactorial studies after 2010. For easier and more effective access to ocean acidification data, the ocean acidification community is strongly encouraged to contribute to the data archiving effort, and help develop standard vocabularies describing the variables and define best practices for archiving ocean acidification data.

Content-aware compression for big textual data analysis

A substantial amount of information on the Internet is present in the form of text. The value of this semi-structured and unstructured data has been widely acknowledged, with consequent scientific and commercial exploitation. The ever-increasing data production, however, pushes data analytic platforms to their limit. This thesis proposes techniques for more efficient textual big data analysis suitable for the Hadoop analytic platform. This research explores the direct processing of compressed textual data. The focus is on developing novel compression methods with a number of desirable properties to support text-based big data analysis in distributed environments. The novel contributions of this work include the following. Firstly, a Content-aware Partial Compression (CaPC) scheme is developed. CaPC makes a distinction between informational and functional content in which only the informational content is compressed. Thus, the compressed data is made transparent to existing software libraries which often rely on functional content to work. Secondly, a context-free bit-oriented compression scheme (Approximated Huffman Compression) based on the Huffman algorithm is developed. This uses a hybrid data structure that allows pattern searching in compressed data in linear time. Thirdly, several modern compression schemes have been extended so that the compressed data can be safely split with respect to logical data records in distributed file systems. Furthermore, an innovative two layer compression architecture is used, in which each compression layer is appropriate for the corresponding stage of data processing. Peripheral libraries are developed that seamlessly link the proposed compression schemes to existing analytic platforms and computational frameworks, and also make the use of the compressed data transparent to developers. The compression schemes have been evaluated for a number of standard MapReduce analysis tasks using a collection of real-world datasets. In comparison with existing solutions, they have shown substantial improvement in performance and significant reduction in system resource requirements.

Global distributions of diazotrophs abundance, biomass and nitrogen fixation rates - Gridded data product (NetCDF) - Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types

The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. This is a gridded data product about diazotrophic organisms . There are 6 variables. Each variable is gridded on a dimension of 360 (longitude) * 180 (latitude) * 33 (depth) * 12 (month). The first group of 3 variables are: (1) number of biomass observations, (2) biomass, and (3) special nifH-gene-based biomass. The second group of 3 variables is same as the first group except that it only grids non-zero data. We have constructed a database on diazotrophic organisms in the global pelagic upper ocean by compiling more than 11,000 direct field measurements including 3 sub-databases: (1) nitrogen fixation rates, (2) cyanobacterial diazotroph abundances from cell counts and (3) cyanobacterial diazotroph abundances from qPCR assays targeting nifH genes. Biomass conversion factors are estimated based on cell sizes to convert abundance data to diazotrophic biomass. Data are assigned to 3 groups including Trichodesmium, unicellular diazotrophic cyanobacteria (group A, B and C when applicable) and heterocystous cyanobacteria (Richelia and Calothrix). Total nitrogen fixation rates and diazotrophic biomass are calculated by summing the values from all the groups. Some of nitrogen fixation rates are whole seawater measurements and are used as total nitrogen fixation rates. Both volumetric and depth-integrated values were reported. Depth-integrated values are also calculated for those vertical profiles with values at 3 or more depths.

Records Management System: Pilot Projects Functional Report, 1998

The Iowa Department of Transportation began preparation for the acquisition of an electronic document management system in 1996. The first phase was development of a strategic plan. The plan provided guidelines for defining the acquisition and implementation of a document management system to automate document handling and distribution. Phase 2 involved developing draft standards (document, indexing and technology) for planning and implementation of a document management system. These standards were to identify existing industry standards and determine which standards would best support the specific requirements of the Iowa Department of Transportation. During development of these standards, the decision was made to enlarge the scope of this effort from a document management system to a records management system (RMS). Phase .3 identified business processes that were to be further developed as pilot projects of a much larger agency-wide records management system.

Legal requirements on secondary use of medical data in the EU and USA – A case study

The use of secondary data in health care research has become a very important issue over the past few years. Data from the treatment context are being used for evaluation of medical data for external quality assurance, as well as to answer medical questions in the form of registers and research databases. Additionally, the establishment of electronic clinical systems like data warehouses provides new opportunities for the secondary use of clinical data. Because health data is among the most sensitive information about an individual, the data must be safeguarded from disclosure.