Example carrier sequencing fastQ data set for CarrierSeq

<div>fastQ files with preserved headers containing channel information. The multiple files must first be combined into a single FastQ file prior to analysis (e.g., cat *.fastq > all_reads.fastq). Concatenated md5: 26ce52582bfcf24c41082ef44224107c</div><div><br></div><div>0.2 ng of B. subtilis DNA prepared with 1000 ng of Lambda DNA using the Oxford Nanopore Technologies ligation sequencing kit - LSK-SQK108.The library was then sequenced on a MinION Mark-1B sequencer and R9.4 flowcell for 48 hours and basecalled using ONTs Albacore v1.10 offline basecaller.</div

Low Input Sequencing FastQ data set

<div>FastQ files with preserved headers containing channel information. The multiple files must first be combined into a single FastQ file prior to analysis (e.g., cat *.fastq > all_reads.fastq). </div><div><br></div><div>Concatenated md5: 853cd6b8804deb54ddf5ace23bd65f6c</div><div><br></div><div>2 pg of <i>B. subtilis</i> DNA, quantified using ddPCR, prepared with 1000 ng of Lambda DNA using the Oxford Nanopore Technologies ligation sequencing kit - LSK-SQK108.</div><div><br></div><div>The library was then sequenced on a MinION Mark-1B sequencer and R9.4 flowcell for 48 hours and basecalled using ONTs MinKnow 1.5.18 live basecaller.<br></div><div><div><br></div></div

Improving Language-Dependent Named Entity Detection

Part 6: MAKE SemanticsInternational audienceNamed Entity Recognition (NER) and Named Entity Linking (NEL) are two research areas that have shown big advancements in recent years. The majority of this research is based on the English language. Hence, some of these improvements are language-dependent and do not necessarily lead to better results when applied to other languages. Therefore, this paper discusses TOMO, an approach to language-aware named entity detection and evaluates it for the German language. This also required the development of a German gold standard dataset, which was based on the English dataset used by the OKE 2016 challenge. An evaluation of the named entity detection task using the web-based platform GERBIL was undertaken and results show that our approach produced higher F1 values than the other annotators did. This indicates that language-dependent features do improve the overall quality of the spotter

A Novel Vision for Navigation and Enrichment in Cultural Heritage Collections

International audienceIn the cultural heritage domain, there is a huge interest in utilizing semantic web technology and build services enabling users to query, explore and access the vast body of cultural heritage information that has been created over decades by memory institutions. For successful conversion of existing data into semantic web data, however, there is often a need to enhance and enrich the legacy data to validate and align it with other resources and reveal its full potential. In this visionary paper, we describe a framework for semantic enrichment that relies on the creation of thematic knowledge bases, i.e., about a given topic. These knowledge bases aggregate information by exploiting structured resources (e.g., Linked Open Data cloud) and by extracting new relationships from streams (e.g., Twitter) and textual documents (e.g., web pages). Our focused application in this paper is how this approach can be utilized when transforming library records into semantic web data based on the FRBR model in the process that commonly is called FRBRization

Claim Detection in Judgments of the EU Court of Justice

Mining arguments from text has recently become a hot topic in Artificial Intelligence. The legal domain offers an ideal scenario to apply novel techniques coming from machine learning and natural language processing, addressing this challenging task. Following recent approaches to argumentation mining in juridical documents, this paper presents two distinct contributions. The first one is a novel annotated corpus for argumentation mining in the legal domain, together with a set of annotation guidelines. The second one is the empirical evaluation of a recent machine learning method for claim detection in judgments. The method, which is based on Tree Kernels, has been applied to context-independent claim detection in other genres such as Wikipedia articles and essays. Here we show that this method also provides a useful instrument in the legal domain, especially when used in combination with domain-specific information