BrainLiner: A Platform for Neurophysiological Data Sharing and Manipulation

This paper highlights the differences among the most common file formats used for storing digital radiological images. It promotes the proper use of these formats to guarantee easy manipulation in handling the most typical practical applications in daily radiological practice. The authors provide a simple yet exhaustive introduction to the concept of "file format" and describe the algorithms and main features of the most common formats (BMP, JPEG, GIF, DICOM, TIF, PNG) and Portable Network Graphics (PNG).The different formats are compared in terms of dimension, quality, portability and with reference to the following specific needs: electronic communications, publication on the World Wide Web, presentation of electronic posters, video presentations for teaching and manuscript publishing. We also illustrate how to handle the various formats with the programmes supplied with standard software installations.The large number of digital applications of image file formats calls for a simplification in daily radiological practice. We recommend the use of JPEG and PNG for electronic communications; PNG and GIF for publication on the worldwide web; JPEG and PNG for electronic poster presentations; DICOM, PNG and JPEG for teaching presentations; TIF and PNG for printing on paper.

BackgroundThe combination of mass spectrometry and solution phase amide hydrogen/deuterium exchange (H/D exchange) experiments is an effective method for characterizing protein dynamics, and protein-protein or protein-ligand interactions. Despite methodological advancements and improvements in instrumentation and automation, data analysis and display remains a tedious process. The factors that contribute to this bottleneck are the large number of data points produced in a typical experiment, each requiring manual curation and validation, and then calculation of the level of backbone amide exchange. Tools have become available that address some of these issues, but lack sufficient integration, functionality, and accessibility required to address the needs of the H/D exchange community. To date there is no software for the analysis of H/D exchange data that comprehensively addresses these issues.ResultsWe have developed an integrated software system for the automated analysis and representation of H/D exchange data that has been titled "The Deuterator". Novel approaches have been implemented that enable high throughput analysis, automated determination of deuterium incorporation, and deconvolution of overlapping peptides. This has been achieved by using methods involving iterative theoretical envelope fitting, and consideration of peak data within expected m/z ranges. Existing common file formats have been leveraged to allow compatibility with the output from the myriad of MS instrument platforms and peptide sequence database search engines.A web-based interface is used to integrate the components of The Deuterator that are able to analyze and present mass spectral data from instruments with varying resolving powers. The results, if necessary, can then be confirmed, adjusted, re-calculated and saved. Additional tools synchronize the curated calculation parameters with replicate time points, increasing throughput. Saved results can then be used to plot deuterium buildup curves and 3D structural overlays. The system has been used successfully in a production environment for over one year and is freely available as a web tool at the project home page .ConclusionThe automated calculation and presentation of H/D exchange data in a user interface enables scientists to organize and analyze data efficiently. Integration of the different components of The Deuterator coupled with the flexibility of common data file formats allow this system to be accessible to the broadening H/D exchange community.

In most cheminformatics workflows, chemical information is stored in files which provide the necessary data for subsequent calculations. The correct interpretation of the file formats is an important prerequisite to obtain meaningful results. Consistent reading of molecules from files, however, is not an easy task. Each file format implicitly represents an underlying chemical model, which has to be taken into consideration when the input data is processed. Additionally, many data sources contain invalid molecules. These have to be identified and either corrected or discarded. We present the chemical file format converter NAOMI, which provides efficient procedures for reliable handling of molecules from the common chemical file formats SDF, MOL2, and SMILES. These procedures are based on a consistent chemical model which has been designed for the appropriate representation of molecules relevant in the context of drug discovery. NAOMI's functionality is tested by round robin file IO exercises with public data sets, which we believe should become a standard test for every cheminformatics tool.

The Portable executable file format is the format of executables, object code and DLL’s (Dynamic Link Library) in Microsoft Windows Operating system. It is the standard of how executable files are organized within file system in Microsoft Windows. The Portable executable file format was designed for Windows NT 3.1 that released in 1993. Many of its features are inherited from COFF (Common object file format) used in Unix Operating systems. It consists of header and sections; headers are rules that tell windows loader how the section should be mapped and loaded into memory. The section are nothing but the data or content. Different sections hold different kind of data for example code section contains executable code while bss (Block Started by Symbol) sections contains uninitialized data. Portable executable file could be used in a way to dent the security of computer, therefore great care should be taken while downloading and running Portable executable files. In our work we develop a static malware analysis tool called ‘Portable Executable File Analysis Framework (PEFAF)’ using data mining techniques. A collection of 8 thousand benign and 7 thousand malicious files were used in this work. We extracted 60 features, analyzed them and found that 34 of them are significant for the detection of malware threats. Based on these 34 indicators our tool classifies input file into malicious or non-malicious.

Activities of the nuclear physics group are described. Progress was made in organizing the Gammasphere Software Working Group, establishing a nuclear computing facility, participating in software development at Lawrence Berkeley, developing a common data file format, and adapting the ORNL UPAK software to run at Gammasphere. A universal histogram object was developed that defines a file format and provides for an objective-oriented programming model. An automated liquid nitrogen fill system was developed for Gammasphere (110 Ge detectors comprise the sphere).

Genes expressing resistance to each antimicrobial agent emerged after each agent became widely used. More than a hundred such genes now spread selectively through global networks of populations of bacteria in humans or animals treated with those agents. Information to monitor and manage this spread exists in the susceptibility test results of tens of thousands of laboratories around the world. The comparability of those results is uncertain, however, and their storage in paper files or in computer files with diverse codes and formats has made them inaccessible for analysis. The WHONET program puts each laboratory's data into a common code and file format at that laboratory, either by serving as or by translating from its own computer reporting system. It then enables each medical center to analyze its files in ways that help it monitor and manage resistance locally and to merge them with files of other centers for collaborative national or global surveillance of resistance.

User-Generated Captions: From Hackers, to the Disability Digerati, to Fansubbers

The Digital Earth Viewer is a tool for the visualisation and exploration of geospatial data in true 3D over time. It runs on Windows, MacOS and Linux and only requires a modern webbrowser to use. Common file formats such as CSV, netCDF, GeoTIFF and many more are natively supported

This paper presents a project for simple web-based system identification from experimental data. A user can upload input/output data from a process to be identified in three common file formats, the data can be filtered simply using two approaches and a structure of the identified model is user-controlled. The resultant discrete-time ARX model is obtained in a common form together with the possibility to assess quality of identification and to obtain also a continuous-time model. The developed application is built on the interconnection of the latest web technologies with the MATLAB computing system where the obtained results can be easily exported. The paper explains motivation for development of this site and gives also detailed description of the whole process including the Web—MATLAB interconnection. The results are presented using selected screen-shots of the application and discussed further.

CORLI is a consortium of Huma-Num, the French national infrastructure dedicated to the technical support and promotion of digital humanities. The goal of CORLI is to promote and provide tools and information for good and efficient research practices in corpus linguistics, especially on spoken language corpora. Because of the time required to collect and transcribe spoken language resources, their number is limited and thus corpora need to be interoperable and reusable in order to improve research on themes such as phonology, prosody, interaction, syntax, and textometry. To help researchers reach this goal, CORLI has designed a pair of tools: TEICORPO to assist in the conversion and use of spoken language corpora, and TEIMETA for metadata purposes. TEICORPO is based on the principle of an underlying common format, namely TEI XML as described in its specification for spoken language use (ISO 2016). This tool enables the conversion of transcriptions created with alignment software such as CLAN, Transcriber, Praat, or ELAN as well as common file formats (CSV, XLSX, TXT, or DOCX) and the TEI format, which plays the role of a lossless pivot format. Backward conversion is possible in many cases, with limitations inherent in the destination target format. TEICORPO can run the Treetagger part-of-speech tagger and the Stanford CoreNLP tools on TEI files and can export the resulting files to textometric tools such as TXM, Le Trameur, or Iramuteq, making it suitable for spoken language corpora editing as well as for various research purposes.

Ecological data are collected and shared at an increasingly rapid pace, but it is often shared in inconsistent and untraceable processed forms. Images of wood contain a wealth of information such as colours and textures but are most commonly reduced to ring‐width measurements before they can be shared in various common file formats. Archiving digital images of wood samples in libraries, which have been developed for ecological analysis and are publicly available, remains the exception. We developed the Wood Image Analysis and Dataset (WIAD), an open‐source application including a web interface to integrate basic visual analysis of wood samples, such as increment cores, thin sections or X‐ray films, basic data processing, and archiving of the images and derived data to facilitate transparency and reproducibility in studies using visual characteristics of wood. WIAD provides user‐friendly tools to manipulate images of wood samples, mark and measure wood characteristics such as growth increments, density fluctuations, early‐ and latewood widths and fire scars, and to visualise, process and archive images, metadata, and the derived data. WIAD constitutes a step towards the reproducible automation of tree‐ring analysis while establishing an open‐source foundation to create improved community‐developed repositories which would enable novel ecological studies harnessing the wealth of existing visual data.

Urban areas across Europe are facing unprecedented challenges from climate change, further intensified by the emergence of urban heat islands (UHIs). The resulting elevated temperatures within cities have profound implications for public health, energy consumption, and overall urban liveability. Climate adaptation and evidence-based urban planning that are needed to address these pressing issues require a better understanding of urban temperature dynamics. To address this pressing issue, our research aims to bridge a critical gap in our understanding of urban temperature dynamics through a comprehensive space-time analysis focused on 9 European cities. The primary emphasis at this stage is placed on the foundational step: collection, enhancement, and intercomparison of local temperature information. This initiative, which is part of the European COST-Action FAIRNESS (https://www.fairness-ca20108.eu/), is driven by the imperative need for accurate and localized data to inform evidence-based urban planning and climate adaptation strategies, highlighting the urgency and significance of our research.   We collected measurements from nine European cities, namely, Amsterdam, Basel, Bern, Biel, Turku, Rennes, Novi Sad, Birmingham, and Zurich The initial phase of the work involved the collection of raw data from different networks, encompassing varying time periods. The datasets exhibited considerable diversity in formats and temporal resolutions, necessitating meticulous handling. In parallel, metadata relevant to each dataset was collected. The primary step was to standardize all data into a common file format, with the Station Exchange Format (SEF) being the chosen standard. During this formatting process, a version with harmonized time resolution was generated, ensuring coherence across the datasets. Subsequently, a series of automatic quality control procedures were developed to systematically enhance the reliability and precision of the datasets. These procedures were designed to be universally applicable to all stations, promoting consistency in the assessment of temperature data. Additionally, for certain networks, a radiation correction was implemented to further refine the accuracy of the collected information.   Looking ahead, the datasets will be published, offering accessibility to urban planners and other stakeholders. The outcomes of this preliminary phase not only contribute to advancing space-time analysis in temperature assessment but also establish a robust foundation for subsequent research stages. The significance of this work resonates with urban planners, environmental scientists, and policymakers actively involved in crafting localized strategies for climate-resilient urban planning in European cities.

Web portal services have become an important medium to deliver digital content (e.g. news, advertisements, etc.) to Web users in a timely fashion. To attract more users to various content modules on the Web portal, it is necessary to design a recommender system that can effectively achieve Web portal content optimization by automatically estimating content item attractiveness and relevance to user interests. The state-of-the-art online learning methodology adapts dedicated pointwise models to independently estimate the attractiveness score for each candidate content item. Although such pointwise models can be easily adapted for online recommendation, there still remain a few critical problems. First, this pointwise methodology fails to use invaluable user preferences between content items. Moreover, the performance of pointwise models decreases drastically when facing the problem of sparse learning samples. To address these problems, we propose exploring a new dynamic pairwise learning methodology for Web portal content optimization in which we exploit dynamic user preferences extracted based on users' actions on portal services to compute the attractiveness scores of content items. In this article, we introduce two specific pairwise learning algorithms, a straightforward graph-based algorithm and a formalized Bayesian modeling one. Experiments on large-scale data from a commercial Web portal demonstrate the significant improvement of pairwise methodologies over the baseline pointwise models. Further analysis illustrates that our new pairwise learning approaches can benefit personalized recommendation more than pointwise models, since the data sparsity is more critical for personalized content optimization.

Systematic review of free and open source software (FOSS) employed in ecomorphological studies with recommendations for user-friendly developments

Identifying the source of a video recording created by a camera or smartphone has been a common and challenging task in media forensics for many years. We present an approach for source identification on the very common MP4 file format. In extension to related works, we propose to consider the suitability of attribute field values and their respective order in the atom/box tree in a specific manner. The significance of a field attribute and its particular value for source identification will be reflected by means of up and down weighting during the training and the matching process. Experimental result indicate that our approach allows distinguishing major brands. Even device identification is possible for a subset of our training data.