Metadata und statistic analysis of archael and bacterial sequences originating from sediments of the Håkon Mosby mud volcano (Z1-Z3, all habitats)

DNA extraction was carried out as described on the MICROBIS project pages (http://icomm.mbl.edu/microbis ) using a commercially available extraction kit. We amplified the hypervariable regions V4-V6 of archaeal and bacterial 16S rRNA genes using PCR and several sets of forward and reverse primers (http://vamps.mbl.edu/resources/primers.php). Massively parallel tag sequencing of the PCR products was carried out on a 454 Life Sciences GS FLX sequencer at Marine Biological Laboratory, Woods Hole, MA, following the same experimental conditions for all samples. Sequence reads were submitted to a rigorous quality control procedure based on mothur v30 (doi:10.1128/AEM.01541-09) including denoising of the flow grams using an algorithm based on PyroNoise (doi:10.1038/nmeth.1361), removal of PCR errors and a chimera check using uchime (doi:10.1093/bioinformatics/btr381). The reads were taxonomically assigned according to the SILVA taxonomy (SSURef v119, 07-2014; doi:10.1093/nar/gks1219) implemented in mothur and clustered at 98% ribosomal RNA gene V4-V6 sequence identity. V4-V6 amplicon sequence abundance tables were standardized to account for unequal sampling effort using 1000 (Archaea) and 2300 (Bacteria) randomly chosen sequences without replacement using mothur and then used to calculate inverse Simpson diversity indices and Chao1 richness (doi:10.2307/4615964). Bray-Curtis dissimilarities (doi:10.2307/1942268) between all samples were calculated and used for 2-dimensional non metric multidimensional scaling (NMDS) ordinations with 20 random starts (doi:10.1007/BF02289694). Stress values below 0.2 indicated that the multidimensional dataset was well represented by the 2D ordination. NMDS ordinations were compared and tested using Procrustes correlation analysis (doi:10.1007/BF02291478). All analyses were carried out with the R statistical environment and the packages vegan (available at: http://cran.r-project.org/package=vegan), labdsv (available at: http://cran.r-project.org/package=labdsv), as well as with custom R scripts. Operational taxonomic units at 98% sequence identity (OTU0.03) that occurred only once in the whole dataset were termed absolute single sequence OTUs (SSOabs; doi:10.1038/ismej.2011.132). OTU0.03 sequences that occurred only once in at least one sample, but may occur more often in other samples were termed relative single sequence OTUs (SSOrel). SSOrel are particularly interesting for community ecology, since they comprise rare organisms that might become abundant when conditions change.16S rRNA amplicons and metagenomic reads have been stored in the sequence read archive under SRA project accession number SRP042162.

Development of a tool for designing tests based on statistics and metadata

Background: This project’s idea arose derived of the need of the professors of the department “Computer Languages and Systems and Software Engineering (DLSIIS)” to develop exams with multiple choice questions in a more productive and comfortable way than the one they are currently using. The goal of this project is to develop an application that can be easily used by the professors of the DLSIIS when they need to create a new exam. The main problems of the previous creation process were the difficulty in searching for a question that meets some specific conditions in the previous exam files; and the difficulty for editing exams because of the format of the employed text files. Result: The results shown in this document allow the reader to understand how the final application works and how it addresses successfully every customer need. The elements that will help the reader to understand the application are the structure of the application, the design of the different components, diagrams that show the workflow of the application and some selected fragments of code. Conclusions: The goals stated in the application requirements are finally met. In addition, there are some thoughts about the work performed during the development of the application and how it improved the author skills in web development.

The Political and Judicial Life of Metadata: Digital Rights Ireland and the Trail of the Data Retention Directive. CEPS Paper in Liberty and Security No. 65, May 2014

This paper examines the challenges facing the EU regarding data retention, particularly in the aftermath of the judgment Digital Rights Ireland by the Court of Justice of the European Union (CJEU) of April 2014, which found the Data Retention Directive 2002/58 to be invalid. It first offers a brief historical account of the Data Retention Directive and then moves to a detailed assessment of what the judgment means for determining the lawfulness of data retention from the perspective of the EU Charter of Fundamental Rights: what is wrong with the Data Retention Directive and how would it need to be changed to comply with the right to respect for privacy? The paper also looks at the responses to the judgment from the European institutions and elsewhere, and presents a set of policy suggestions to the European institutions on the way forward. It is argued here that one of the main issues underlying the Digital Rights Ireland judgment has been the role of fundamental rights in the EU legal order, and in particular the extent to which the retention of metadata for law enforcement purposes is consistent with EU citizens’ right to respect for privacy and to data protection. The paper offers three main recommendations to EU policy-makers: first, to give priority to a full and independent evaluation of the value of the data retention directive; second, to assess the judgment’s implications for other large EU information systems and proposals that provide for the mass collection of metadata from innocent persons, in the EU; and third, to adopt without delay the proposal for Directive COM(2012)10 dealing with data protection in the fields of police and judicial cooperation in criminal matters.

List of size fractionated eukaryotic plankton community samples and associated metadata (Database W1)

The present data set provides an Excel file in a zip archive. The file lists 334 samples of size fractionated eukaryotic plankton community with a suite of associated metadata (Database W1). Note that if most samples represented the piconano- (0.8-5 µm, 73 samples), nano- (5-20 µm, 74 samples), micro- (20-180 µm, 70 samples), and meso- (180-2000 µm, 76 samples) planktonic size fractions, some represented different organismal size-fractions: 0.2-3 µm (1 sample), 0.8-20 µm (6 samples), 0.8 µm - infinity (33 samples), and 3-20 µm (1 sample). The table contains the following fields: a unique sample sequence identifier; the sampling station identifier; the Tara Oceans sample identifier (TARA_xxxxxxxxxx); an INDSC accession number allowing to retrieve raw sequence data for the major nucleotide databases (short read archives at EBI, NCBI or DDBJ); the depth of sampling (Subsurface - SUR or Deep Chlorophyll Maximum - DCM); the targeted size range; the sequences template (either DNA or WGA/DNA if DNA extracted from the filters was Whole Genome Amplified); the latitude of the sampling event (decimal degrees); the longitude of the sampling event (decimal degrees); the time and date of the sampling event; the device used to collect the sample; the logsheet event corresponding to the sampling event ; the volume of water sampled (liters). Then follows information on the cleaning bioinformatics pipeline shown on Figure W2 of the supplementary litterature publication: the number of merged pairs present in the raw sequence file; the number of those sequences matching both primers; the number of sequences after quality-check filtering; the number of sequences after chimera removal; and finally the number of sequences after selecting only barcodes present in at least three copies in total and in at least two samples. Finally, are given for each sequence sample: the number of distinct sequences (metabarcodes); the number of OTUs; the average number of barcode per OTU; the Shannon diversity index based on barcodes for each sample (URL of W4 dataset in PANGAEA); and the Shannon diversity index based on each OTU (URL of W5 dataset in PANGAEA).

Metadata and Geospatial Data Processing on the Base of XML and Grid

The software architecture and development consideration for open metadata extraction and processing framework are outlined. Special attention is paid to the aspects of reliability and fault tolerance. Grid infrastructure is shown as useful backend for general-purpose task.

Automatized Generation of Metadata and Calibrated Test Item Banks for E-learning

Report published in the Proceedings of the National Conference on "Education in the Information Society", Plovdiv, May, 2013

Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, specially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.

The RAGE Software Asset Model and Metadata Model

Software assets are key output of the RAGE project and they can be used by applied game developers to enhance the pedagogical and educational value of their games. These software assets cover a broad spectrum of functionalities – from player analytics including emotion detection to intelligent adaptation and social gamification. In order to facilitate integration and interoperability, all of these assets adhere to a common model, which describes their properties through a set of metadata. In this paper the RAGE asset model and asset metadata model is presented, capturing the detail of assets and their potential usage within three distinct dimensions – technological, gaming and pedagogical. The paper highlights key issues and challenges in constructing the RAGE asset and asset metadata model and details the process and design of a flexible metadata editor that facilitates both adaptation and improvement of the asset metadata model.

Collection-Level Subject Access in Aggregations of Digital Collections: Metadata Application and Use

Problems in subject access to information organization systems have been under investigation for a long time. Focusing on item-level information discovery and access, researchers have identified a range of subject access problems, including quality and application of metadata, as well as the complexity of user knowledge required for successful subject exploration. While aggregations of digital collections built in the United States and abroad generate collection-level metadata of various levels of granularity and richness, no research has yet focused on the role of collection-level metadata in user interaction with these aggregations. This dissertation research sought to bridge this gap by answering the question “How does collection-level metadata mediate scholarly subject access to aggregated digital collections?” This goal was achieved using three research methods: • in-depth comparative content analysis of collection-level metadata in three large-scale aggregations of cultural heritage digital collections: Opening History, American Memory, and The European Library • transaction log analysis of user interactions, with Opening History, and • interview and observation data on academic historians interacting with two aggregations: Opening History and American Memory. It was found that subject-based resource discovery is significantly influenced by collection-level metadata richness. The richness includes such components as: 1) describing collection’s subject matter with mutually-complementary values in different metadata fields, and 2) a variety of collection properties/characteristics encoded in the free-text Description field, including types and genres of objects in a digital collection, as well as topical, geographic and temporal coverage are the most consistently represented collection characteristics in free-text Description fields. Analysis of user interactions with aggregations of digital collections yields a number of interesting findings. Item-level user interactions were found to occur more often than collection-level interactions. Collection browse is initiated more often than search, while subject browse (topical and geographic) is used most often. Majority of collection search queries fall within FRBR Group 3 categories: object, concept, and place. Significantly more object, concept, and corporate body searches and less individual person, event and class of persons searches were observed in collection searches than in item searches. While collection search is most often satisfied by Description and/or Subjects collection metadata fields, it would not retrieve a significant proportion of collection records without controlled-vocabulary subject metadata (Temporal Coverage, Geographic Coverage, Subjects, and Objects), and free-text metadata (the Description field). Observation data shows that collection metadata records in Opening History and American Memory aggregations are often viewed. Transaction log data show a high level of engagement with collection metadata records in Opening History, with the total page views for collections more than 4 times greater than item page views. Scholars observed viewing collection records valued descriptive information on provenance, collection size, types of objects, subjects, geographic coverage, and temporal coverage information. They also considered the structured display of collection metadata in Opening History more useful than the alternative approach taken by other aggregations, such as American Memory, which displays only the free-text Description field to the end-user. The results extend the understanding of the value of collection-level subject metadata, particularly free-text metadata, for the scholarly users of aggregations of digital collections. The analysis of the collection metadata created by three large-scale aggregations provides a better understanding of collection-level metadata application patterns and suggests best practices. This dissertation is also the first empirical research contribution to test the FRBR model as a conceptual and analytic framework for studying collection-level subject access.