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Readme files are simple documentation files that can be included with data packages and are a way to convey information to yourself and others about your data packages. Documenting your data packages can help you remember where you have placed data, and how you structured data, which can be helpful when revisiting data after a vacation or a few years. In this workshop, we'll show where to find the UU Geosciences faculty README template, how to edit it, and what information to put in README files.In this workshop, we aimed to reach the following learning outcome: Where to save the README file How to create a README file What information to include in a README Learn where to download the UU Geosciences README template How to edit the UU Geosciences README template Why it is important at different research phases (with examples) Introduce different components of the README file Provide them tips and tricks to customize their README files In the presentation, we provided general information about README, itscontents, and in which file format it should be created. It was shown how to use Markdown (.md) file format to write stylish README using some examples given in the MarkDown_Writing_tips file. We use an online Markdown editor to show this example. In the second part of the workshop, we showed where the participants can find the README templates of the Faculty of Geoscience and usethem for their purposes. In the third part of the workshop, the participants were divided into three groups.Each group received real a READMEto review and identify what are the missing partsand give suggestions to improve each of them. Each group had up to 5 minutes to explain what they found in README. The READMEs that were used in this part are included in the README.zip file.

English: Coffee Lecture Slides: Description of Research Data – The creation of codebooks and README files (Coffee Lecture 25.01.2023) This Coffee Lecture will describe how you can improve the documentation of your datasets for publication by correctly describing the attributes of tabular data with a codebook and by providing any general datasets with a readme file. In addition to the origin of the data, this also includes a description of the processing and which usage rights are defined for the data set. Templates are also shared for both types of documentation. Video: Link ------------------------------------------------------ Deutsch: Coffee Lecture Folien: Forschungsdatenbeschreibung - Das Anlegen von Codebooks und Readme-Dateien (Coffee Lecture 25.01.2023) In dieser Coffee Lecture soll beschrieben werden, wie man die Dokumentation seiner Datensätze zur Veröffentlichung verbessern kann, indem man zum einen mit einem Codebook die Attribute von tabellarischen Daten richtig beschreibt, als auch generelle beliebige Datensätze mit einer Readme-Datei versehen kann. Diese umfasst neben der Herkunft der Daten auch eine Beschreibung der Verarbeitung und welche Nutzungsrechte für den Datensatz festgelegt sind. Für beide Arten der Dokumentation werden auch Templates geteilt. Video: Link

Short presentation about file naming, project structure and README's for the EUR Data Coffee Breaks at Erasmus University Rotterdam. Presented January 19 & 30, 2023 (previous version presented March 14 & 30, 2022 at the ESSB Data Coffee Breaks at the Erasmus School for Social and Behavioural Sciences)

This is the replication data associated with our article in "Journal of Ecology" titled "Multi-year drought alters plant species composition more than productivity across northern temperate grasslands". Please see the ReadMe file for details on dataset contents.

Data consists of: README, qualitative data files in nvpx and qdpx formats, codebook and zipped file of codes.

How to use Economics Syllabi

— Readme file is a short document associated with most open source software (OSS), it provides useful information about the software. Software developers use different structure when documenting their software, accordingly there were many attempts to uniform the structure of readme file as this file is the main file most user read and help the user to decide upon using the software or not .In this paper we suggest using audio format for readme file (ListenToMe) to do so we used 100 GitHub repositories as a dataset. Extracting commits from it results in 326793 messages. Next, commits regarding documentation have been isolated for further analysis. Finally, we observe the structure of readme file and the number of key attributes used. The result shows that Github developers did not committee to the general Readme file structure suggested by literature[17]. Most developers will not save time to write documentation, thus we suggest an audio version of Readme file. Also structures for the ListentoMe file have been proposed to bridge the gap in documentation structures. To evaluate our suggestion, we conduct aquestionnaire targeting software developers in GitHub for feedbacks. The questionnaire results shows that more than 70% of developers prefer ListentoMe (audio version of ReadMe) , they mostly agreed about the suggested ListentoMe structure.

Continuous Integration (CI) frameworks such as Travis CI, automatically build and run tests whenever a new commit is submitted/pushed. Although there are many advantages in using CI, e.g., speeding up the release cycle and automating the test execution process, it has been noted that the CI process can take a very long time to complete. One of the possible reasons for such delays is the fact that some commits (e.g., changes to readme files) unnecessarily kick off the CI process. Therefore, the goal of this paper is to automate the process of determining which commits can be CI skipped. We start by examining the commits of 58 Java projects and identify commits that were explicitly CI skipped by developers. Based on the manual investigation of 1,813 explicitly CI skipped commits, we first devise an initial model of a CI skipped commit and use this model to propose a rule-based technique that automatically identifies commits that should be CI skipped. To evaluate the rule-based technique, we perform a study on unseen datasets extracted from ten projects and show that the devised rule-based technique is able to detect and label CI skip commits, achieving Areas Under the Curve (AUC) values between 0.56 and 0.98 (average of 0.73). Additionally, we show that, on average, our technique can reduce the number of commits that need to trigger the CI process by 18.16 percent. We also qualitatively triangulated our analysis on the importance of skipping the CI process through a survey with 40 developers. The survey results showed that 75 percent of the surveyed developers consider it to be nice, important or very important to have a technique that automatically flags CI skip commits. To operationalize our technique, we develop a publicly available prototype tool, called CI-Skipper , that can be integrated with any git repository and automatically mark commits that can be CI skipped.

Objectives Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F’s genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014–2017. Data description Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.

Este fichero proporciona información sobre el software para que sea correctamente interpretado por personas y máquinas.

This companion paper is provided to describe the major experiments reported in our paper "On Learning Disentangled Representation for Acoustic Event Detection" published in ACM Multimedia 2019. To make the replication of our work easier, we first give an introduction of the computing environment where all of our experiments are conducted. Furthermore, we provide an environmental configuration file to setup the compiling environment and other artifacts including the source code, datasets and the files generated during our experiments. Finally, we summarize the structure and usage of the source code. For more details, please consult the README file in the archive of artifacts on GitHub: https://github.com/mastergofujs/SED_PyTorch.

Data supporting "β-Hydroxybutyrate Accumulates in the Rat Heart during Low-Flow Ischaemia with Implications for Functional Recovery". See README file for description.

We report the aperiodic titanate Ba10Y6Ti4O27 with a room‐temperature thermal conductivity that equals the lowest reported for an oxide. The structure is characterised by discontinuous occupancy modulation of each of the sites and can be considered as a quasicrystal. The resulting localisation of lattice vibrations suppresses phonon transport of heat. This new lead material for low‐thermal‐conductivity oxides is metastable and located within a quaternary phase field that has been previously explored. Its isolation thus requires a precisely defined synthetic protocol. The necessary narrowing of the search space for experimental investigation was achieved by evaluation of titanate crystal chemistry, prediction of unexplored structural motifs that would favour synthetically accessible new compositions, and assessment of their properties with machine‐learning models., Ba10Y6Ti4O27 has a complex aperiodic structure with the lowest thermal conductivity of any first‐transition‐series oxide. It is metastable, which makes it challenging to identify. Experimental exploration was focused sufficiently by structure prediction and property‐targeted machine learning to enable the isolation of this material (see picture), whose structural motifs provide new insight into the control of heat transport.

Software version control platforms, such as GitHub, host millions of open-source software projects. Due to their diversity, these projects are an appealing realm for discovering software trends. In our work, we seek to quantify synergy between software projects by connecting them via their similar as well as different software features. Our approach is based on the Literature-Based-Discovery (LBD), originally developed to uncover implicit knowledge in scientific literature databases by linking them through transitive connections. We tested our approach by conducting experiments on 13,264 GitHub (open-source) Python projects. Evaluation, based on human ratings of a subset of 90 project pairs, shows that our developed models are capable of identifying potential synergy between software projects by solely relying on their short descriptions (i.e. readme files).

While open-source software has become ubiquitous, its sustainability is in question: without a constant supply of contributor effort, open-source projects are at risk. While prior work has extensively studied the motivations of open-source contributors in general, relatively little is known about how people choose which project to contribute to, beyond personal interest. This question is especially relevant in transparent social coding environments like GitHub, where visible cues on personal profile and repository pages, known as signals, are known to impact impression formation and decision making. In this paper, we report on a mixed-methods empirical study of the signals that influence the contributors' decision to join a GitHub project. We first interviewed 15 GitHub contributors about their project evaluation processes and identified the important signals they used, including the structure of the README and the amount of recent activity. Then, we proceeded quantitatively to test out the impact of each signal based on the data of 9,977 GitHub projects. We reveal that many important pieces of information lack easily observable signals, and that some signals may be both attractive and unattractive. Our findings have direct implications for open-source maintainers and the design of social coding environments, e.g., features to be added to facilitate better project searching experience.

Data archive for Bradley, R., and J. G. Venditti, (2019), The Growth of Dunes in Rivers, Journal of Geophysical Research: Earth Science. This dataset includes Growth Time Series (Growth_Time_Series.xlsx), Bed Maps (BedMapsXYZ.zip), and a ReadMe file. This dataset was originally deposited in the Simon Fraser University institutional repository.

This describes the artifact associated with the article "Representation of Developer Expertise in Open Source Software" at the International Conference on Software Engineering 2021. The aim of the original paper was to define a feasible representation of a developer's expertise in specific focus areas of software development by gauging their fluency with different sets of APIs. The artifact is made available through Zenodo under the CC-BY-4.0 license at https://doi.org/10.5281/zenodo.4457107. The README file has detailed instructions on how to replicate the results presented in the original paper. The artifact includes the input dataset (with the developers' names and email addresses replaced by their corresponding SHA1 digest values to protect privacy) and all the associated scripts. The trained Doc2Vec models are also included in the artifact. These models can be used to obtain the Skill Space representations of developers, projects, and APIs without having to re-train the model.

The data files for this item are under embargo pending peer review of the corresponding paper. They consist of 2 3D models of segmented oolites in .raw format and 2 corresponding image stacks in .tif format. Please refer to the README file for more details.

We present here an open-source platform capable of simulating Optical Coherence Tomography (OCT) images to be used by the community. Such a simulator can have a variety of applications such as the training of neural networks. Our first-generation Monte Carlo simulator quantifies detected photons path length. The code achieves reasonable runtime by exploiting a bias scattering scheme. The results are then interpreted to produce an OCT A-line. A Github platform was implemented with necessary documentation (e.g. readme file and instructions guide) to allow a variety of application and geometry as well as community-based improvements over time.

Open source software has drawn more and more attention from researchers, developers and companies nowadays. Meanwhile, many Chinese technology companies are embracing open source and choosing to open source their projects. Nevertheless, most previous studies are concentrated on international companies such as Microsoft or Google, while the practical values of open source projects of Chinese technology companies remain unclear. To address this issue, we conduct a mixed-method study to investigate the landscape of projects open sourced by three large Chinese technology companies, namely Baidu, Alibaba, and Tencent (BAT). We study the categories and characteristics of open source projects, the developer's perceptions towards open sourcing effort for these companies, and the internationalization effort of their open source projects. We collected 1,000 open source projects that were open sourced by BAT in GitHub and performed an online survey that received 101 responses from developers of these projects. Some key findings include: 1) BAT prefer to open source frontend development projects, 2) 88\% of the respondents are positive towards open sourcing software projects in their respective companies, 3) 64\% of the respondents reveal that the most common motivations for BAT to open source their projects are the desire to gain fame, expand their influence and gain recruitment advantage, 4) respondents believe that the most common internationalization effort is "providing an English version of readme files", 5) projects with more internationalization effort (i.e., include an English readme file) are more popular. Our findings provide directions for software engineering researchers and provide practical suggestions to software developers and Chinese technology companies.

To address a need for improved tools for annotation and comparative genomics of bacteriophage genomes, we developed multiPhATE2. As an extension of multiPhATE, a functional annotation code released previously, multiPhATE2 performs gene finding using multiple algorithms, compares the results of the algorithms, performs functional annotation of coding sequences, and incorporates additional search algorithms and databases to extend the search space of the original code. MultiPhATE2 performs gene matching among sets of closely related bacteriophage genomes, and uses multiprocessing to speed computations. MultiPhATE2 can be re-started at multiple points within the workflow to allow the user to examine intermediate results and adjust the subsequent computations accordingly. In addition, multiPhATE2 accommodates custom gene calls and sequence databases, again adding flexibility. MultiPhATE2 was implemented in Python 3.7 and runs as a command-line code under Linux or MAC operating systems. Full documentation is provided as a README file and a Wiki website.

Coordination of stem cell function by local and niche-derived signals is essential to preserve adult tissue homeostasis and organismal health. The vasculature is a prominent component of multiple stem cell niches. However, its role in adult intestinal homeostasis remains largely understudied. Here, we uncover a previously unrecognised crosstalk between adult intestinal stem cells (ISCs) inDrosophilaand the vasculature-like tracheal system, which is essential for intestinal regeneration. Following damage to the intestinal epithelium, gut-derived reactive oxygen species (ROS) activate tracheal HIF-1α and bidirectional FGF/FGFR signaling, leading to reversible remodelling of gut-associated terminal tracheal cells and ISC proliferation following damage. Unexpectedly, ROS-induced adult tracheal plasticity involves downregulation of the tracheal specification factortrachealess(trh) and upregulation of IGF2 mRNA-binding protein (IGF2BP2/Imp). Our results reveal a novel intestine/vasculature interorgan communication program, which is essential to adapt stem cells response to the proliferative demands of the intestinal epithelium.

“Readme” = Summary of accumulated data (complete) on Extant Object Identifier (Confirmed) FuzzySet designated “Humanity” (Η) and members of set Η such that Η = {h0, h1, h2, h3…hk}: Extant Object Identifier (Confirmed) Individual h defined in dataset “ΗTotal” in accumulated data (complete): FuzzySet (Inferred) “Humanity” = Η = N = 4.2 × 109 h (Extant Object Identifier (Confirmed)) (=n) + 9.7 × 109 h (Extant Object Identifier (Inferred)): User’s primary analogue interface, designated [“Roger Waters,” Η/AJTT1024 42, Entity Level = 0.000000022]: Maximum recorded Entity Level (h) = 0.00000736.

A document for those wishing to use the Project Places dataset with author contacts and information on interview schedule, calendar for data collection and annotation.

This system description paper describes our participation in CMCL 2021 shared task on predicting human reading patterns. Our focus in this study is making use of well-known,traditional oculomotor control models and machine learning systems. We present experiments with a traditional oculomotor control model (the EZ Reader) and two machine learning models (a linear regression model and a re-current network model), as well as combining the two different models. In all experiments we test effects of features well-known in the literature for predicting reading patterns, such as frequency, word length and predictability.Our experiments support the earlier findings that such features are useful when combined.Furthermore, we show that although machine learning models perform better in comparison to traditional models, combination of both gives a consistent improvement for predicting multiple eye tracking variables during reading.

As part of the curriculum, students learn different subjects and in order to showcase their learning, mini projects in the respective subjects every year and a major project in the final year of their study has to be developed. Students work on personal machines which have preset environments and a tools base to facilitate seamless running of the code and project demonstration. The problem here is when these projects are submitted by students, they are received in softcopy form and are stored along with a readme file. All the projects which are submitted have different runtime environments and specific conditions which need to be replicated for successful execution of the project. So whenever there is a need to refer to or demonstrate the projects in the future to various committees or junior students, setting up of environment and tools base must be done for several projects keeping in mind the various versions of softwares to be used and resolving conflicts arising due to dependencies issue. All of this simply multiplies the effort required by the teachers/evaluators and significantly decreases the efficiency of the overall process hence limiting the access of the project to just one terminal that has been setup. More often, projects are preserved in the form of CDs and/or physical files which are subject to wear and tear leading to an arduous process of recovery. Ours is a cloud-based web application supported by DevOps practices, which helps users in creating images for their project and storing them in a persistent state. Also, it will help users to emulate projects by running containers of their image. We have built a cloud based private network which acts as the backbone of our system. The projects are saved as Docker images i.e. the snapshot of their project along with the runtime environment. These projects can then be emulated in a container at any time and can be accessed from anywhere using all the benefits cloud computing offers.

Many platforms exploit collaborative tagging to provide their users with faster and more accurate results while searching or navigating. Tags can communicate different concepts such as the main features, technologies, functionality, and the goal of a software repository. Recently, GitHub has enabled users to annotate repositories with topic tags. It has also provided a set of featured topics, and their possible aliases carefully curated with the help of the community. This creates the opportunity to use this initial seed of topics to automatically annotate all remaining repositories, by training models that recommend high-quality topic tags to developers. In this work, we study the application of multi-label classification techniques to predict software repositories' topics. First, we map the large space of user-defined topics to those featured by GitHub. The core idea is to derive more information from projects' available documentation. Our data contains about $152$K GitHub repositories and $228$ featured topics. Then, we apply supervised models on repositories' textual information such as descriptions, README files, wiki pages, and file names. We assess the performance of our approach both quantitatively and qualitatively. Our proposed model achieves Recall@5 and LRAP scores of $0.890$ and $0.805$, respectively. Moreover, based on users' assessment, our approach is highly capable of recommending a correct and complete set of topics. Finally, we use our models to develop an online tool named \texttt{Repository Catalogue}, that automatically predicts topics for GitHub repositories and is publicly available.

Many AI researchers are publishing code, data and other resources that accompany their papers in GitHub repositories. In this paper, we refer to these repositories as academic AI repositories. Our preliminary study shows that highly cited papers are more likely to have popular academic AI repositories (and vice versa). Hence, in this study, we perform an empirical study on academic AI repositories to highlight good software engineering practices of popular academic AI repositories for AI researchers. We collect 1,149 academic AI repositories, in which we label the top 20% repositories that have the most number of stars as popular, and we label the bottom 70% repositories as unpopular. The remaining 10% repositories are set as a gap between popular and unpopular academic AI repositories. We propose 21 features to characterize the software engineering practices of academic AI repositories. Our experimental results show that popular and unpopular academic AI repositories are statistically significantly different in 11 of the studied features---indicating that the two groups of repositories have significantly different software engineering practices. Furthermore, we find that the number of links to other GitHub repositories in the README file, the number of images in the README file and the inclusion of a license are the most important features for differentiating the two groups of academic AI repositories. Our dataset and code are made publicly available to share with the community.

A readme is a guide to your dataset and is usually a plain text file to maximize its usability and long-term preservation potential. This guide provides researchers with the information and tools necessary to write a README file.

Les fichiers README sont des guides, habituellement en texte clair, qui maximisent la stabilité et la préservation à long terme de vos ensembles de données. Le présent guide apportera aux chercheurs l'information et les outilsnécessaires pour écrire un fichier README.

"Datendokumentation leicht gemacht! Ein interaktiver Online-Workshop" richtet sich insbesondere an FDM-Trainer*innen und Data Stewards, aber auch Forschende sind potenzielle Teilnehmende. Mit dem eineinhalbstündigen Online-Workshop wird die Datendokumentation als lästiges aber notwendiges Thema im Forschungsdatenmanagement aufgegriffen und Lehrkonzepte umgesetzt, die die Interaktivität von Online-Veranstaltungen steigern. Das Konzept und die zugehörigen Materialien wurden von der UAG Schulungen/Fortbildungen der DINI/nestor-AG Forschungsdaten entwickelt und zusammengestellt. Ziel: Sensibilisierung für und Vermittlung von Kompetenzen zum Thema Datendokumentation Notwendiges Material: Die Lehreinheit umfasst PowerPoint-Präsentationsfolien, ein Lehrdrehbuch mit detailliertem Ablauf des Online-Workshops, ein Beispieldatensatz zur Sensibilisierung für das Thema, eine Sammlung von Tipps zur Verbesserung der Tabelle (Musterlösung), ein Data-Dictionary-Template, ein Readme-Template sowie ein Beispielpadlet für die Sammlung von notwendigen Metadaten. Vorkenntnisse: keine Zielgruppe: FDM-Trainer*innen, Data Stewards und (mit leichten Anpassungen) Forschende Dauer: 90 Minuten (inkl. Pufferzeit) &nbsp

Ineffective knowledge dissemination contributes to clinical practice and service improvements not being realised. Meaningful knowledge translation can occur through the understanding and matching of appropriate communication mediums that are relevant for different stakeholders or audiences. To this end, we present a dissemination instrument, the ‘REAch and Diffusion of health iMprovement Evidence’ (README) checklist, for the communication of research findings, integrating both traditional and newer communication mediums. Additionally, we propose a ‘Strategic Translation and Engagement Planning’ (STEP) tool, for use when deciding which mediums to select. The STEP tool challenges the need for communicating complex and simple information against the desire for passive or active stakeholder interaction. Used collaboratively by academics and health professionals, README and STEP can promote co-production of research, subsequent diffusion of knowledge, and develop the capacity and skills of all stakeholders.

Good software documentation encourages good software engineering, but the meaning of "good" documentation is vaguely defined in the software engineering literature. To clarify this ambiguity, we draw on work from the data and information quality community to propose a framework that decomposes documentation quality into ten dimensions of structure, content, and style. To demonstrate its application, we recruited technical editors to apply the framework when evaluating examples from several genres of software documentation. We summarise their assessments -- for example, reference documentation and README files excel in quality whereas blog articles have more problems -- and we describe our vision for reasoning about software documentation quality and for the expansion and potential of a unified quality framework., to appear in the Visions and Reflections Track of the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering 2020

GitHub has become a precious service for storing and managing software source code. Over the last year, 10M new developers have joined the GitHub community, contributing to more than 44M repositories. In order to help developers increase the reachability of their repositories, in 2017 GitHub introduced the possibility to classify them by means of topics. However, assigning wrong topics to a given repository can compromise the possibility of helping other developers approach it, and thus preventing them from contributing to its development. In this paper we investigate the application of Multinomial Naive Bayesian (MNB) networks to automatically classify GitHub repositories. By analyzing the README file(s) of the repository to be classified and the source code implementing it, the conceived approach is able to recommend GitHub topics. To the best of our knowledge, this is the first supervised approach addressing the considered problem. Consequently, since there exists no suitable baseline for the comparison, we validated the approach by considering different metrics, aiming to study various quality aspects.

This paper proposes a new aggregated classification scheme aimed to support the implementation of semantic text analysis methods in contexts characterized by the presence of rare text categories. The proposed approach starts from the aggregate supervised text classifier developed by Hopkins and King and moves forward, relying on rare event sampling methods. In detail, it enables the analyst to enlarge the number of estimated sentiment categories, both preserving the estimation accuracy and reducing the working time to unconditionally increase the size of the training set. The approach is applied to study the daily evolution of the web reputation of one of the last mega-event taking place in Europe: Expo Milano. The corpus consists of more than one million tweets in both Italian and English, discussing about the event. The analysis provides an interesting portrayal of the evolution of the Expo stakeholders’ opinions over time and allows the identification of the main drivers of the Expo reputation. The algorithm will be implemented as a running option in the next release of the R package ReadMe.

Internet of Things (IoT) integrates billions of the heterogeneous IoT things with the Internet in which the embedded systems such as sensors and actuators linked together to improve quality of life, and becomes the future of technologies in any field of human daily life. These IoT devices cooperate with each other and generate useful information to provide better services and applications to the governments and the society. Also, there is a need to store these data on Cloud for monitoring. This paper, surveys IoTapplications, new challenges and issues arise in different fields and provides IoT architecture, focuses on explanation of IoT protocols and their operations and functionalities, presents different microcontroller types used by researchers. With the huge amount of data generated from IoT devices, the integrating Cloud and IoT may helpful, Therefore, a survey on open issues faced when these two concepts integrating together is discussed. The objective of this paper is to provide a survey for everything related to IoT and direct it to all beginners in this filed or academic researchers.

This article presents MATLAB routines that may be used to evaluate radiation-enhanced diffusion (RED) in ion irradiation materials. Four routines are included: Main, DataCollect, Diffuse, and Directory. A sample input file and README are also included. The input may be directly modified as provided and used as an input to the routines. Data from Stopping Range of Ions in Matter (SRIM) is also required as an input. A stream of data files at different damage conditions is created by the routines.

Scientific software has become a key asset to reproduce and understand the products of scientific research in many disciplines. However, scientific software is becoming increasingly complex and, as a result, researchers need to spend a significant amount of time finding, reading and understanding software documentation to set it up. In this paper we describe SoMEF, a Software Metadata Extraction Framework designed to help highlighting the most important parts of scientific software documentation. SoMEF processes the README files in GitHub repositories to automatically extract which parts of their text refer to the description, installation, invocation, or citation of a software component. Despite its simple features, SoMEF successfully categorizes README excerpts with a minimum 0.92 precision and 0.90 ROC AUC. These results, tested on a corpus of over 70 scientific software repositories, are a promising start towards automatically generating knowledge graphs of scientific software metadata.

A growing body of evidence indicates that circular RNAs (circRNAs) play a pivotal role in various biological processes and have a close association with the initiation and progression of diseases. Moreover, circRNAs are considered as promising biomarkers for disease diagnosis owing to their characteristics of conservation, stability and universality. Inferring disease–circRNA relationships will contribute to the understanding of disease pathology. However, it is costly and laborious to discover novel disease–circRNA interactions by wet-lab experiments, and few computational methods have been devoted to predicting potential circRNAs for diseases. Here, we advance a computational method (NCPCDA) to identify novel circRNA–disease associations based on network consistency projection. For starters, we make use of multi-view similarity data, including circRNA functional similarity, disease semantic similarity, and association profile similarity, to construct the integrated circRNA similarity and disease similarity. Then, we project circRNA space and disease space on the circRNA–disease interaction network, respectively. Finally, we can obtain the predicted circRNA–disease association score matrix by combining the above two space projection scores. Simulation results show that NCPCDA can efficiently infer disease–circRNA relationships with high accuracy, obtaining AUCs of 0.9541 and 0.9201 in leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, case studies also suggest that NCPCDA is promising for discovering new disease–circRNA interactions. The NCPCDA dataset and code, as well as the detailed readme file for our code, can be downloaded from Github (https://github.com/ghli16/NNCPCD).

The PROTEOFORMER pipeline feeds ribosome profiling-driven information into an MS/MS search space. The pipeline has been greatly expanded and updated since its first publication. These novelties are presented and validated with matching MS/MS data, leading to the endorsement of a set of new proteoforms on MS/MS level and to a collection of general considerations for the ribosome profiling-based proteogenomics community., Graphical Abstract Highlights PROTEOFORMER adds ribosome profiling information to MS/MS search spaces. The PROTEOFORMER pipeline is greatly expanded and updated since its first publication. New features are demonstrated with matching ribosome profiling and MS/MS data. Experiments lead to MS/MS-proven proteoforms and general proteogenomic notices., PROTEOFORMER is a pipeline that enables the automated processing of data derived from ribosome profiling (RIBO-seq, i.e. the sequencing of ribosome-protected mRNA fragments). As such, genome-wide ribosome occupancies lead to the delineation of data-specific translation product candidates and these can improve the mass spectrometry-based identification. Since its first publication, different upgrades, new features and extensions have been added to the PROTEOFORMER pipeline. Some of the most important upgrades include P-site offset calculation during mapping, comprehensive data pre-exploration, the introduction of two alternative proteoform calling strategies and extended pipeline output features. These novelties are illustrated by analyzing ribosome profiling data of human HCT116 and Jurkat data. The different proteoform calling strategies are used alongside one another and in the end combined together with reference sequences from UniProt. Matching mass spectrometry data are searched against this extended search space with MaxQuant. Overall, besides annotated proteoforms, this pipeline leads to the identification and validation of different categories of new proteoforms, including translation products of up- and downstream open reading frames, 5′ and 3′ extended and truncated proteoforms, single amino acid variants, splice variants and translation products of so-called noncoding regions. Further, proof-of-concept is reported for the improvement of spectrum matching by including Prosit, a deep neural network strategy that adds extra fragmentation spectrum intensity features to the analysis. In the light of ribosome profiling-driven proteogenomics, it is shown that this allows validating the spectrum matches of newly identified proteoforms with elevated stringency. These updates and novel conclusions provide new insights and lessons for the ribosome profiling-based proteogenomic research field. More practical information on the pipeline, raw code, the user manual (README) and explanations on the different modes of availability can be found at the GitHub repository of PROTEOFORMER: https://github.com/Biobix/proteoformer.

Nowadays, the effective utilization of open-source software could significantly boost both clinical research and practices, especially in resource-poor countries. However, the plethora of open-source clinical software has left many people unable to quickly locate the appropriate one for their needs. Commonly available software quality metrics and software documentation, such as downloads, forks, stars, and readme files, are useful selection criteria, but they only indicate the software quality from the perspective of IT experts. This paper proposes a method that offers additional insights on the performance and effectiveness of clinical software. It links open-source clinical software with relevant scientific literature, such as papers that use case studies of clinical software to reveal the strength and weakness of a given software from the clinical perspective. To interactively present the open-source clinical software and their related literature, we have developed the LOCATE web a pplication that enables users to explore related literature for a given open-source clinical software. Moreover, the peer-review cycle of the application allows users to improve the application by confirming, adding or removing related literature. An evaluation experiment of the five most popular open-source clinical tools demonstrates the potential usefulness of LOCATE.

Essay writing is a difficult cognitive task for both students striving to express their ideas, as well as for tutors investing time to perform evaluations. Besides the validity of the presented ideas, students should verify that their writing is correct and coherent before submitting their essay. ReadME provides an automated method for students to evaluate and improve their writing style based on personalized suggestions. The system is also beneficial for tutors who receive a substantial number of essays for grading and require support. ReadME provides feedback at different granularity levels based on advanced Natural Language Processing techniques used to analyze the submitted texts in terms of lexicon, morphology, syntax, semantics, discourse, with emphasis on text cohesion. The prototype system is available for Romanian and English languages.

Hybridization among populations and species is a central theme in many areas of biology, and the study of hybridization has direct applicability to testing hypotheses about evolution, speciation and genetic recombination, as well as having conservation, legal and regulatory implications. Yet, despite being a topic of considerable interest, the identification of hybrid individuals, and quantification of the (un)certainty surrounding the identifications, remains difficult. Unlike other programs that exist to identify hybrids based on genotypic information, newhybrids is able to assign individuals to specific hybrid classes (e.g. F1 , F2 ) because it makes use of patterns of gene inheritance within each locus, rather than just the proportions of gene inheritance within each individual. For each comparison and set of markers, multiple independent runs of each data set should be used to develop an estimate of the hybrid class assignment accuracy. The necessity of analysing multiple simulated data sets, constructed from large genomewide data sets, presents significant computational challenges. To address these challenges, we present parallelnewhybrid, an r package designed to decrease user burden when undertaking multiple newhybrids analyses. parallelnewhybrid does so by taking advantage of the parallel computational capabilities inherent in modern computers to efficiently and automatically execute separate newhybrids runs in parallel. We show that parallelization of analyses using this package affords users several-fold reductions in time over a traditional serial analysis. parallelnewhybrid consists of an example data set, a readme and three operating system-specific functions to execute parallel newhybrids analyses on each of a computer's c cores. parallelnewhybrid is freely available on the long-term software hosting site github (www.github.com/bwringe/parallelnewhybrid).