Showing papers by "German Criminal Police Office published in 2022"

Development and inter-laboratory validation of the VISAGE enhanced tool for age estimation from semen using quantitative DNA methylation analysis

Abstract: The analysis of DNA methylation has become an established method for chronological age estimation. This has triggered interest in the forensic community to develop new methods for age estimation from biological crime scene material. Various assays are available for age estimation from somatic tissues, the majority from blood. Age prediction from semen requires different DNA methylation markers and the only assays currently developed for forensic analysis are based on SNaPshot or pyrosequencing. Here, we describe a new assay using massively parallel sequencing to analyse 13 candidate CpG sites targeted in two multiplex PCRs. The assay has been validated by five consortium laboratories of the VISible Attributes through GEnomics (VISAGE) project within a collaborative exercise and was tested for reproducible quantification of DNA methylation levels and sensitivity with DNA methylation controls. Furthermore, DNA extracts and stains on Whatman FTA cards from two semen samples were used to evaluate concordance and mimic casework samples. Overall, the assay yielded high read depths (> 1000 reads) at all 13 marker positions. The methylation values obtained indicated robust quantification with an average standard deviation of 2.8% at the expected methylation level of 50% across the 13 markers and a good performance with 50 ng DNA input into bisulfite conversion. The absolute difference of quantifications from one participating laboratory to the mean quantifications of concordance and semen stains of remaining laboratories was approximately 1%. These results demonstrated the assay to be robust and suitable for age estimation from semen in forensic investigations. In addition to the 13-marker assay, a more streamlined protocol combining only five age markers in one multiplex PCR was developed. Preliminary results showed no substantial differences in DNA methylation quantification between the two assays, indicating its applicability with the VISAGE age model for semen developed with data from the complete 13-marker tool.

Freva, a software framework for the Earth System community. Overview and and new features.

Abstract: <p>The complexity of the climate system calls for a combined approach of different knowledge areas. For that, increasingly larger projects need a coordinate effort that fosters an active collaboration between members. On the other hand, although the continuous improvement of numerical models and larger observational data availability provides researchers with a growing amount of data to analyze, the need for greater resources to host, access, and evaluate them efficiently through High Performance Computing (HPC) infrastructures is growing more than ever. Finally, the thriving emphasis on FAIR data principles [1] and the easy reproducibility of evaluation workflows also requires a framework that facilitates these tasks. Freva (Free Evaluation System Framework [2, 3]) is an efficient solution to handle customizable evaluation systems of large research projects, institutes or universities in the Earth system community [4-6] over the HPC environment and in a centralized manner.</p><p> </p><p>Freva is a scientific software infrastructure for standardized data and analysis tools (plugins) that provides all its available features both in a shell and web environment. Written in python, is equipped with a standardized model database, an application-programming interface (API) and a history of evaluations, among others:</p><ul><li>An implemented metadata system in SOLR with its own search tool allows scientists and their plugins to retrieve the required information from a centralized database. The databrowser interface satisfies the international standards provided by the Earth System Grid Federation (ESGF, e.g. [7]).</li> <li>An API allows scientific developers to connect their plugins with the evaluation system independently of the programming language. The connected plugins are able to access from and integrate their results back to the database, allowing for a concatenation of plugins as well. This ecosystem increases the number of scientists involved in the studies, boosting the interchange of results and ideas. It also fosters an active collaboration between plugin developers.</li> <li>The history and configuration sub-system stores every analysis performed with Freva in a MySQL database. Analysis configurations and results can be searched and shared among the scientists, offering transparency and reproducibility, and saving CPU hours, I/O, disk space and time.</li> </ul><p>Freva efficiently frames the interaction between different technologies thus improving the Earth system modeling science.</p><p> </p><p>This framework has undergone major refactoring and restructuring of the core that will also be discussed. Among others:</p><ul><li>Major core Python update (2.7 to 3.9).</li> <li>Easier deployment and containerization of the framework via Docker.</li> <li>More secure system configuration via Vault integration.</li> <li>Direct Freva function calls via python client (e.g. for jupyter notebooks).</li> <li>Improvements in the dataset incorporation.</li> </ul><p><strong> </strong></p><p><strong>References:</strong></p><p>[1] https://www.go-fair.org/fair-principles/</p><p>[2] Kadow, C. et al. , 2021. Introduction to Freva – A Free Evaluation System Framework for Earth System Modeling. <em>JORS</em>. http://doi.org/10.5334/jors.253</p><p>[3] gitlab.dkrz.de/freva</p><p>[4] freva.met.fu-berlin.de</p><p>[5] https://www.xces.dkrz.de/</p><p>[6] www-regiklim.dkrz.de</p><p>[7] https://esgf-data.dkrz.de/projects/esgf-dkrz/</p>

Why we need to highlight FAIR and Open Data and how to do it with EASYDAB

Abstract: <p>The FAIR<sup>1</sup> data principles are important for the findability, accessibility, interoperability, and reusability of data. Therefore, many repositories make huge efforts to curate data so that they become FAIR and assign DataCite<sup>2</sup> DOIs to archived data for increasing the findability. Nevertheless, recent investigations (Strecker<sup>3</sup>, 2021) show that many datasets published with a DataCite DOI don’t meet all aspects of the FAIR principles, as they are missing important information for the reuse and interoperability in their metadata. Further examinations of data from the Earth System Sciences (ESS) reveal that especially automatic processability is suboptimal, e.g. because of missing persistent identifiers for creators and affiliations, missing information about geolocations or time ranges of simulations. As well, many datasets either don’t have any licence information or a non-open licence.</p><p>The question arises of how datasets with open licences<sup>4</sup> and high-quality metadata can be highlighted so that they stand out from the crowd of published data. One solution is the newly developed branding for FAIR and open ESS data, called EASYDAB<sup>5</sup> (Earth System Data Branding).  It consists of a logo that earmarks landing pages of those datasets with a DataCite DOI, an open licence, open file formats<sup>6</sup>, rich metadata, and which were quality controlled by the responsible repository. The EASYDAB logo is protected and may only be used by repositories that agree to follow the EASYDAB Guidelines<sup>7</sup>. These guidelines define principles on how to achieve high metadata quality of ESS datasets by demanding specific metadata information. Domain-specific quality guidelines define the mandatory metadata for a self-explaining description of the data. One example is a quality guideline for atmospheric model data - the ATMODAT Standard<sup>8</sup>. It prescribes not only the metadata for the files but also for the DOI and the landing page. The atmodat data checker<sup>9</sup> additionally helps data providers and repositories to check whether data files meet the requirements of the ATMODAT Standard.</p><p>The use of the EASYDAB logo is free of charge, but repositories must sign a contract with TIB – Leibniz Information Centre for Science and Technology<sup>10</sup>. TIB will control in the future that datasets with landing pages highlighted with EASYDAB indeed follow the EASYDAB Guidelines to ensure that EASYDAB remains a branding for high-quality data. </p><p>Using EASYDAB, repositories can indicate their efforts to publish data with high-quality metadata. The EASYDAB logo also indicates to data users that the dataset is quality controlled and can be easily reused.</p><p> </p><p>1: https://www.go-fair.org/fair-principles/</p><p>2: https://datacite.org/</p><p>3: https://edoc.hu-berlin.de/bitstream/handle/18452/23590/BHR470_Strecker.pdf?sequence=1</p><p>4: https://opendefinition.org/licenses/</p><p>5: https://www.easydab.de</p><p>6: http://opendatahandbook.org/guide/en/appendices/file-formats/</p><p>7: https://cera-www.dkrz.de/WDCC/ui/cerasearch/entry?acronym=EASYDAB_Guideline_v1.1</p><p>8: https://doi.org/10.35095/WDCC/atmodat_standard_en_v3_0</p><p>9: https://github.com/AtMoDat/atmodat_data_checker</p><p>10: https://www.tib.eu/en/</p>

Data amounts and reproducibility: How FAIR Digital Objects can revolutionise Research Workflows 

Abstract: <p>Some disciplines, especially those that look at the Earth system, work with large to very large amounts of data. Storing this data, but also processing it, places completely new demands on scientific work itself.</p><p>Let's take the example of climate research and specifically climate modelling. In addition to long-term meteorological measurements in the recent past, results from climate models form the main basis for research and statements on past and possible future global, regional and local climate. Climate models are very complex numerical models that require high-performance computing. However, with the current and future increasing spatial and temporal resolution of the models, the demand for computing resources and storage space is also increasing. Previous working methods and processes no longer hold up and need to be rethought.</p><p>Taking the German Climate Computing Centre (DKRZ) as an example, we analysed the users, their goals and working methods. DKRZ provides the climate science community with resources such as high-performance computing (HPC), data storage and specialised services and hosts the World Data Center for Climate (WDCC). In analysing users, we distinguish between two groups: those who need the HPC system to run resource-intensive simulations and then analyse them, and those who reuse, build on and analyse existing data. Each group subdivides into subgroups. We have analysed the workflows for each identified user and found identical parts in an abstracted form and derived Canonical Workflow Modules.</p><p>In the process, we critically examined the possible use of so-called FAIR Digital Objects (FDOs) and checked to what extent the derived workflows and workflow modules are actually future-proof.</p><p>The vision is that the global integrated data space is formed by standardised, independent and persistent entities that contain all information about diverse data objects (data, documents, metadata, software, etc.) so that human and, above all, machine agents can find, access, interpret and reuse (FAIR) them in an efficient and cost-saving way. At the same time, these units become independent of technologies and heterogeneous organisation of data, and will contain a built-in mechanism that supports data sovereignty. This will make the handling of data sustainable and secure.</p><p>So, each step in a research workflow can be a FDO. In this case, the research is fully reproducible, but parts can also be exchanged and, e.g. experiments can be varied transparently. FDOs can easily be linked to others. The redundancy of data is minimised and thus also the susceptibility to errors is reduced. FDOs open up the possibility of combining data, software or whole parts of workflows in a new and simple but at all times comprehensible way. FDOs will make an important contribution to the reproducibility of research results, but they are also crucial for saving storage space. There are already data that are FDOs, but also self-contained frameworks that store data via tracking workflows. Similar to the TCP/IP standard, DO interface protocols are already developed. However, there are still some open points that are currently being worked on and defined with regard to FDOs in order to make them a globally functioning system.</p>