dataesr/scientific-paragraphs-categorization huggingface dataset:
They include tabular data per subject in. xlsx Excel spreadsheet format within each included participant's folder. In this spreadsheet, each row corresponds to a point of interest within one trial with data and factors as columns. Additionally, a MATLAB. mat file is stored in the current working directory which includes the same data as the. xlsx files but for all participants that have been selected for aggregation within SollarPlot. This file is also used for data storage within SollarPlot, which means that it is updated as dummy variables are created within SollarPlot and can be loaded again when re-opening SollarPlot at a later time. In addition to that, an 'Export Plots' button in SollarPlot's GUI is available for both kinds of plots. It creates a clean rendition of the current plot in a separate figure window, prompts the user for a filename, and exports this plot to PNG and EPS formats.
Examples of data visualization In the following, we showcase applications of SollarSuite to demonstrate how it can help pre-process ultrasound data and improve the quality of tongue contours, especially when dealing with a recording situation wherein less control is exercised over the participants (e. g. , with children). The studies mentioned below have been approved by the ethical Committee of the University of Potsdam and conform to the Declaration of Helsinki. The first example (Figure  7 ) provides an illustration of six averaged midsagittal tongue contours of an adult participant created with SollarPlot. These tongue contours were obtained subsequent to the recording of pseudo-words elicited for a study investigating coarticulatory effects from vowels onto various preceding consonants.
, 2010). During the production tasks, participants' lips can be painted in blue as this color maximizes contrast with the skin. In post-processing the video data, the blue shapes corresponding to the lips can be tracked for measurement of lip aperture, interlabial area, and upper lip protrusion. While this feature could easily be integrated in SollarSuite, it has not been our focus so far. In the future, SOLLAR can potentially be used in clinical practice, e. g. , for the description and diagnostic of speech-related disorders (e. g. , speech sound disorder: Cleland et al. , 2015; stuttering:  Lenoci & Ricci, 2018). However, in its current state, the SOLLAR platform requires some space and uses several pieces of equipment in addition to the ultrasound device which may only be available in laboratories, not in speech and language therapy offices.
Conclusion SOLLAR has been designed to respond to the growing need among developmental psycholinguists and phoneticians to collect kinematic data in young children and the concurrent lack of suitable methods. While SOLLAR does not solve all experimental challenges, it has been designed as a child-friendly environment that can fairly easily be implemented to record kinematic data in young children. In future studies, it may be combined with other behavioral methods (e. g. , eye-movement tracking, EEG) to develop more integrated empirical approaches to language acquisition, in which concurrent examinations of speech motor and cognitive abilities are possible (e. g. , perception, attention) as well as their on-line interactions.
Acknowledgements This research was supported by the  Deutsche Forschungsgemeinschaft  ( 255676067  and  1098 , recipient:  Aude Noiray) ,  Natural Sciences and Engineering Research Council of Canada  ( Discovery Grant , recipient:  Lucie Ménard ). Our gratitude to  Anthony de Simone  for constructing the probe holder and pedestal used in our research. We also thank all students at LOLA who have contributed to improve the SOLLAR platform over the past couple of years and to a broader extent, researchers in the ultrasound imaging research community who have developed various methods for the collection and processing of speech-related ultrasound data. The design of SOLLAR has certainly benefitted from their efforts.
State of the art Many papers focus on benchmarking different container solutions but few of them are focusing only on a particular feature comparison of containers. In  [1, 2, 3, 4, 5, 6, 7, 8] , the main container's solutions such as Docker, LXC, OpenVZ, Singularity are compared with hypervisors like KVM and Xen in terms of performance. Those works mainly measure the CPU and memory load, networking bandwidth, and disk I/O. Results point to the overhead of hypervisorbased virtual machines and also the lack of security of the container solutions. The OpenVZ and Linux-Vserver solutions have been gradually dropped  [2, 4] , and replaced by solutions such as Docker and LXC because they do not require a specific kernel to work. In  [6]  the authors focus on an important comparison aspect: the boot-up speed.
LXC and LXD LXC  [10]  is a userspace interface for the Linux kernel containment features. Through a powerful API and simple tools, it lets Linux users easily create and manage systems or application containers. LXD  [10]  is building on top of LXC to provide an alternative to LXC's tools and distribution template. Its image-based with pre-made images available for a wide number of Linux distributions and is built around a very powerful, yet pretty simple, REST API.
Kata-containers (kata-runtime). Kata-containers  [12]  is an open project supports by the OpenStack Foundation (OSF) working to build a standard implementation of lightweight Virtual Machines (VMs) that feel and perform like containers, but provide the workload isolation and security advantages of VMs. The Kata-containers runtime (kataruntime) is compatible with the OCI runtime specification. In other words, docker can select without changing any configuration between the default Docker container runtime (runc) and kata-runtime. gVisor (runsc). gVisor  [9]  is a user-space kernel, written in Go, that implements a substantial portion of the Linux system call interface. gVisor intercepts the application system calls and acts as the guest kernel. As gVisor has not implemented all the Linux syscalls, applications that use unimplemented syscalls can't run in gVisor.
Isolation Features The main isolation feature that forms the basis of a container is the namespace concept. Namespace  [7, 13, 14, 15]  is a security feature of the Linux kernel in version 2. 4. 19 allowing resource isolation to a group of processes. This allows a group of processes to use their isolated instance of system resources. Theses resources are invisible from another namespace. Each process has a /proc/[pid]/ns/subdirectory containing one entry for each namespace. The kernel provides six namespaces to isolate different resources: Mount, IPC, Network, PID, User, and UTS. The Mount namespace provides isolation of the list of mount points seen by the processes inside the namespace. The IPC namespace provides isolation of IPC resources like System V IPC objects and POSIX message queue.
There is a huge security risk because if an attacker escapes from the container, as presented in  [13, 14] , he will be root on the host. To reduce the risk of escaping the container, the user namespace feature of the kernel allows creating unprivileged container which means that the user, inside the container, is mapped to an unprivileged user on the host. The main security issue in a namespace is that all containers use a shared kernel. If there is a vulnerability in the Linux kernel then all containers will be impacted. For each namespace, we tested whether it is used by verifying that the identifier in the /proc/PID/ns directory of the namespace inside the container is different from the host. If this identifier is different, we conclude that the namespace is used by the solution. Two other isolation features increase the isolation of containers and namespaces: Hypervisor and Kernel isolation.
Network Features Network management for containers is a key point. There are three main solutions to link a container to the network: System Bridge, Open vSwitch and Physical. A System Bridge behaves like a virtual network switch where real devices (e. g. eth0) and virtual devices (e. g. tap0) can be connected to it. Open vSwitch (https://www. openvswitch. org/) is a software implementation of a virtual multilayer network switch, designed to enable effective network automation through programmatic extensions, while supporting standard management interfaces and protocols. The Physical network feature allows to dedicate a physical interface to the container. Within the context of container security, it is interesting to be able to limit the bandwidth of the containers to avoid a denial of service  [7, 13, 14]  of the network of the host and other containers.
Optional manual By default, all solutions allow you to link a container to a Linux bridge but with the exception of the LXC and Docker solutions, connecting a container to an Open vSwitch bridge has to be done manually. Bandwidth limitation is by default only possible on Singularity. For the other solutions the implementation is manual either through the QoS on an Open vSwitch bridge either through traffic control and the use of Cgroups https://www. kernel. org/doc/Documentation/cgroup-v1/ for identify the flow from the containers. Dedicated a physical interface to a container is by default only possible with LXC and LXD. In terms of network features, LXD may be the most efficient choice.
BTRFS (https://btrfs. wiki. kernel. org/index. php/Main Page) is a copy on write (CoW) filesystem for Linux aimed at implementing advanced features like subvolumes, quotas, compression, deduplication, snapshot. Logical Volume Management (LVM) (https://wiki. gentoo. org/wiki/LVM) utilizes the Linux kernel's device-mapper feature to provide a system of partitions independent of the underlying disk layout. This allows a complete abstraction of the storage with "virtual partitions" that are extending/shrinking easier. ZFS (https://zfsonlinux. org/) is a combined filesystem and logical volume manager that includes extended features like BTRFS. The Overlay filesystem (OverlayFS) (https://www. kernel. org/doc/Documentation/filesystems/overlayfs. txt) allows one, usually read-write, directory tree to be overlaid onto another, read-only directory tree.
Security Features As mentioned in  [7, 13, 14]  many other Linux kernel features are involved in the security of a container: Control Groups, Capabilities, Seccomp, and Apparmor. Control Groups (Cgroups) isolate and limit hardware resource to a process or collection of processes. Capabilities is a kernel feature that divides the privileges traditionally associated with superuser into distinct units, known as capabilities, which can be independently enabled and disabled. Seccomp (https://www. kernel. org/doc/html/v4. 16/userspace-api/seccomp filter. html) provides a method to reduce the number of system calls available for application to interface with the kernel. Apparmor (https://gitlab. com/apparmor/apparmor/-/wikis/home) provides a way to confine programs to a limited set of resources. This utility is mainly used to restrict access and mount options to filesystems paths.
Background: Agronomical and plant physiological studies employ a large variety of compounds that impact root development. The effect of these compounds is often evaluated by root architecture analyses using dedicated software. During the past decade, a growing number of tools proposed complex and deep analysis of the root system architecture. While these software applications are often complex and require speci c set up, here, we propose a simple method based on the most common tools used by biologists: ImageJ and Excel®. Results: First, roots are measured manually with ImageJ following a succession of operations (i. choose a plant; ii. Measure: a. the primary root -b. lateral roots). Secondly, RootIX, an Excel le with dedicated macros that will automatically extract, sort and treat the data, is launched.
2010) ; and interaction, e. g. jasmonates promote lateral root formation through its interaction with auxin but also inhibit primary root growth (Xue and Zhang 2007;  Sun et al. 2009 ). To gain more details on the subtle hormone control of root growth, the reader may appreciate the nice reviews written by  Jung and McCouch, 2013  or Rutten and ten Tusscher, 2019. Despite the strong interest to understand how the hormonal balance may be controlled by a given treatment, in many cases, the point to determine is just the effect on elongation and lateral root formation. Numerous software solutions bring extensive and deep root architectural analysis, such as WinRHIZO, IJ_Rhizo  (Pierret et al. 2013)  or archiDART  (Delory et al. 2018 ). However, many scientists and papers prefer to measure root lengths with their regular tools (ImageJ, ruler,…)  (Li et al.
2014 ; Pereira and Pereira 2018; Fonouni-Farde et al. 2019) rather than investing time and money in a strong and often complex root architecture analysis software. We faced this context while studying the effect of algae extracts on roots. After several failures with some of the above-mentioned programs, we get back to ImageJ manual measurements and developed the RootIX solution to extract, sort and treat the huge quantity of generated data. Finally, we propose to share this tool that may be appreciated by all of those who just need the essential details about root growth and lateral root formation rather than complete 3D architecture, and by the way who want to save time and money.
Computing environment RootIX is an Excel le with a batch of macros developed in Visual Basic for Excel which is the well-known spreadsheet developed by Microsoft. This commonly used software is part of the Microsoft O ce 365 Suit available on https://products. o ce. com for Windows and MACOS. Notice that it may be free for university members. Furthermore, RootIX is compatible with all Excel version since the 2007 version until the Microsoft O ce 365 suit. A macro is an automated program that can be launch from a simple button present in an Excel worksheet. Here, RootIX will analyze and organize the data obtained with ImageJ. ImageJ is a public image processing program developed at the National Institute of Health. It allows to analyze images with a large variety of tools. ImageJ can be downloaded for free from http://rsbweb.
c. Several types of calculation: RootIX collects and analyzes data from the folder whose path is lled in the worksheet (cell B1). Thus, it is required to store the les to compare in a same folder to make a comparison analysis. The macro extracts, deduces and calculates several types of information from the data les and it presents them in up to six tables (named Table When samples were measured at two different time points, RootIX calculates the percentage of increase of different parameters between the two time points in Table  2  below the previous one. Five parameters are evaluated: 1 st , it indicates the elongation rate of the whole root system (primary and lateral roots taken together). 2 nd , it estimates the part of elongation attributed to the primary root. 3 rd , the elongation rate of the primary root is indicated.
Measurement First, each ten-day-old plant of a culture plate has been measured and saved in a same le (one data le = one condition/one plate of 19. 2 ±2. 4 plants). Plates were scanned immediately after treatment (T0h) and four days later (T72h). Three independent experiments were conducted. Roots were measured with ImageJ following the indicated sequence for each plant: primary root (RI) followed by its lateral roots: secondary roots (RII) and tertiary roots (RIII). Secondly, the large amount of data generated needed to be extracted, sorted and analyzed. RootIX was developed for this purpose. RootIX was ran and gave a high number of information that were then analyzed with GraphPad Prism6 to determine their statistically reproducibility (Fig2). This analysis was conducted on the sum of the primary root length with its lateral roots' length (RI + RII length).
Root development All roots lengths were compared before and 72h after treatment in order to determine how treatments affect root elongation. We evaluated the mean of RI and RII development under the different treatments in uence with the standard error of the mean (SEM) as error value. Data were also submitted to Dunnett's multiple comparison test against the water condition. RI elongation (Fig3A). H2O treated plants presented a RI elongation of 7. 98 ±0. 51mm when g22 treatment slightly reduced the elongation with slight difference to 6. 02 ±0. 58mm (P value = 0. 0360). AlgEx1 at 2000ppm treatment showed a phenotype strongly close to water with an elongation of 7. 73 ±0. 54mm (P value = 0. 9994). However, when the dose increased to 6000ppm, elongation decreased signi cantly to 4. 45 ±0. 78mm with a P value <0.
Advantages Our RootIX method does not require to install speci c software nor deep con gurations. It only needs the two most common biologist software ImageJ and Excel. To ensure the RootIX operation, measurement must follow this simple sequence: 1 st a plant is chosen, 2 nd its primary roots (RI) is measured, 3 rd its lateral roots (RIIs) are measured, 4 th a second plant is chosen and so forth. Finally, RootIX provides data in a few clicks: elongation rates; number of additional RIIs; RI, RII and total roots (RI+RII of a same plant) sizes (Table  2 ). Graphs (Fig3 and 4) are ready to be interpreted as well as tables are ready for statistical analysis. For the complete explanation of the tool functioning, please refer to the material and methods chapter. This tool is wanted to be totally free and thus enables low-cost experiments that may be useful for researchers with tight budget or who suffers underinvestment such as in emerging and developing countries (UNESCO and Schlegel 2015).
Indeed, data obtained with WinRhizo© were strongly overvalued due to the di cult a liation of roots that cross each other (Table  2 ). However, since it uses ImageJ, the manual measurement could be bypassed, while it is the surest method to avoid artefacts. To this end, it is possible to create/use ImageJ macro that would measure root lengths in the required sequence (RI then RIIs for each plant) to allow RootIX analyzes. It may be frequent to record root length in a large variety of scienti c projects but the use of strong software or ImageJ packages such as evocated above are more dedicated to scientists with signi cant computing backgrounds. For example, IJ_Rhizo is an ImageJ macro and archiDART a R package, both require relevant computing capacities in ImageJ macro or R language to fully perform their operation that may be discouraging for someone with less digital experiences.
Finally, soon come the time to choose to invest time in understanding how to use the software, or to simply invest the time on taking the measures and process the data with traditional methods (ruler, ImageJ manual measure, spreadsheet data treatment). Indeed some scientists prefer to use easier methods even more time-consuming  (Li et al. 2014 ; Pereira and Pereira 2018; Fonouni-Farde et al. 2019). RootIX method may be a very good opportunity to ll the gap and bring them an easy to use tool that can help making use of their raw data and save another very time-consuming part of the work.
views on policy, but IZA takes no institutional policy positions. The IZA research network is committed to the  IZA Guiding Principles of Research Integrity. The  IZA Institute  based policy advice on labor market issues. Supported by the  Deutsche Post Foundation , IZA runs the world's largest network of economists, whose research aims to provide answers to the global labor market challenges of our time. Our key objective is to build bridges between academic research, policymakers and society. * Zhang acknowledges the financial support from  Singapore Ministry of Education Tier 2 grant  under grant no. MOE2018-T2-2-169.
Background A genetically modified organism (GMO) is a living being "in which the genetic material has been altered in a way that does not occur naturally by mating and/or natural recombination" (European Commission, 2001)  [1]. We call the host genome the wild, original genome to which a genetic modification was made to build a GMO. In this article, we focus on GMOs resulting from the integration of exogenous genes into the host genome. European legislation requires the presence of GMOs in food products to be reported when they represent more than 0. 9% of the ingredients  [2]. This regulation requires that all GMOs or products sourced from GMOs must be declared to the European authorities, the modifications of their nucleic acid sequences have to be recorded in appropriate databases, and a detection method based on standard molecular techniques (e.
g. , PCR) must be provided. The GMOMETHODS  [3] , Eugenius  [4] , and GMO-Amplicon  [5]  databases list the GMOs authorized on the European market and their associated detection methods. In addition, Morisset et al. 2014  [6]  developed a software program called GMOseek to optimise routine GMO testing in the laboratory, thus helping decision-making at all phases of analysis. More recently, with the development of high-throughput whole genome sequencing (WGS)  [1, [7] [8] [9] [10] , bioinformatics analyses of the sequencing datasets based on alignment similarities  [1, 7, 8]  have been developed to characterize referenced GMO elements. Willems et al. 2016  [11]  proposed a statistical framework based on next generation sequencing (NGS) to predict the number of reads required to detect insert sequences, and thus confirm their integration into the host genome.
DUGMO detects sequences of unknown exogenous inserts of a genetically modified (GM) bacterium. It uses high-throughput sequencing data that are previously cleaned and sorted thanks to a dedicated pipeline and two BLASTN alignments on the host pangenome (section 2. 2). After the cleaning and sorting steps, a databank of known GMO inserts is cleaned to remove wild CDSs of the species (section 2. 2). The proposed tool is based on statistical calculation of distances (section 2. 3) associated with an automated learning step (section 2. 4). GMOs resulting from small modifications by deletion or substitution will not be taken into account. GMOs resulting from methodologies such as CRISPR-CAS9  [16]  can only be detected if they incorporate at least one exogenous CDS or involve gene truncation or fusion.
Datasets A GM strain of B. subtilis has been sequenced at the Bavarian Health and Food Safety Authority (LGL) in Germany in the Hi-seq 1500 system  [17, 18]. This dataset is called 'Data1' in the following. The paired Hi-seq 2500 sequencing data of the wild type B. subtilis 9407 strain used are available on the NCBI Sequence Read Archive (SRA) under accession number SRR8935610. This dataset is called 'Data2' in the following. The paired Mi-seq sequencing data of the three GM strains of Escherichia coli  [19]  used are available on the NCBI SRA under accession numbers SRR9304542, SRR9304539, and SRR9304540. These three genomes come from the same strain but each one has a distinct plasmid containing the parE gene from one of three different species: Agrobacterium tumefaciens, Mycobacterium tuberculosis and Streptococcus pyogenes.
At the time of writing this article, the NGS datasets used are the only ones publicly available clearly described as GM bacteria. Two reference genomes, B. subtilis str. 168 (NC_000964. 3) and E. coli str. K-12 substr. MG1655 (U00096. 3) were also used. Two pangenomes were constructed, one for B. subtilis composed of 37 strains, and one for E. coli composed of 45 strains. Pangenomes were constructed manually to encompass the full range of analysed species and are available in the "testdata" directory of the DUGMO git repository. For B. subtilis, we took a known pangenome  [20]  and enriched it with wild strains from various environments available on the NCBI (list of accession numbers given in the Additional file 1, section 2). For E. coli, the 5 main species subgroups  [21]  were considered and enriched with newer complete PacBio genomes and plasmids from various environments available on the NCBI (list of accession numbers given in the Additional file 1, section 3).