dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses The analyses will follow the intention-to-treat principle. Postoperative delirium cumulative incidence function (CIF) will be compared at day 10 between the two randomised arms (melatonin vs placebo) by means of a competing risks framework, using the Fine and Grey model, that allows to estimate CIF on the presence of other cause of failure (deceased in our study), altering the probability of experiencing the event of interest, delirium. The significant level of all statistical analyses will be a 2-sided 5%. All statistical analyses will be performed using SAS software (SAS Institute) V. 9. 4 or later, or R software (R Foundation for Statistical Computing, Vienna, Austria. http://www. r-project. org/) V. 4. 0 or later.
All analyses results will be reported according to the Consolidated Standards of Reporting Trials 2010 guidelines and the Consolidated Health Economic Evaluation Reporting Standards guidelines on economic evaluation in healthcare. 32 ta collection and management Data collection will be done in electronic format, the statistical software CleanWeb for data entry will be used. The software will fulfil the regulatory requirements and security norms. Data will be handled according to the French law. All original records (including consent forms, reports of suspected unexpected serious adverse reactions and relevant correspondences) will be archived at trial sites for 15 years. The clean trial database file will be anonymised and maintained for 15 years. We will collect data on primary and secondary endpoints, as well as potential risk factors of delirium (postoperative medication, comorbidities and complications) detailed in table  2.
Data collection and quality control The persons responsible for the quality control of clinical matters will take all necessary precautions to ensure the confidentiality of information relating to the study participants. These persons, as well as the investigators themselves, are bound by professional confidentiality. During or after the research, all data collected about the participants and sent to the sponsor by the investigators (or any other specialised collaborators) will be anonymised. Under no circumstances should the names, addresses and other protected identifiers of the subjects involved be shown. In any case of premature withdrawals and exits, the investigator must document their reason(s) and try to collect primary endpoint, secondary endpoints and safety assessment, if the participant agrees.
If a participant exits the study prematurely or withdraws consent, any data collected prior to the date of premature exit may still be used excepted if the participant refuses in writing. To monitor compliance all treatment blisters will be stored after use for counting and auditing. All processing units (used or not) will be stored in the medical ward and sent to the site pharmacy at study end for destruction. A data monitoring committee has not been convened, on the grounds that the study is low risk. This has been approved by the Sponsor, Steering Committee and the independent Ethical Board. The research data will be collected and monitored using an eCRF through CleanWEB Electronic Observation Book and will be centralised on a server hosted by the AP-HP Operations Department. This research is governed by the CNIL (Commission Nationale de l'Informatique et des Libertés, national commission for informatic and liberty) 'Reference Method for processing personal data for clinical studies' (MR-001, amended).
An audit can be carried out at any time by independent individuals appointed by the sponsor. The aims of the audits are to ensure the quality of the study, the validity of the results and compliance with the legislation and regulations in force. The persons who manage and monitor the study agree to comply with the sponsor's audit requirements. The audit may encompass all stages of the study, from the development of the protocol to the publication of the results and the storage of the data used or produced as part of the study. Sponsor is responsible for access to the study database.
1d, e ). These two features were significantly reduced in mice vaccinated with the IL-4-K, and absent in mice vaccinated with the IL-13-K or the combination of IL-4-K and IL-13-K (Fig. 1d, e ). The superior benefit of the dual IL-4/IL-13 vaccine on airway hyperresponsiveness (AHR) was even more apparent when using noninvasive whole-body plethysmography  22  (Supplementary Fig. 7 ). Our data suggest that AHR may be more dependent on IL-13 than IL-4 in this chronic asthma model, and can be prevented upon prophylactic dual vaccination against IL-4 and IL-13. Next, we assessed the effect of the kinoids on airway eosinophilia and inflammation. HDM-treated mice demonstrated elevated eosinophil numbers in bronchoalveolar lavage (BAL) fluid that were reduced fivefold and sixfold in mice vaccinated with IL-4-K or IL-13-K, respectively, and reduced even further (21-fold) upon dual vaccination (Fig.
i, j Representative lung sections stained with hematoxylin and eosin (H&E; revealing leukocyte infiltration) (i), or periodic acid-Schiff (PAS; revealing mucus-producing goblet cells in dark purple) (j). Lower panels in i and j represent magnifications of the dashed areas. Lung sections are representative of each group (n = 8 mice/group). P values in d and e were calculated using two-way ANOVA followed by a Tukey posttest. d: ### < 0. 0001, # = 0. 0381, ***: 0. 003 for IL-13-K/HDM, 0. 0002 for IL-4-K + IL-13-K/HDM, <0. 0001 for CRM 197 /PBS and PBS/PBS; e: ### < 0. 0001, ** = 0. 0019, ***: <0. 0001 for all groups. P values in f-h were calculated using two-tailed Mann-Whitney U test (f-h) vs. CRM 197 /PBS (f, g), CRM 197 /HDM group (h), or indicated groups. Source data are provided in the Source data file.
a hIL-4 levels in the supernatant of splenocytes from n = 2 WT or n = 4 hIL-4/hIL-13 KI ; hIL-4Rα KI humanized mice stimulated ex vivo with PMA (20 nM) b hIL-13 levels in the supernatant of splenocytes from hIL-4/hIL-13 KI ; hIL-4Rα KI mice sensitized with ovalbumin (OVA), and stimulated ex vivo with OVA (n = 5 mice). c hIL-13 levels in BAL fluid from hIL-4/hIL-13 KI ; hIL-4Rα KI mice sensitized and challenged with OVA (n = 5 mice). Samples were collected 24 h after the last OVA challenge. Data in a-c show values from individual mice with bars indicating mean ± SD (in a and c). d, e Representative staining of skin samples from WT or hIL-4/hIL-13 KI ; hIL-4Rα KI mice with anti-mouse IL-4Rα (d) or the anti-human IL-4Rα mAb dupilumab (e) and DAPI. Staining skin sample are representative from three WT and six hIL-4/hIL-13 KI ; hIL-4Rα KI mice from two separate experiments.
P values were calculated using two-tailed Mann-Whitney U test. Source data are provided in the Source data file. starting 3 days after the last sensitization, for a total of nine administrations. Mice were sacrificed 24 h after the last challenge with HDM or PBS. In the therapeutic protocol (Fig. 3 ), BALB/c mice were sensitized three times with HDM (100 μg in 30 μl PBS), and PBS as control, with a 3-day interval between each administration. Lightly isoflurane (3% in air)-anesthetized mice were then challenged i. n. with 25 µg of HDM in 30 µl PBS, or PBS alone as a control, twice a week for a total of 18 challenges, starting 3 days after the last sensitization. Vaccination with mouse IL-4-K and mouse IL-13-K was initiated 4 days after the third HDM challenge. Mice were sacrificed 24 h after the last challenge with HDM or PBS.
Measurement of airway reactivity to methacholine. Twenty-four hours after the last challenge with HDM or PBS, responses to aerosolized methacholine were measured using whole-body plethysmography (EMKA technologies), using IOX base 8c/RF-8a (0796) software. Responses to inhaled methacholine were assessed by recording Penh over 5 min after each dose of aerosolized methacholine (baseline, 0, 3, 5, 7, and 14 mg/ml). Data were analyzed using Datanalyst software (DATA 4238). Invasive measurements were also performed in anesthetized, tracheostomized, mechanically ventilated mice using a FlexiVent software FlexiWare 8. 0 (Scireq). Aerosolized methacholine was administered in increasing concentrations (baseline, 0, 6, 25, 50 and 100 mg/ml). Lung resistance (R) and tissue elastance (E) were computed by assuming a constant phase model.
Cells were stained with anti-Siglec-F-PECy7 (clone # REA798, Miltenyi), anti-CD49b-APC (clone Dx5, eBioscience), and anti-IgE-FITC (clone # R35-72, BD Pharmingen). Blood eosinophils were gated as Siglec-F + , SSC high , and blood basophils as CD49b + , IgE +. For analysis of peritoneal mast cells, 5 ml of PBS were injected into the peritoneal cavity and the abdomen was massaged gently for 20 s. Fluid containing peritoneal cells was collected and cells were stained with anti-c-KIT APC (clone # 2B8, Bioscience) and anti-IgE-FITC (clone # R35-72, BD Pharmingen). Peritoneal mast cells were gated as c-KIT + , IgE +. Samples were acquired on Miltenyi MACSQUANT 10 and 16. Data were analyzed using FlowJo 10. 4. 2 software. All FACS sequential gating strategies are presented in Supplementary Figs. 19  and 20.
9 ). Samples were acquired on BD LSRFortessa cell analyzer (BD Biosciences). Data were analyzed using FlowJo 10. 4. 2 software. Quantification of antibodies against mouse and human IL-4 and IL-13, and CRM 197. The immunogenicity of the kinoids was assessed by evaluating antibodies against mouse IL-4, human IL-4, mouse IL-13, human IL-13, and CRM 197 in sera collected at different time points after vaccination. Mouse IL-4, human IL-4, mouse IL-13, human IL-13, or CRM 197 were coated and incubated overnight at 4 °C at 1 µg/ml. After each step, plates were washed three times with PBS Tween 20 0. 01% (v/v). After blocking with casein 2% (w/v) in PBS, serum samples were added, a twofold serial dilution was conducted starting at 500 dil -1 (diluted in PBS, casein 1% (w/v) and Tween 20 0. 01% (v/v)).
55  ). The extent of mucus production was also quantified in a blind manner on PAS-stained lung sections by a score according to the percentage of goblet cells in the epithelial cells 56 : 0: no goblet cells, 1: <25%, 2: 25-50%, 3: 50-80%, and 4: >80%. Mast cell quantification on toluidine blue-stained lung sections was performed using Zen Software 3. 1 blue edition. IHC section and acquisition. For lung and ear skin IHC, the bigger lobe from the right lung or the ears were excised postmortem fixed with 1% PFA for 24 h at room temperature. Tissue dehydration was performed in sucrose gradient baths (10, 20 and 30%), then tissues were embedded in OCT compound. A total of 8-μm thick sections of lung tissue sections were treated using a heatinduced epitope retrieval method, as previously described  57.
75 dry. Images were processed using Zen Software 3. 1 blue edition (Zeiss) and ImageJ (1. 53c) software. CD3 -KLRG1 + IL-4 + ILC2 were counted on images of four to six consecutive microscopic fields from each mouse. A 8-μm thick sections of ear skin sections were blocked and permeabilized with PBS supplemented with 0. 5% (w/v) BSA (Sigma-Aldrich) and 0. 1% saponin (Sigma-Aldrich), for 30 min at room temperature, and incubated with an anti-IgE antibody coupled to Alexa Fluor (AF) 488 (Biolegend, UK), at 10 µg/ml overnight at 4 °C in the dark. Tissue sections were then washed three times in PBS 0. 5% (w/v) BSA and 0. 1% saponin, and incubated with DAPI and avidin-sulforhodamine in PBS 0. 5% (w/v) BSA and 0. 1% saponin, for 2 h at room temperature in the dark. Finally, samples were mounted in Mowiol medium (Sigma-Aldrich).
Acknowledgements The authors acknowledge  Dr. Livia Brunner  and  Dr. Nicolas Collin  from the  Vaccine Formulation Laboratory  ( University of Lausanne, Switzerland ) for the provision of SWE adjuvant, and thank  Dr. Thomas Marichal  ( GIGA Institute, Liege, Belgium ) for helpful advices on the gating strategy to identify lung-resident regulatory eosinophils. We thank  Lhorane Lobjois  for technical assistance at the  Cell Imaging Facility of Infinity (Inserm UMR1291, Toulouse, France ) and  Dr. Muriel Pichavant  ( Institut Pasteur, Lille, France ) for technical advices on airway measurements. Requests regarding hIL-4/IL-13; hIL-4Rα KI humanized mice should be made to  Dr. Beverly Koller  ( University of North Carolina, USA ). E. C. is the recipient of a CIFRE Ph. D. fellowship. B. B. was supported partly by a stipend from the  Pasteur-Paris University (PPU)   International Ph.
D. program , and a fellowship from the  French "Fondation pour la Recherche Médicale FRM". N. G. acknowledges funding from the  European Research Council  ( ERC-2018-STG , No. 802041 ) and the  INSERM ATIP-Avenir program , B. H. K. acknowledges funding from the  National Institutes of Health (NIH)   HL093735  and an  Award  from the  American Asthma Foundation. L. L. R. acknowledges support from the  ATIP-Avenir program , and P. B. from the  European Research Council (ERC) - Seventh Framework Program  ( ERC-2013-CoG 616050 ). This work was supported by a grant from the  French National Research Agency   ANR-18-CE18-0023  " AllergyVACS ", NEOVACS, the  Institut National de la Santé et de la Recherche Médicale (INSERM)  and the  Institut Pasteur.
In the age of big data, soil data are more available than ever, but -outside of a few large soil survey resources-remain largely unusable for informing soil management and understanding Earth system processes outside of the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for global relevance. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including 5 unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: data discovery, input, harmonization, curation, and publication.
, 2020) , which can be difficult if it relies on the standard 'available on request' note included at the end of publications  (Savage and Vickers, 2009; Vines et al. , 2014). While data access has improved due to funding agencies requiring data management plans that include public archiving, more widely available data does not ensure that they are interoperable and reusable. Many of the challenges and best practices we highlight below address this latter half of FAIR data -interoperability and reusability -and are reflective of the diversity of soil data generators, users, and stakeholders. In this work, we hope to guide prospective soil data providers, users, and synthesizers by highlighting both challenges and examples of working toward FAIR data within the soil science community. While it is not desirable nor tenable to force all soil data to adhere to a single data template, there are shared strategies, semantic tools, community practices, and protocols for data harmonization and integration efforts that would increase transparency and decrease initialization effort of these projects.
We set out in this paper to summarize current practices in soil data management and database harmonization, as well as suggest new semantic and community tools for the future of soil harmonization. We offer this summary from the collective viewpoint of over two dozen researchers across North America and Europe, working on a broad array of funded and unfunded projects to construct independent harmonized soil databases. With our perspective and definitions for context, our specific objectives were to define common and aspirational practices in harmonized database development, and envision next steps to improve reproducibility and transparency of harmonized database development for soils.
Current database pipeline A database pipeline is a series of steps that brings data from an initial format to a harmonized database, with steps including: generation, discovery, acquisition, input and harmonization, curation, gap-filling and pruning, and publication of the database (Figure  1 ). While these general steps are fairly easy to articulate, their specific implementation varies and is often time consuming. Discovery, access, and "wrangling" can exceed 80% of the effort required for a new scientific discovery  (Lohr, 2014; Beno et al. , 2017). The extension of this pipeline to create a generalizable solution adds an additional level of difficulty  (Furche et al. , 2016) , but can be essential to ensure that both the data and workflows are FAIR  (Goble et al. , 2020). For brevity, we assume that datasets of interest have already been identified.
Data sources Figure  1. Database pipeline with pain points (indicated by "-") and suggestions for improvement (indicated by "+") to conform more closely to FAIR Data Principles. Data sources can be diverse, including published (online repositories and scientific literature) and unpublished sources (direct from principal investigator; PI). After these sources have been discovered, the data must be accessed, harmonized according to a standard format or data model (internal to the project or a community-driven standard). The aggregated data must then be curated before ultimately being published for reuse.
Availability Reproducible analysis is fundamental to robust science and data analysis. Naively, a newcomer to the field of 21st century science might be forgiven the assumption that a published peer-reviewed journal article would, by default, also be accompanied by a published dataset in a machine readable format. In the authors' experiences, this is uncommon for a number of reasons. While some peer-reviewed journals and funding organizations require the data to be deposited in a trusted repository that supports the FAIR principles  (Fox et al. , 2021) , confirming that data meet these high standards is often overlooked during the review process. Indeed, there is often confusion in the field as to what exactly such 'high standards' are. Key contextual data for one study may be mostly irrelevant for a second.
Data providers have unique knowledge about their systems and can be instrumental in expanding or modifying the scope of the resulting database analysis. Data-centered collaborations can lead to new communities of practice and better science (see Future recommendations section). In addition to these benefits there are also tradeoffs. Acknowledgement and level of visibility of original data contributions remain an open question. Data providers may expect to be listed as co-authors upon reuse of their data, in recognition of their past effort collecting the data, despite having limited or no engagement in the reuse project. This is often frustrating to a data aggregator who, in turn, is left with an ever expanding list of coauthors with varying levels of involvement (see Publication section).
Data input and harmonization Datasets typically reflect the purposes of the original study; however examining those same data in a broader context often requires a different data format. Harmonizing one data contribution with a broader collection entails merging or breaking apart data tables, renaming columns, and occasionally converting units of observation (see Section 2. 3 below). Translation of the data table structure (often called the data model) is typically handled one of three ways: manual template transcription, scripted template transcription, or keyed translation. All three methods have justifications for use, and data aggregators may choose https://doi. org/10. 5194/bg-2021-323 Preprint. Discussion started: 15 December 2021 c Author(s) 2021. CC BY 4. 0 License. to turn from one method to another as a database grows.
This approach requires familiarity with both soil science (to understand the measurements) and programming (to write the scripts). In practice, the authors find such a skill combination unusual for any individual researcher, necessitating the use of interdisciplinary teams and adding organizational complexity. The codebase can also become unwieldy if written on a case-by-case basis for each input dataset. These costs are countered by an increase in accuracy, transparency, and reproducibility when compared with manual transcription. Keyed translation is the most general approach and, as a result, requires the most extensive informatics work. Keyed translation is related to scripted transcription but uses a dictionary to define relationships between the input data format and target data format; for example mapping Column 1 to Column A or Table  A to Table B.
Keyed translation combines metadata about each dataset with a generalized conversion script to generate a harmonized database (see A4). Such a generalized approach can be more easily extended to expand the number of data sources. However there is currently no broadly agreed upon annotation vocabulary making it necessary to annotate each dataset individually within each project. In addition, the computational expertise needed for this approach is the highest of the three outlined here. While we feel this holds great promise for future studies, this is an uncommon approach due to these challenges.
Curation Data that are in an integrated database often still need to be curated to ensure accuracy, convert units, address missing data or gaps, and reduce or aggregate data to derive relevant data products. While scripts are often used extensively at this phase, expert interpretation and review is a critical component. Finally, reuse of databases often requires a repeat of this curation phasewhat may be appropriate for one question or purpose may not be appropriate for another. Scripting is heavily utilized to augment expert review to quality control data. These scripts both automate and document quality control criteria, however setting those criteria often requires an extensive knowledge of the system and measurement https://doi. org/10. 5194/bg-2021-323 Preprint. Discussion started: 15 December 2021 c Author(s) 2021.
CC BY 4. 0 License. methods. For example, the ISRaD database has an automated quality control protocol accessible via web interface which ensures that values are within reasonable ranges, and checks that records and critical metadata are appropriately linked across database tables  (Lawrence et al. , 2020). Following this initial filter, a manual individual 'expert review' is conducted by a trained ISRaD volunteer (see A2). These extensive quality control procedures require time and diverse expertise, making them unattractive for many open source database projects without broad recognition of service by the field. Gap-filling expands data coverage, and can include a number of strategies to fill in missing data at both the layer or horizon level as well as the profile or site level. Strategies include linear interpolation, pedotransfer functions, georeferenced data extraction, or more sophisticated machine learning algorithms.
Given the wide variability in gap-filling practices and objectives, these methods must be extensively documented, clearly state use case restrictions, and estimate uncertainties. Finally, a common step is to remove unnecessary data from the data product via pruning. Pruning removes samples based on location or type of measurement after database compilation, typically to cater to specific data user needs, and reduces the size of the data product. Both pruning and gap-filling highlights the importance of maintaining an intermediary harmonized database as well as the final data product both to preserve the original contextual data and reuse of that data for alternative projects.