dataesr/scientific-paragraphs-categorization huggingface dataset:
In a mixed model setting, such likelihood ratio test have been shown to be anticonservative (see, e. g. , Section 2. 4 in  Pinheiro and Bates 2006) , and can hence lead to many false discoveries. We applied the limma cor method on the log 2 TPM values, that performed best on the realistic simulations above, to the same dataset, and compared the of differentially expressed genes to the one found in  Stern and Crandall (2018a). We also applied the phylolm OU method, that is the most similar to the OUwie method, for comparison.
Acknowledgments P. B. and M. G are grateful to  Sylvain Merlot  for initiating this project, to  Christine Drevet ,  Marie-Laure Martin  and  Guillem Rigaill  for useful discussions, and to  Claire Ducos ,  Marie Michel , and  Sarah Jelassi  for their work during their master internship. This work was partly funded by the  I2BC  and the  MI CNRS  through the  MODELCOG  (M. G. ) and X-TrEM projects ( Sylvain Merlot ). We are grateful to the  INRAE MIGALE bioinformatics facility  (MIGALE, INRAE, 2020. Migale bioinformatics Facility, doi: 10. 15454/ 1. 5572390655343293E12) for providing computing and storage resources.
Data Availability The simulation tool is integrated into the compcodeR package, that is freely available on the Bioconductor platform, and documented through a specific vignette (https://doi. org/10. 18129/B9. bioc. compcodeR). The data and code used for the simulation study are available on the following GitHub repository: https://github. com/ i2bc/InterspeciesDE, and was deposited on Zenodo: www. doi. org/10. 5281/zenodo. 7311523.
Open online communities provide unique ecological opportunities to study social dynamics at multiple levels (e. g. ,  Hill & Shaw, 2019; Schroeder & Taylor, 2015). Because these communities collaborate primarily through GitHub (although not exclusively; cf. Yasir, Michael, Savarimuthu, & Licorish, 2018) , this study allows us to examine a nearly complete historical record of a community's interactions. As such, we are able to test predictions of community-specific language structures in a naturalistic setting  (R ączaszek-Leonardi & Kelso, 2008).
Ethnographic and qualitative studies of software engineers Ethnographers have qualitatively studied software development and FOSS communities for decades  (Coleman & Hill, 2005; Kelty, 2005). We turn our attention to a more recent development: the dominance of GitHub, a "social coding" platform that facilitates programming and coordination. Previous qualitative and ethnographic work has conducted deep dives into the motivations and experiences both of software developers generally and of software developers working specifically on this platform. We review these findings here as a basis for understanding the motivation and culture of FOSS communities broadly.
An overview of GitHub The present work analyzes community data from GitHub (http://www. github. com), an online platform designed to facilitate code exchange, code sharing, and collaborative code development. Each software project on GitHub has its own repository (or repo), which contains the full history of all files in a directory structure, with all changes tracked and indexed. While authorized members of a repository are (by default) able to immediately merge (or update the repository's official files) a commit (or a proposed change to something in the repository) to include their code changes, all other GitHub users can make a pull request (or PR), asking for their proposed code changes to be merged into the repository. Every new PR creates an open discussion thread. Many projects-especially larger ones-require all PRs to be reviewed before being merged.
Issues and PRs share similar structures. Each is an ordered set of comments, headed by the text of the issue or PR. The web interface orders comments reverse chronologically and displays the commenter's username, profile image, time of the comment, and whether they are a project member or (starting in July 2017) a first-time contributor to the repo. We chose GitHub because its integrated communication and code development gives us a uniquely expansive view of the lifecycle of open-source software. While not every single act of communication in a FOSS project hosted on GitHub takes place exclusively through GitHub, many projects have strong norms or policies encouraging as much communication to take place on GitHub as possible. Although projects often specify unique expectations about communication for their members and newcomers, the issue tracker and PRs are the default spaces for members and non-members to raise issues and discuss changes.
Therefore, patterns should vary by the community  (Port, 2010; Thorne et al. , 2009)  and over time (i. e. , as members join and leave;  Demjén, 2018; R ączaszek-Leonardi & Kelso, 2008). We analyze naturally occurring data or "trace data" (i. e. , data left behind as people interact with the physical or digital world) in eight FOSS communities as they developed pivotal software projects on GitHub. We selected this "purposeful sample"  (Palinkas et al. , 2015)  of projects based on the following criteria: All develop widely relied-upon digital infrastructure, primarily use GitHub issues and pull requests to collaborate, have existed for at least 3 years, have over 50 contributors with more than five contributions each, and have over 100 contributors total. However, projects have a wide variation in terms of their longevity (founded in 2001 to 2014) and community size (number of members from 90 to 6,806).
Corpus and data preparation For the present study, we analyzed posts (i. e. , original issues and original PRs) and comments (i. e. , comments on issues and comments on pull requests)-together denoted as "activities" moving forward-in eight projects on GitHub: matplotlib, mayavi, numpy, pandas, scikit-image, scikit-learn, scipy, and sphinx-gallery. All projects rely on the Python programming language and emerged from the scientific Python community. These projects provide support for a number of vital scientific activities-from documentation to machine learning-and are accordingly essential both to specific scientific projects and to other Python packages. For summary statistics on these projects, see Supplementary Table  7  in the Supporting Information. Data were collected from GitHub's API using watchtower, a Python library.
We assembled a dataset of the following for each activity: unique activity identifier, author name, author's membership status in the community at the time of activity, activity type (i. e. , original issue, original PR, comment on issue, comment on PR), associated original activity identifier (for comments), text body, date created, date edited (if applicable), and date resolved. We Fig. 1. Diagram of data collection, feature extraction, and analysis workflow for the current project. Records of activity were downloaded from GitHub for eight free and open-source software projects. We extracted a series of features from each activity record, including basic metadata (i. e. , the date it was created, the user who created it), communication context (i. e. , whether someone was posting a PR, commenting on a PR, posting an issue, or commenting on an issue), community metrics (i.
e. , the project to which it belonged, whether the user was a member of the community or not), and language analysis (i. e. , extracting a sentiment score and a count of gratitude words). Data were then subjected to a series of analyses, including comparing the overall behavior of groups, tracking of communities' language use over time, and quantifying factors related to newcomer retention. downloaded all records created and updated before January 1, 2019. The dataset comprises 19,430 unique users; 36,538 posted pull requests; 253,495 comments on PRs; 32,133 posted issues; and 185,529 comments on issues. Because the data are naturally occurring corpus data, we needed to extensively clean them before moving forward with analyses. We filtered out all activities associated with bots to ensure that our analyses only targeted actual community members.
For each activity, we first removed any text not viewed by the user; projects often use templates that include hidden HTML elements in the body of the activity that is often automatically generated by the template. We then annotated each activity with positive, negative, and global sentiment scores using vaderSentiment  (Hutto & Gilbert, 2014) , a sentiment analysis tool that is designed to capture affective features of language in online contexts. Importantly, it captures both standard affect words and novel affect features of computer-mediated conversation (e. g. , UTF-8 encoded emojis, punctuation, capitalization). Each activity was also annotated with the number of words associated with gratitude, compiled with traditional words of gratitude and online expressions of gratitude  (i. e. , appreciate, appreciated, appreciative, grateful, gratitude, indebted, obliged, thank, thanks, thankful, thanx, thx, tks).
Contribution-specific responses to newcomers shape the likelihood of returning In our final set of analyses, we investigated the impact of language dynamics and community responsiveness on the likelihood that a first-time contributor will come back to post a second time (see Fig. 5  and Supplementary Table  5  in the Supporting Information). The modal outcome, of course, is that newcomers do not come back: Only 35. 01% of first-time contributors return to make a second contribution. However, this analysis seeks to understand how the community's response to newcomers could make them more or less likely to return. Although these communities have other interactions outside of GitHub, their responses to newcomers will often be the only point of contact-and, therefore, potential recruitment or distancing-that these newcomers have with the community.
Generally, newcomers whose first post was an issue were significantly less likely to return, while newcomers whose first post was a PR were significantly more likely to return. Importantly, the majority of newcomers (70. 50%) in our dataset contributed an issue. The structure and content of comments on newcomers' posts were also influential: More positive, more grateful, more commented upon, and less member-heavy comment threads were more predictive of newcomers returning. Neither open time nor maximum negative sentiment significantly predicted retention. As with previous models, these effects were modulated by interactions with post type. Two factors-more comments and less member-heavy comment threads-were both predictive for issues and PRs, but the remaining effects differed between post types.
Implications for cognitive science Although we specifically examined FOSS communities as our sample of interest, the current focused on these communities as an opportunity to test the dynamical systems view of language and interaction in a real-world setting and within a naturally occurring dataset  (Paxton et al. , 2016; R ączaszek-Leonardi & Kelso, 2008). We hypothesized that the goals of the interaction, the role of the individual within the community, and the culture of the community itself would uniquely shape language use. In other words, we sought out signatures of top-down and bottom-up social and cognitive pressures on linguistic behaviors. Consistent with these hypotheses, we found signatures of time-, context-, and goal-varying behaviors. Not only did a person's status within a community and their goals change their eventual linguistic behaviors, but those behaviors varied across communities and as those communities developed.
Psychologists and cognitive scientists have increasingly looked to naturally occurring data as an opportunity to bring theories out of the lab  (Goldstone & Lupyan, 2016). Our work demonstrates how this approach can be deployed not only to expand the development of basic science-by testing a specific theory (here, DST) within a specific set of behaviors (here, e. g. , engagement, community development, social-emotional dynamics)-but also to simultaneously give back to communities under examination (here, FOSS contributors).
Conclusion FOSS has proliferated as the critical international infrastructure, and their reliance on largely volunteer contributors-one of their key strengths-makes the retention of community members vital to their existence. Building on prior work in ethnography and psychology by analyzing community dynamics of FOSS communities on GitHub, we here demonstrated that communication context (i. e. , the goals and setting in which a communicative act occurs) and community membership (i. e. , being a member or a non-member of a particular community) shapes the way that people use emotion language and expressions of gratitude. At the same time, FOSS communities exhibited their own dynamics, suggesting that these language dynamics emerge from a fundamental connection to a specific community rather than a common reflection of all FOSS work.
Acknowledgments Our thanks go to  Lilly Irani (University of California, San Diego), Dorothy Howard (University of California, San Diego ), and  A. P. Roxy. This work was supported by the  Alfred P. Sloan Foundation  and the  Ford Foundation  as part of the  Digital Infrastructure Research Grants Initiative  (Grant  G-2018-11354 ) [all authors]. This work was also supported by the  Gordon and Betty Moore Foundation  (grant  GBMF3834 ) and  Alfred P. Sloan Foundation  (grant  2013-10-27 ) as part of the  Moore-Sloan Data Science Environments grant  to the  University of California, Berkeley  [in the form of fellowships to all authors].
Most courses were short training events with a median of 2 da ys , but some ran for multiple months like a number of high school or university courses that used TIaaS over the entire semester. The variability in administrator deployments of TIaaS can allow it to accommodate a wide range of teaching scenarios; for some courses, large  resources may be allocated like the Galaxy Community Conferences, where the big 3 Galaxies configured TIaaS with considerable resources to permit local and r emote sync hr onous tr aining, all the way to semester-long courses, which may not necessitate a large allocation. TIaaS has been successfully scaled to extremely large and highl y geogr a phicall y distributed e v ents. T he GTÑ project successfully used it for a Spanish-language bioinformatics course spanning the Americas and Europe  [ 21 ] , while the 2 Smörgåsbord e v ents used TIaaS for a weeklong, global, async hr onous course with trainees across 111 countries  [ 22 ].
In a hackathon environment, TIaaS has allo w ed large dataset (single-cell RNA sequencing) manipulation within group projects in remote courses with up to 30 participants performing unique analyses  [ 23 ]. It has successfully supported an introduction to a bioinformatics course at a r emote-learning, entr ance exam-fr ee alternative education institution (The Open University), as well as industry courses, allowing them to test out Galaxy as a collabora- tive working environment before making decisions on consortium platform use.
Implementation TIaaS was written in Python with the Django fr ame work  [ 24 ]. It has been designed from the start to have a very limited scope: provide a form to register events, an approval flow for administr ators, mana gement of user groups and roles in the Galaxy database, and the status dashboard. Service metrics are exposed via a Prometheus  [ 25 ]  endpoint at /tiaas/metrics  [ 26 ] , for visualization and alerting. Instructors can visit ( /tiaas/new ) to register a new event and r equest r esources. When submitted, this form is stored in the associated database, and administrators can view the requested training events and approve or reject them using the built-in Django admin interface. When users visit their training URL ( /join-training/ < id > ), the system accesses their Galaxy session cookie, which is present as TIaaS is deplo y ed at a path below Galaxy, and decodes it turning it into a Galaxy identity.
Flexible deployments As the Galaxy community has largely settled on Ansible for deployment of Galaxy and related components, an Ansible role was produced for deploying the TIaaS service. A few kno wn deplo yments make their configuration public, and as such, we can see what c hoices eac h administr ator made. One of the motivating factors in TIaaS's design was such flexibility, and this advantage is seen dir ectl y in those deployments. Galaxy Europe uses it with HTCondor and job rules that allow spillover to the main cluster; new machines are brought up in an OpenStack cluster specifically for training events and destro y ed afterw ar d. Eac h mac hine is ta gged with an HTCondor attribute indicating whic h tr aining it belongs to, and the job rules (visible in https:// github. com/usegalaxy-eu/ infr astr ucture-playbook/ pull/447/files ) use that to enable access to those machines and a pr efer ence for them.
Galaxy Australia has a separate "training cluster" in their Open-Stack deployment and routes all training jobs to the single shared cluster (visible in https:// github. com/usegalaxy-au/ infr astr uc tur e/tr ee/57cd80030d72929c263955e895079d6ac25aa24f /f iles/g alaxy/dynamic _ job _ rules/production/total _ perspective _ vortex ; note the training role and destinations tagged training). Galaxy US takes a different approach, lacking additional clusters but having an efficient queuing system that can pr operl y pac k jobs based on walltimes; they instead artificially limit the runtime, memory, and CPU resources allocated to users running jobs within a TIaaS group. Av ans Hogesc hool uses TIaaS in an internal Galaxy where they provide no preferential treatment and just use the dashboard to follow students' pr ogr ess (visible at https:// github.
Availability of Source Code and Requirements r Project name: Training Infrastructure as a Service r SCR_023200 r bio. tools ID: tiaas r Pr oject homepa ge: https://github. com/galaxypr oject/tiaas2/ r Admin training manual: https://gxy. io/GTN:T00022 r Teac her tr aining manual: https://gxy. io/GTN:T00286.
Funding The work is in part funded by  Collabor ativ e Researc h Centre 992 Medical Epigenetics  ( Deutsche Forschungsgemeinsc haft (DFG)  gr ant  SFB 992/1 2012 ),  Bundesministerium für Bildung und Forsc  hung ( BMBF  gr ants  031 A538A/A538C RBC  and  031L0101B/031L0101C de. NBI-epi, doi: 10. 13039/501100002347 ), and the  National Institutes of Health (National Human Genome Research Institute ; grant  2U24HG006620 , doi: 10. 13039/100000051 ). De v elopment work was funded with the support of the Er asm us + pr ogr am of the  European Union  (grant  2020-1-NL01-KA203-064717 , doi: 10. 13039/100001501 ). This project has received funding from the  EC H2020  (doi: 10. 13039/100010676 ) project  CINECA  (doi: 10. 3030/825775 ).
Introduction: The conect4children (c4c) consortium was setup to facilitate the development of new drugs and therapies for paediatric populations and address key challenges associated with paediatric clinical trials. Two of the major adopting principles for c4c were academia-industry partnership and data harmonisation and interoperability through common eCRF definitions. To understand the challenges arising out of these principles, the c4c team at Newcastle University conducted semi-structured interviews with four c4c industry partners. Methods: Each partner was asked 10 questions about the data standards used in their company, management and maintenance of data dictionaries, how they dealt with paediatric-specific issues, major knowledge gaps and how academia could aid in bridging these gaps.
This in turn will facilitate collaborative research across institutions by bringing together the data from different sources. Pooling together multi-institutional data is particularly important for rare disease, where each individual institution's data may lack statistical power for any meaningful analysis. It is imperative that data of benefit for research in rare and paediatric conditions is collected and standardized in a manner which allows for secondary use, wherever possible, to support further research in the future. An important tool to standardize and harmonize data is a data dictionary. While numerous data dictionaries have been developed at institutional levels, there has been a push to develop common data standards at higher levels. Two such standards are the Common Data Model (CDM)  20  by Observational Health Data Sciences and Informatics (OHDSI) and the Operational Data Model (ODM)  21  by Clinical Data Interchange Standards Consortium (CDISC).
The CDM was originally developed by the Observational Medical Outcomes Partnership (OMOP) and carried forward by OHDSI. The CDM facilitates analysis of different databases by transforming data into a common format and representation, and then performing analyses using routines based on the common format. The ODM, which is independent of vendors and platforms, is used for the exchange and storage of clinical and translational research data, along with the associated metadata. The CDISC ODM has become the preferred standard for representing CRFs content in many electronic data capture tools. 3] [24]  Despite such large data models being available, concerns remain whether they capture the paediatric terminologies sufficiently. 25, 26  address these issues the c4c team at Newcastle University conducted a series of interviews with representatives of c4c industry partners.
Each interview started with a presentation (by RL) about the c4c data tasks, to provide context. One member of the Newcastle University team (either AP or JL) then asked the questions. If further information or clarifications were required, additional questions could be asked by all members of the team. Each call was recorded, and notes were taken (either by AP or JL). These notes were formalised into reports after the interviews. During the interview, if the company representative(s) was unable to provide an answer, they were allowed to get back to the NU team later via email. While no explicit instruction was provided for answering the question in a global context, given the pan-European nature of c4c, it would be safe to assume the answers would at least apply to all of Europe. All company representatives were notified that in case of any publication arising from the interview, they would have the opportunity to provide or decline consent, and to review the manuscript.
| Data analysis Question-wise thematic analysis was performed on the interview reports (by AS) using the methodology described by Braun and Clarke. 27 Questions 9 and 10 were combined for the analysis as they dealt with similar themes. The reports were read several times for familiarisation. Following this, the initial annotations (or codes) were generated. An annotation is a basic unit from the answer text that can be meaningfully analysed and is relevant to the corresponding question. They could be words, phrases or even sentences. There was no limit on the number of annotations per answer. For follow-up questions, the same annotation appearing in both the question and answer was annotated once. These annotations were then grouped into themes based on their conceptual similarity. The themes were reviewed to ensure all annotations within a theme were appropriate, no new themes could emerge from the answers and patterns were then identified.
When discussing development of advanced tools, Company D suggested that a complex system is not needed to manage data standards and an Excel spreadsheet is often sufficient. Though only companies B and D explicitly mentioned the use of CDISC terminology in the answer to Q1, subsequent answers confirmed all four companies were using the CDISC suite of standards. Three of the companies (A, B and C) use SAS (Statistical Analysis System-a statistical software) in addition to Excel for modelling and analysis. No answer could be provided. No answer could be provided. T A B L E 1 Summary of the responses from the four companies to the 10 questions about data standards used with their institutions Adherence to CDISC standards and dealing with data items not available in CDSICs were themes that occurred in multiple questions.