dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion Surgical volume is associated with a better outcome after a hip arthroplasty within 90 days and 1 year, with no effect demonstrated on hospital readmission. Such results are consistent even after adjustment for distance and time of travel, but the persistence of exogenous factors not documented in the French databases suggests that regionalization of hip arthroplasty should be organized with caution. Quantity should not be opposed with quality. Such facility reorganization should be investigated with more precisions.
Quantification and statistical analysis Analysis of obtained currents in whole-cell recordings was performed using ClampFit. Data were treated and analyzed using Excel, SigmaPlot 11. 0, GraphPad Prism 8, and R according to their probability distributions. QuasiBinomial GLM was used for current inhibitions while Poisson and QuasiPoisson GLMs were used for Omega turn and nocifensive behavior counting. Neuronal excitability-, photocurrent-, current density-, paw withdrawal latency, and paw withdrawal thresholdrelated data were assessed to verify the assumption of Normality either with the Shapiro test or with a Q-Q plot. Data verifying Normality were analyzed using either paired t-test when comparing two paired conditions or two-way ANOVA RM and mixed-effects model RM when comparing more than two paired conditions; otherwise, data were analyzed using Wilcoxon signed-rank test.
Acknowledgements We thank  Pr. Daniel L. Minor  for providing us with the selective activator of TREK channels  ML67-33  and  Dr. Christian Braendle  for providing us with C. elegans. This work was supported by a grant to G. S. by the  Fondation pour la Recherche Medicale  (Equipe labellisée FRM 2017,  FRM DEQ20170336753 ) the  Agence Nationale pour la Recherche  ( AT2R-TRAAK-Bioanalgesics ANR-17-CE18-0001  and  Viral_Rhodopsins ,  ANR-19-CE11-0026-01 ), the  Laboratory of Excellence "Ion Channel Science and Therapeutics"  (grant  ANR-11-LABX-0015-01 ), and the  French government , through the  UCAJEDI Investments  in the  Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-15-IDEX-01 , by grant to F. L. (Equipe labellisée FRM 2020, FRM  EQU202003010587  to X.
Simulation model The case study presented in Fig. 1  was modelled in Matlab/Simulink -Simscape Electrical as shown in Fig. 7. The model is composed of a 20 MW AC-DC converter station (including two cascaded NPC converters, 10 MW each), and three DC stations (two of them rated at 200 kW and the third one at 19. 6 MW). The grounding is realized on the DC side, at the connection of two NPC converters. The value of the grounding resistance R n is modified to investigate the influence on the protection  The two DC stations of 200 kW closer to the AC-DC converter are modelled using an average model  [49]. In this model, a controlled voltage source controls the current being exchanged with the MVDC network. A combination of an ideal switch and a freewheeling diode allows to model the converter behavior during blocked state.
Background: Data management is fast becoming an essential part of scientific practice, driven by open science and FAIR (findable, accessible, interoperable, and reusable) data sharing requirements. Whilst data management plans (DMPs) are clear to data management experts and data stewards, understandings of their purpose and creation are often obscure to the producers of the data, which in academic environments are often PhD students. Methods: Within the RNAct EU Horizon 2020 ITN project, we engaged the 10 RNAct early-stage researchers (ESRs) in a training project aimed at formulating a DMP. To do so, we used the Data Stewardship Wizard (DSW) framework and modified the existing Life Sciences Knowledge Model into a simplified version aimed at training young scientists, with computational or experimental backgrounds, in core data management principles.
Introduction During the last decade, Open Science practices have become mainstream, with Open Access publications now common  (Mills, 2020) , and data collection and sharing under the FAIR (findable, accessible, interoperable, and reusable) principles  (Jacobsen et al. , 2020)  strongly encouraged by many research funding agencies 1. Nowadays, for most research projects, a data management plan 2 (DMP) is required as a formal document that outlines how to handle research data both during research and after the research project is completed. The DMP essentially documents key activities in the research data lifecycle, such as the collection, description, preservation, and access or discovery of data. In brief, it should specify the services and legal support that the project needs to make the data as FAIR and open as possible.
Such documentation is crucial for the reproducibility of research results, which is a fundamental precept of scientific investigations. International organisations such as the Research Data Alliance (RDA) host working groups which are trying to define DMP standards, while research funding agencies such as Horizon Europe propose template documents for DMPs. However, researchers that produce data need tools to help them create a DMP, and there are still only a limited number of online tools for filling in a DMP questionnaire, probably due to the lack of standardization. The Opidor DMP tool and the ELIXIR Data Stewardship Wizard are good examples of frameworks that facilitate DMP production. Nevertheless, the collection of data in a way that enables FAIR data sharing is not trivial, and requires some knowledge of underlying principles of data annotation (metadata) and the overall ways in which data can be organized  (formats, storage, etc.
). Increasing efforts at academic and research institutions, mainly by employing data stewards to help the producers of the data, as well as upcoming changes in evaluation practices, are enabling FAIR data sharing, but there are still many hurdles present. For example, whilst data stewards can educate scientists in data management principles and help them, the scientist themselves have to understand the data management terminology and aims to a certain extent, so that they can reliably collect and organize relevant data, while remaining motivated to do so. This active participation of scientists is especially relevant as data (storage) is often very domain specific, making it impossible for data stewards to understand all the subtleties and prior practices of each field. Our goal here is to describe the experience of learning about data management by  Early-Stage Researchers (ESRs)  in the frame of the RNAct Marie Sklodowska Curie European innovative training network (ITN)  (Gownaris et al.
, 2022). The RNAct ITN (https://rnact. eu/) started in 2018 and employed 10 ESRs in research with the main goal being the re-design of RNA Recognition Motif (RRM) protein domains. By investigating how these RRMs bind RNA molecules, through structural biology and bioinformatics approaches, their application in biosensor development and synthetic biology was envisaged. The project employed a mix of synergistic computational and experimental approaches, in a 50/50 ratio, and so encompassed two very different audiences in terms of how data is managed. To address this as part of the project, a data management subcommittee consisting of the principal investigators responsible for RNAct data management (DM PIs; Drs. Devignes, Smaïl-Tabbone, Chauvot de Beauchêne and Vranken) was initiated at the start of the project, with regular meetings held.
This subcommittee trained the 10 ESRs on basic and practical data management principles, with the main goal the creation of individual Data Management Plans (DMPs). This process highlighted difficulties in explaining data management: what it means, which resources are available, how related it is to the publication of FAIR data, etc. A key difficulty for our ESRs was, for example, how to describe the data they were producing during their PhD thesis work. Explaining this required an iterative process of providing information about data management, while asking them for relevant information about their own datasets. During this process, we developed a 'simple' version of the generic Life Sciences Data Stewardship Wizard (DSW) Knowledge Model  (Pergl et al. , 2019). This simplified model is aimed at training young scientists, both with computational and experimental backgrounds, in core data management principles.
It is organized by chapters, which capture aspects of data management (e. g. Administrative information). Each chapter has sections, which are more specific (e. g. contributors to a DMP) and which contain questions to collect information (e. g. name of a contributor). The questions are tagged depending on the timeline in a data/project lifecycle (e. g. start of the project), and depending on the impact of answers on the compliance with respect to FAIR principles (e. g. answering Yes to the question "Will you describe your data with standard vocabularies or ontologies?" will result in a green FAIR tag). A knowledge model can be instantiated as a DMP project through a user-friendly questionnaire interface. Moreover, one can save a pre-filled version of a project as a template, when several DMP projects have to be produced with shared information (e.
Experimental design The experimental design was based on an iterative process, with task sharing between ESRs and DM PIs as data management supervisors (Figure  3 ). All ESRs had to select among all the data they produced one relevant dataset for which they had to produce a DMP project. Starting from the default Life Science DSW knowledge model (version 2. 4. 0), we performed two iterations of a process composed of four steps: (i) creating or updating the RNAct DSW knowledge model, (ii) having the ESRs create their own DMPs, (iii) reviewing the DMPs and (iv) collecting feedback from the ESRs. The first round of this process was interactively performed at a hybrid session during an RNAct workshop (3 rd June 2021, Brussels), with 7 ESRs physically present and 3 present online. For this session, version 1.
0. 3 of the RNAct_ ESRTraining_KM DMP questionnaire was used (see  Vranken et al. , 2023). The following steps were then taken: 1. Presentation of general concepts around data management (available at  Vranken et al. , 2023) , including an introduction of the DSW and an overview of the DMP questionnaire. 2. The ESRs selected the dataset(s) that they wanted to create a DMP for, with diverse computational and experimental topics: protein domain structure data; binding and RNA/protein interface data; cell cultures and other data in relation to the Mushashi-1 protein; commercial data; and cell lines (see Table  1 ). 3. During a 3-hour session they filled in the DMP questionnaire, with technical help provided but minimal help on content, in order to let them independently explore the questionnaire and identify problems.
4. This session was followed by a one-hour session to qualitatively gather their feedback, for example in relation to how much they understood of the DMP terminology that was used, if they encountered specific needs with regard to their dataset, etc. We obtained 10 DMPs (available at  Vranken et al. , 2023). This procedure revealed that most ESRs had particular difficulties with: i) the many nested questions inside the DMP    Binding kinetic traces (heliOS) and extracted association and dissociation rate constants, as well as affinity values for a determined proteinnucleic acid interaction (Musashi-1).
RNA-Musashi1 interaction in living cells Protein-nucleic acid interaction analysis; Gene expression ltr, ltv, txt <5 Gb Data collected using LigandTracer technology (. ltr) and analyzed using TraceDrawer (. ltv). Detection of RNA-protein binding and kinetics in living cells using real time binding assays. questionnaire, pointing to its complexity, and ii) lack of understanding of data management concepts and terminology, especially the metadata section, which was overlooked by most ESRs because they did not comprehend why this was relevant. The qualitative feedback therefore indicated a strong need to simplify the version 1. 0. 3 knowledge model, with more explanations provided for each of the questions. The 10 DMPs were therefore reviewed in detail by the DM PIs in subsequent meetings and the version 1.
0. 3 model was updated accordingly to result in the further simplified and annotated RNAct_ESR-Training_KM version 1. 0. 14 DMP questionnaire (available at  Vranken et al. , 2023)  (Figure  4 ). A template project based on this questionnaire and pre-filled with general information about the RNAct project (such as funding sources) was proposed to the ESRs in April 2022 (available at  Vranken et al. , 2023). The following steps were then taken: 1. The ESRs were asked to fill this template anew, now online using the -by then more extensive -information about their dataset. 2. To obtain more quantitative feedback, an online survey form (available at  Vranken et al. , 2023)  was created for the ESRs to fill in after completing their DMP. The resulting 8 full completed projects and the survey results were collected in June 2022 (available at  Vranken et al.
The RNAct DSW Knowledge Model We present the final RNAct DSW Knowledge Model that was implemented based on the ESR feedback after the first round of DMP production and which is available at https://registry. ds-wizard. org/knowledge-models/rnact:rnactesr:1. 1. 0. We reduced the number of chapters from 7 to 4, thereby merging chapters III, IV and V about 'Creating and Collecting data', 'Processing data', and 'Interpreting data' into a single chapter III: 'Collecting and Describing your data'. We also merged Chapters VI and VII about 'Preserving data' and 'Giving access to data' into a single chapter IV 'Storing and giving access to your dataset'. Meanwhile, we pruned certain nested questions to avoid cognitive overload. We mention here some examples of removed or simplified questions. -In chapter I, we skipped questions relative to the description of diverse policies and procedures for data management or requiring any additional specialist expertise (as these questions are beyond the ESR role in the RNAct project).
The original metadata question in the Chapter 'Collecting and Describing your data' was restructured as shown in Figure  5. We added three sub-questions to the question "What kind of metadata will you collect and save?" to collect information on metadata for: • Identifying the dataset • Describing the dataset • Reusing the dataset. By doing so, we wanted to clarify what the metadata concretely refer to. We also provided examples of relevant vocabularies and ontologies for each category of metadata, as well as links to online resources to guide ESRs in the task of understanding and selecting proper vocabulary and ontology terms for their data.
ESR feedback The discussions at the DM subcommittee and with the ESRs identified a series of issues that surfaced, and solutions which we employed, which are summarized in Table  2. In addition, we formulated a final survey for the ESRs to quantify where the key problems are situated from their perspective. At the end of the process, ESRs were asked to answer this survey. Results for the three checkbox questions are presented in Figure  6. • To the 1 st question of the survey, ESRs mostly answered that they found the 4 chapters equally useful, while some of them specified that the most useful was Chapter III (Collecting and describing the data), others opted for Chapter IV (Storing and giving access to the dataset). • To the 2 nd question, concerning the most difficult chapters, answers were quite diverse and covered all chapters, except for the first one (administrative information).
Conclusion A limitation of our study and results is that they only cover in depth the experiences of 10 ESRs in the life sciences field, although with a relatively wide coverage of topics from protein structure to biosensors and synthetic biology, and especially covering both experimental and computational angles. The data we collected is highly qualitative, with some quantitative aspects. The main product of the process is the RNAct knowledge model (version 1. 1. 0), which we are making available via https://registry. ds-wizard. org/knowledge-models/ rnact:rnact-esr:1. 1. 0, with any updates available via https://registry-ppe. ds-wizard. org/knowledge-models/rnact:rnact-esr:latest. We also noticed that some ESRs filled in the absolute minimum in the templates, likely because of a lack of motivation.
Issue Possible solutions The final aim of a DMP is very difficult to get across and is in general considered very abstract, hindering the students' motivation. Concrete examples help to clarify why they are creating a DMP, and to motivate them. The selection of the most important datasets to document and store is difficult, especially during exploratory phases of research. At which point should one archive data? Provide a wider perspective: which datasets will in the end be most useful for other users? This is especially important for early-stage researchers that might not yet have this view. It is very difficult to label datasets with metadata and other info in a structured way (using ontologies, …) so that it is easily re-usable. In other words, questions related to terminologies and vocabularies are difficult.
In the focussed data case, organised file directory structures are often the key organisational feature, and help to understand dataset content. The need for correctly licensing data is difficult to get across, but is essential for researchers to understand in relation to making their data open. Online resources such as http://ufal. github. io/public-licenseselector/ to guide the selection of suitable licenses can help. Once the DMP is finalized, how should the dataset be published in public repositories, and where? This question goes beyond the DMP production per se but it shows that the exercise can prepare to next steps of open science, e. g. , data sharing. The experimentalists need more explanations and (video) tutorials to help them answering the most difficult questions. Identify existing training material and adapt them to the audience if necessary, e.
g. , from the ELIXIR TESS catalogue produced DMPs are also not machine actionable; each DMP can be visualized and exported in various formats, but we still lack tools with functionalities such as querying, aggregating, performing statistical analyses, etc. Institutions increasingly ask for DMPs, but their further use beyond the initial generation stage, where it ideally makes researchers ponder their data, seems limited  (Smale et al. , 2020). To exploit DMPs as the field progresses, it will become increasingly important to direct researchers to topic specific databases, where their data can be stored in a highly structured and meaningful way. The EOSC 3 (European Open Science Cloud) organisation is working towards this, but combined data storage and DMP infrastructure is required, as ideally the DMP should only describe the specific data locations where field-specific information will 3 https://eosc.
To get a more complete picture of DMP training, we think that such an exercise with DS wizard should be extended with two other steps: • An upstream step presenting basic concepts and principles on DMPs: such presentations can be selected from existing training material in the ELIXIR TESS catalogue, or can be built by exploring the ELIXIR RDMkit web resources, • A downstream step that corresponds to the publication of the dataset in a public repository like Zenodo, for which some training material already exists in the FAIR CookBook or in the RDMkit. Overall, the process we followed thanks to the DS-Wizard played a central role in our DMP training for ESRs in the frame of this RNAct project, and we hope that our efforts can be used in other projects, and by data stewards, to create more complete training on data management for young researchers.
" This claim is offered in the context of how it is likely to "become increasingly important to direct researchers to topic specific databases, where their data can be stored in a highly structured and meaningful way. " This position in and of itself is untestable through the project's method and can't be justified through the shared results. Some further clarity is needed here, because if strictly interpreted or taken out of context this position could significantly narrow the policy purposes of data management planning. Many data management and sharing policies require DMP elements that describe: 1) related tools, software and code; 2) timelines for when and how long data will be available; 3) access, distribution, and re-use considerations, as well as; 4) details surrounding how plan compliance will be monitored and by whom; therefore a DMP compliant with such mandates must include more content than "specific data l;cations where field specific information will be stored".
Specific suggestions: In the abstract, "Data management is fast becoming an essential part of scientific practice," one could argue it's been there the whole time since early astronomical observations, Mendel, etc. It hasn't always been done well or been called out. I'd argue the practice of ○ data management co-evolved with digital data's emergence. Perhaps change the phrasing here so one isn't disagreeing with tiny things before they read the major ideas. Consider revising, "During the last decade, Open Science practices have become mainstream," Perhaps the discussion or prioritization has reached the mainstream, but how far that translates into practice is up for debate. I'd sidestep this normative claim and rephrase ○ I found the leap of creating a DMP for a dataset confusing vs. an entire project.
○ Under Figure  1 : "questions are tagged depending on the timeline in a data/project lifecycle" -> not timeline, but time point in the project lifecycle by when they should probably be answered. ○ "answering Yes to the question "Will you describe your data with standard vocabularies or ontologies?" will result in a green FAIR tag" -> Can this be made a little bit more precise? The flag is already visible before the answer is given, because it is not an exam on FAIR knowledge, the flags are part of the guidance. The particular green flag in this example is "interoperability".
○ Table 2: Expresses the need for video instruction. I'd like to express my agreement there. ○ publication had it's own challenges, especially when ESRs were asked to describe their own datasets. To overcome this, a simplified version of the Life Sciences Data Stewardship Wizard (DSW) Knowledge Model was developed, as a means towards offering training in data management. ESRs were asked to provide feedback on the use of the DSW, and based on their feedback, a new training template for life sciences data management plans was designed. The experimental approach towards the design of this new and simplified DMP was iterative, with revisions between feedback from the ESRs and re-design from the data management supervisors. Overall the work presented here is interesting, and highlights (among others) the need to offer targeted training on data management and DMPs, especially for early career researchers.
Data availability Underlying data Zenodo: Supporting information for the RNAct Data Science Wizard (DSW) knowledge model for early-stage researchers. https://doi. org/10. 5281/zenodo. 7912419  (Vranken et al. , 2023)  This project contains the following files: -RNAct feedback questionnaire. xlsx -RNAct_datasets. xlsx -dmp_development. zip Data are available under the terms of the Creative Commons Attribution 4. 0 International license (CC-BY 4. 0).