dataesr/scientific-paragraphs-categorization huggingface dataset:
Calculation of GC Content We calculated the GC content for each gene plus its 2-and 10-kb flanking regions excluding any TEs. The human chromosome sequences were downloaded from Ensembl (https:// www. ensembl. org/ release 88, March 2017) and masked using a Python program if the presence of TE sequences was detected with the program "One code to find them all" (see above) with the result that each base of a TE sequence was replaced with an X. For each gene localized on the masked chromosomes, we extracted the sequences plus its 2-and 10kb flanking regions. GC content in the vicinity of genes was defined as: %GC ¼ N G þ N C L g þ ð2 Â L f Þ À N x Â 100 where N G is the number of G bases, N C the number of C bases, L g the length of the gene, L f the length of the chosen flanking region (2 or 10 kb), and N X the number of Xs.
Analysis of Protein Function Distribution We obtained human phenotypic data from the database of Online GEne Essentiality (OGEE)  (Chen et al. 2017). We retrieved 21,556 different genes whose essentiality have been tested under 18 conditions by  Chen et al. (2017). Among these genes  (Chen et al. 2017) , 6,985 are conditional essential genes (CEGs) (37% of tested genes) as defined by  Chen et al. (2017)  as they are only essential in certain conditions, whereas 183 were defined as essential genes because they are essential in all conditions tested, making a total of 7,168 genes with some degree of essentiality. In OGEE, each of the 21,556 genes tested was considered as essential if found to be so in a minimum of 1 and a maximum of 11 datasets among the 18. To redefine different levels of essentiality and conditional essentiality, we first selected only genes that had been tested for their essentiality in at least five datasets, a total of 17,322 genes.
We then defined five categories of essentiality for these genes: "Essential" (E) genes were essential in all the test datasets in which they appeared: "Conditional Restricted Essential" (CRE) genes were essential in fewer than 25% of the test datasets, "Conditional Medium Essential" (CME) genes were essential in between 25% and 75% of the test datasets and "Conditional Largely Essential" (CLE) genes were essential in more than 75% of the test datasets. Other genes were defined as "Non essential" (NoE). We thus retrieved 118 E genes, 3,808 CRE genes, 1,983 CME genes, 984 CLE genes, and 10,423 NoE genes. GO term enrichments for groups of genes were calculated with the web-based application PANTHER version 14. 1  (Mi et al. 2019 ). We defined gene categories according to their TE density. We did not categorize according to TE coverage for two reasons: 1) TE density is a better estimator of the evolutionary dynamics of TEs than coverage because TE density takes insertion events into account as TE copy numbers are used in the calculation, which is not the case for TE coverage; 2) for simplicity knowing that TE density and TE coverage are highly correlated  (Gr egoire et al.
2016). The TEpoor gene category was defined as the 25% of genes with the lowest TE densities (for the 2-kb flanking region this corresponds to a TE density <0. 51 and for the 10-kb flanking region TE density <0. 6009). The TE-rich gene category encompasses the 25% of genes with the highest TE densities (for the 2-kb flanking region this corresponds to a TE density !1. 63 and for the 10-kb flanking region TE density !1. 703). The remaining genes were assigned to the TE-medium gene category. For each TE density category, genes were also defined as duplicated genes or singleton genes according to dataset 1. PANTHER was used to extract Gene Ontology (GO) slim terms defined according to the PANTHER classification system  (Mi et al. 2019 ) that were significantly over-or underrepresented in each comparison.
Statistical Analysis All statistical calculations were performed using R software (Version 3. 4. 3). We considered for each dataset, each TE category, and each TE superfamily the following linear model for TE density and TE coverage. In each case, the full linear model can be written TE ¼ b 0 þ b s Statusþ b l GL þ b k K a =K s þb g GC þ b r Recomb þ b sl Status Â GL þ bsk Status Â K a =K s þb gkGL Â K a =K s þ b sg Status Â GCþ b lg GLÂ GCþ b kg K a = K s Â GCþ b rs RecombÂ Status þ b rl Recomb Â GLþ b rk Recomb Â K a =K s þ b rg Recomb Â GC þ where TE corresponds either to TE density or to TE coverage, Status corresponds to a binary variable (one for singleton genes and zero for duplicated genes), GL is the gene length, GC the GC content, K a =K s the K a =K s ratio, and Recomb the recombination rate.
We also considered for each dataset, each TE category, and each TE superfamily a similar linear model adding essentiality as a covariable for TE density and TE coverage. In each case, the full linear model can be written TE ¼ b 0 þ b s Status þb e Essþ b l GL þ b kK a =K s þ b g GC þ b r Recomb þ b se Status Â Ess þ b sl Status Â GL þ b sk StatusÂ K a =K s þ b sg Status Â GC þb sr Status Â Recombþ beta el Ess Â GLþ beta ek EssÂ K a =K s þ beta egEss Â GC þ beta er EssÂ Recomb þb lk GL Â K a = K s þ b lg GL Â GC þ b lrGL Â Recomb þ b kg K a =K s Â GCþ b kr K a =K s Â Recomb þ b gr GC Â Recomb þ where Ess corresponds to essentiality. We verified the absence of significant colinearity between the explicative variables of the linear models. TE density, K a =K s , GC, and GL values were log-transformed to obtain more symmetrical distributions.
Acknowledgments We would like to thank  Eduardo Corel ,  Claudine Devauchelle ,  Yolande Diaz , and  Emma Saulnier  for helpful discussions on the determination of duplicated genes and gene family definition. We would like to thank again  Yolande Diaz  for her technical assistance on the C code of the merge program. We are also grateful to  Agathe Guilloux  for help for statistical analysis and to  Rachel Carol  for English proofreading of the manuscript.
Beyond the classical multivariate extensions of CTs, CGs efficiently capture the whole structural information of multi-band images as directed acyclic graph (DAG) variants. Such DAG variants are more general and more powerful at the cost of a higher construction complexity. Component-graphs have been increasingly considered for detection and segmentation applications in the field  [10]. This work explores the use of the component-graphs for multi-band object detection and proposes an application to astronomical images. lenge is to develop efficient and automated tool for the large datasets/surveys. The most often used source finder is SExtractor  [11] , an efficient and easy to use application. However, it fails at detecting some faint and diffuse objects. For this reason, MTObject/Sourcerer  [12] ,  [13]  was introduced to improve the SExtractor thresholding strategy by using a CT structure.
More precisely, MTObject/Sourcerer relies on statistical tests to identify nodes of a Max-Tree that are significantly different from the background. MTObject/Sourcerer has already shown its capability at detecting faint astronomical sources  [14]  while requiring far less parameter tuning than SExtractor. However, both methods focus on singleband processing while most optical astronomical surveys are multi-band. To handle such images, that are expected to lead to an increased sensitivity, we propose to generalize the detection method based on statistical testing to CGs. The main challenge is to effectively leverage multi-bands to filter relevant information from the richer component-graph structure. This article extends our previous work  [15]  on Component Graph Objects (CGO) -a novel multi-band object detection framework with comprehensive analyses on both simulated and real datasets.
1) FDS Simulation We rely on a simulated three-band astronomical dataset with ground-truth imitating the Fornax Deep Survey  [17] , a wide field imaging survey of the Fornax Cluster using ESO's VST telescope. It contains 1500 stars and 4000 galaxies. Because the Θ construction is computationally expensive (O(n 2 )), we performed the simulation into tiles of size (500, 500) pixels with an overlapping of 250 pixels. For each tile, we have a  three-band image with a ground-truth segmentation. The fullsize simulation is visualized in Fig. 13.
4) Quantitative Result and Discussion We compare CGO and MTObject/M-MTObject  [12]  on the registered KiDS-HST images. As shown in Fig. 16 , CGO achieves better F1-scores than MTObject/M-MTObject on this real dataset. Similarly to the previous simulation setting, M-MTObject on multi-band images is again not better than MTObject on single-band images, i. e. , adding multi-band information confuses the object detector in the case of MT-Object. These results are consistent with the experiment performed in the FDS Simulation and with visual assessments. Note that all the F1-scores on the KiDS-HST experiment are much lower than the F1-scores in the FDS Simulation test. This is due to HST images (reference images) being much deeper than KIDS images, i. e. , more objects are visible in the HST images, therefore many objects in the reference images are simply undetectable on the KiDS images.
Introduction and Highlights High Energy Physics (HEP) experiments' open-access data, like the multiple datasets hosted in the CERN Open Data portal  [1] , tend to be used as inputs in university classrooms and laboratories. Those samples help enhance curricula and teach cutting-edge research and techniques not present yet in the literature, making it a powerful tool for training and integrating new researchers and technicians into a particular scientific endeavour in this field. In the case of ATLAS  [2] , the usage of Open Data for education came from the use of event data by the International Particle Physics Outreach Group (IPPOG) Master Classes  [3]  and satellite events  [4] , and the dedicated project called ATLAS Open Data  [5]. The latter is devoted to releasing sample datasets of data recorded by the ATLAS experiment and simulated data.
Overview The ATLAS Open Data project  [5]  is an effort of the ATLAS Collaboration dedicated to designing a holistic educational programme in HEP for undergraduate and master students all over the world. This effort involves the design, production, testing, validation and analysis of real and simulated Monte Carlo datasets that will be released to the public following the ATLAS Policy on Open Access with focus on educational and training objectives  [10, 11]. Dedicated documentation, software tools, executable analysis examples and web-based applications are produced and delivered alongside the datasets.
Usage and Users The ATLAS Open Data datasets and tools are already -and were for some time -used by a set of universities and dedicated academic or outreach projects  [6, [12] [13] [14]  with the aim to enhance their educational programmes in experimental particle physics, data analysis, computer sciences and statistics. In several cases, ATLAS Open Data has been used to develop, prepare and defend undergraduate and master theses in several universities worldwide  [15] [16] [17]. The traditional way that those resources are reached by the user -like a student -is using a local computer element (e. g. a personal computer or university laboratory computer) with internet access to download the tools. Tools may include e. g. a Virtual Machine (VM), some of the analysis frameworks and a collection of Jupyter notebooks  [18].
Also, the user consumes the content online, such as the documentation, video-tutorials and web-based applications. In terms of the datasets, they can be downloaded or be accessible in streaming: reading the ROOT  [19]  files over the internet as the analysis code runs over those files. Figure  3  exemplifies a common user computing environment where a VM is used as an internal-and-personal server, allowing to run Jupyter notebooks, ROOT and other software directly in the host browser while accessing others resources on the ATLAS Open Data website, web-based apps and dedicated documentation  [20]. Notice that after the download of all the necessary datasets and resources, the user will be able to run and further develop the exercises without an internet connection (See Figure  4 ).
Institutional Infrastructure Many (if not most) of the ATLAS Open Data users -students, professors, trainers -use such resources in institutional facilities: university computer labs, libraries and training centres equipped with the necessary infrastructure to execute a workshop, a masterclass or a complete academic period. Still, the current ATLAS Open Data tools are designed considering users that will use their computers, e. g. students using their personal laptops, at home or at school. Over the last five years, such individual and institutional experiences helped identify ideas and technologies for designing and creating protocols to deploy and use the ATLAS Open Data resources using Infrastructure as a Service (IaaS) and Infrastructure as Code (IaC) in small and medium-size academic institutions.
For example, a popular way to use the ATLAS Open Data resources in workshops and classes is with the usage of a service called MyBinder  [21]. This cloud computing service allows users to get a dedicated computer element in a cloud that -for free and anonymously -can be used to run an instance of Jupyter, and other software, defined in a Docker  [22]  container  [23]  for up to 12 hours. Figure  5  represents the way that a user uses MyBinder in combination with the ATLAS Open Data web-based resources to access, compute and modify the analysis examples. A web browser is the only application needed, something that is very attractive due to the simplicity and the minimal friction regarding local setups. A similar approach can and has been implemented using commercial cloud services. Individuals and small groups in universities have been looking for ways to implement Software as a Service (SaaS) to their trainees and students so that they can access Open Source resources and perform a lab course or a workshop.
ATLAS Open Data is now deployed in multiple university infrastructures  [13, 14, 24]  -on-premises and commercial-by the instructors and the local system administrators, or external collaborators so that the users, the students, can run exercises and conduct a practical course without the hassle of installing dedicated software locally. This brings an exciting and heterogeneous scenery where multiple users have multiple setups. [25]  and ICTP  [26] , or commercial clouds rented for a specific time. This SaaS and IaaS ecosystem is a fortunate sub-product of the multiple usage of Open Data for education, but it produces a "re-invention" of almost the same solution every time an individual or institution wants to deploy such resources to their students and instructors. On the other side of the road are those individuals and small-size institutions -usually with limited SysAdmins expertise or assistance -that would like to deploy an educational analysis suite: Open Source and Open Data resources in the form of JupyterHub [27] (and its evolution as JupyterLab [28]) environments.
Here is where our next target audience resides: educators and local resource providers who will help set up, maintain and run activities using the ATLAS (and others) Open Data and Open Source software analysis tools, profiting from a SaaS suite deployed as IaaS/IaC on-premises or commercial cloud services. The next section explores a series of proposals to enhance the current ATLAS Open Data model, from using a single-client-based approach (Figure  6 ) to a multi-user approach. The plan is to develop and deliver a series of custom and reproducible instructions and scripts that allow the deployment -as easy as possible -of VMs and containers used to implement IaaS and SaaS in small and medium-size institutions. As the usage of CERN Open Data  [1]  expands into different educational and training programmes  [29, 30] , more and more individuals and institutions want to set up a multi-user infrastructure that allows a continuous or on-demand running of experimental HEP courses and related computer and data sciences, while minimising the friction relative to setup and installation of such services by the users -students and professors -and the universities.
ATLAS Open Data VMs and containers The design of possible solutions has started, and a first approach has been implemented at CERN: the use of IaC under the Open Source software Terraform [32] and the CERN Open-Stack [33] internal infrastructure during a winter student project in 2020. In this case, a series of Terraform configuration files are able to automatically create and configure an OpenStack instance, or VM, with all the needed elements to execute the ATLAS Open Data resources -a Ubuntu OS-based VM, plus the sequential installation and configuration of Python3, ROOT, JupyterHub and other Linux-related libraries. The final setup looks very similar to the one described in Figure  7 , where a VM is used to deploy SaaS, making it possible to run a complete workshop or training sessions for users at CERN.
The cloud "Institutional resources" provide the SaaS, computing power, permanent storage and authentication methods so the students can use the mentioned educational resources. In such a model, it is expected that the datasets are also stored on-premises for faster access, and also to reduce the consumption of bandwidth by individual users, and avoid possible security threats coming from the exposure to the internet. Figure  9  shows a similar multi-user architecture as in Figure  8 , but, the users' computing elements, like the computers in a classroom, are connected to an external (e. g. commercial) cloud computing infrastructure. The cloud provides the SaaS, the educational resources and datasets, computing power, permanent storage and authentication methods. In this architecture, the institution or individuals use the flexibility to rent the computational resources only when needed.
Summary Thanks to several Open Data and Open Source projects, multiple physics and computer sciences educational programmes worldwide have been implementing -or enhancing -their curricula to include hands-on sessions and long-term projects in experimental data analysis. This paper describes how resources, like the ATLAS Open Data project, can be deployed in a multi-user approach, taking advantage of current and widely available software and computing tools. This proposal intends to deliver a series of IaC recipes and containers that can be used by educators and IT service providers in small and medium-size institutions to deploy IaaS and SaaS on-premises or on commercial clouds. A first proof-of-concept was performed using Terraform and the CERN OpenStack internal cloud service. The current activities aim to design, create, test and adequately deliver IaC and IaaS/SaaS recipes using VMs, containers, and ATLAS know-how to cover several multi-user configuration scenarios.
For model testing and validation, we conducted a survey (N = 2,398) and gathered user insights from 35 Western European countries, with a special focus on Germany. As  Kondylakis et al. (2020)  criticize in their work, recent research projects in this field involve mostly younger people (students), although the epidemiological impact of older people seems to be more significant. We were able to include this point into our data collection procedure by recruiting a wide range of age categories. The remainder of this paper is organized as follows. We first provide background information on digital pandemic instruments and related papers. From this information, we derive our research hypotheses and build our research model (section 3). Next, our measurement model and the data gathering approach itself are presented (section 4), followed by the survey results and the computed PLS-SEM model in section 5.
, 2020). While most countries performed a COVID-19 risk assessment, the above-described data exchange with COVID-19 laboratories and a derived warning for positive cases is another scenario. Another feature could be a manual tracking of COVID-19 symptoms by the user itself. In less privacyfriendly countries, quarantine-monitoring (geofencing) by government organizations emerged, as did as a warning system when approaching a COVID-19-infected person  (Ahmad et al. , 2020). The German tracing app (Corona-Warn-App) approach is an implementation of the exposure framework by Google and Apple, however it represents a public money and public code approach, that is publicly documented on GitHub. Compared to other papers, this study involves existing COVID-19 tracing applications and we combine the sample and the model itself with qualitative insights, like the meta-model in Figure  1.
Data Collection To gather as many participants as possible, we translated the survey with the help of native speakers into six languages (German, English, French, Spanish, Korean, and Brazilian Portuguese). We created social media templates for interested participants, the press, and our own accounts, which helped us to reach out to various local radio stations, newspapers, and magazines, asking the public for help and participation in our survey. Overall, we ended the data collection in mid-July 2020. In total, we were able to reach more than 3,500 people, of which 2,682 completed the questionnaire. Prior to interpreting the data, we conducted two data-cleansing steps. The final number was 2,398 respondents. First, we deleted all uncompleted answers (929). Second, we calculated the average time participants needed to fulfill our survey and defined a minimal processing time (4 minutes).
Proposition 5: Improve Acceptance Through an Open-Source Approach Open-source creates unanimous security as it allows anyone (such as privacy advocates as well as NGOs and security professionals) to view the code  (Cho et al. , 2020). In this way, data security concerns can be lowered because the application transmission data is transparent  (Bock et al. , 2020). However,  Dehmel et al. (2020)  noted that besides the initial publication of the code, a management concept is required in reaction to feedback and experiences in the field (learning 1). SAP, the developer of the COVID-19 tracing app in Germany, considers this the right approach to increase feelings of transparency and trust. This is shown with the GitHub repository, which made it not only possible to review the entire interaction concept up-front, but simultaneously to provide feedback and direct communication with the development team  (Mueller, 2020).
The diversification of modern birds has been shaped by a number of radiations. Rapid diversification events make reconstructing the evolutionary relationships among taxa challenging due to the convoluted effects of incomplete lineage sorting (ILS) and introgression. Phylogenomic datasets have the potential to detect patterns of phylogenetic incongruence, and to address their causes. However, the footprints of ILS and introgression on sequence data can vary between different phylogenomic markers at different phylogenetic scales depending on factors such as their evolutionary rates or their selection pressures. We show that combining phylogenomic markers that evolve at different rates, such as paired-end double-digest restriction site-associated DNA (PE-ddRAD) and ultraconserved elements (UCEs), allows a comprehensive exploration of the causes of phylogenetic discordance associated with short internodes at different timescales.
We used thousands of UCE and PE-ddRAD markers to produce the first well-resolved phylogeny of shearwaters, a group of medium-sized pelagic seabirds that are among the most phylogenetically controversial and endangered bird groups. We found that phylogenomic conflict was mainly derived from high levels of ILS due to rapid speciation events. We also documented a case of introgression, despite the high philopatry of shearwaters to their breeding sites, which typically limits gene flow. We integrated state-of-the-art concatenated and coalescent-based approaches to expand on previous comparisons of UCE and RAD-Seq datasets for phylogenetics, divergence time.