dataesr/scientific-paragraphs-categorization huggingface dataset:
Material and methods The present retrospective study was conducted following the Declaration of Helsinki Ethical Principles and Good Clinical Practices and was approved by the ethical committee of the Medical University of Innsbruck (AN2015-0050 346/4. 28). A retrospective analysis of prospectively collected data from the database of a specialized joint surgery clinic was conducted. All consecutive patients who underwent arthroscopic primary ACL reconstruction (ACLR) between January 2022 and June 2022 were considered for study eligibility. Inclusion and exclusion criteria are listed in Fig. 1. Two senior surgeons (CF and CH) performed all the surgeries in both groups. Preoperatively, all patients had sustained an ACL tear, diagnosed based on clinical examination, MRI and arthroscopically. MRI examinations were performed using the department protocol with the patient in the supine position and the knee extended (a maximum of 10° of slight flexion of the knee was allowed in case of marked effusion) on a 3-T whole-body scanner (Skyra, Siemens Healthineers, Er-langen, Germany) using a 6-channel flex coil.
Statistical analysis Data were collected and analyzed using respectively Excel (Microsoft, Redmond, WA, USA) and XLSTAT statistical software packages (Addinsoft LLC, Paris, France). Categorical variables were expressed in numbers and percentages (%). The distribution of continuous variables was assessed by Shapiro-Wilk test. Mean and standard deviation (SD) or Median and interquartile ranges (IQR) were used to summarize the continuous variables according to their distribution. A 2-way random interclass correlation coefficient (ICC) was used to assess the interobserver reliability of the KFs and ALL injuries. ICC values were calculated for absolute agreement and consistency of agreement. ICC values were graded as follows: < 0. 4 Fig. 2  SEA -PTC angle poor reliability, 0. 4-0. 75 moderate reliability, and > 0.
The modelling of the ttW process includes additional electroweak corrections simulated using MadGraph5_amc@nlo and proportional to α S α 3 EW  [69, 70] , where α S (α EW ) is the strong (electroweak) coupling constant. Another sample of ttH events simulated with MadGraph5_amc@nlo at NLO is used in order to compute one of the uncertainties described in section 7. The NLO program powheg v2. 0  [71] [72] [73]  is used to simulate the backgrounds arising from tt, tW, and diboson (W ± W ∓ , WZ, and ZZ) production, from the production of single top quarks, from SM H boson production via gluon fusion (ggH) and vector boson fusion (qqH) processes, and from the production of SM H bosons in association with W and Z bosons (WH, ZH) and with W and Z bosons along with a pair of top quarks (ttWH, ttZH).
For the 2016 and 2017 data-taking periods, additional corrections to the ECAL measurements are applied to take into account the inefficiency in the trigger caused by the gradual shift in the timing of the inputs of the ECAL level 1 (L1) trigger in the region at |η| > 2. 0  [105]. Correction factors are computed from data and applied to the acceptance evaluated by simulation. The uncertainty in these corrections is propagated to the final discriminators and taken as a shape uncertainty, uncorrelated across data-taking periods. The efficiencies of triggers based on the presence of two or three electrons or muons are measured as a function of the lepton multiplicity using samples of tt and diboson events that have been recorded using triggers based on p miss T. The uncertainties on the trigger efficiencies result in uncertainties in the range 1-2%, once propagated to the event yields.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid and other centres for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the  CMS detector , and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;.
Introduction Modern biomedical research relies heavily on quantitative analysis, necessitating mechanisms that ensure transparency, rigor, and reproducibility. Since 2011, an alarming number of retracted papers have been quantified in a "retraction index" indicating a strong correlation with journal impact factor 1 products, to ensure that biomedical research can be replicated and built upon by others in the scientific community. The majority of biomedical studies has limited code availability In 2015, a Repeatability in Computer Science study was carried out to see the extent to which Computer science researchers share their source code 14. Eight years later, we conducted an extensive study to assess the availability of code and data in biomedical research, analyzing a random sample of 480 articles across eight biomedical journals published from 2016 to 2021 (Table  1 ).
Ten manuscripts were selected from each journal for each year of the study period. Out of 480 manuscripts, we considered 453 manuscripts for further analysis. The remaining 27 manuscripts were research letters, editorials, or research reports which did not report any data or code and were discarded from the analysis. In our analysis, we recorded code availability, data availability in the manuscripts, and the presence of data and code availability statements and code organization. Our results indicate that nearly half (49. 9%) of the studies examined failed to share the analytical code used to generate the figures and results used by the researchers (Figure  1a ). Of the code that was shared, the majority (76. 92%) was found on GitHub, while 13. 66% was hosted on other platforms such as Bitbucket, Sourceforge, or individual websites, 5.
98% on Zenodo, 2. 56% was included as supplementary material, and 0. 85% on GitLab (Table  1 ). Of the 453 studies, 209 (46. 14%) used a code availability statement, while 179 (85. 65%) of these 209 actually shared their code (Figure  1b ). However, even if the paper did share the analytical code, there was still the question of whether the link for the code itself functioned or if it had an archival stable link. The term "archival stable link" refers to a permanent or persistent link that remains functional and accessible over time, ensuring that it continues to point to the same resource or information even if the website structure or content undergoes changes. To assess the archival stability of code sharing repositories, we examined the links of the manuscripts that did share the code.
Data sharing is a more prevalent practice than code sharing in biomedical research In this study, we investigated the utilization of data repositories and code repositories to assess the prevalence of code and data sharing practices. Of the 44. 64% of biomedical research articles that conducted primary analyses between 2016 and 2021, we found that a significant majority had shared data while not sharing their code (Pearson's ߯2= ) (Figure  1d ). Specifically, only 29. 90% of the articles were accompanied by both code and data. In contrast, 24. 02% of articles shared neither the code nor the data. Of the 204 primary analysis manuscripts, 39. 71% were accompanied by data only, whereas 6. 4% shared code only (Figure  1e ). When we examined the 453 articles on a year-by-year basis, we found that code sharing practices have exhibited a consistent positive trend from 2016 to 2021 with an increase from 7.
30% in 2016 to 33. 55% in 2021 (Supplementary Figure  6 ). In addition to examining the prevalence of data and code availability practices, we also made specific observations regarding the utilization of data repositories. Our analysis revealed that GEO emerged as the most commonly used repository for sharing data used in analyses, comprising 28. 77% of the shared data, followed by SRA at 17. 81%. The remaining 47. 26% of shared data originated from other repositories (Supplementary Figure  7 ). Additionally, 65. 20% of the articles that shared raw data had a data availability statement mentioned in the manuscript (Supplementary Figure  8 ). To explore the relationship between the presence of data availability statements and data sharing, we conducted an analysis and found a statistically significant association between the two (Pearson's ߯2= 125.
2. Software and resources should be hosted on on archivally stable services Selecting the appropriate service to host your software and resources is critical. A simple solution is to use web services designed to host source code (e. g. , GitHub or SourceForge). To facilitate simple installation, provide an easy-to-use interface to download and install all required dependencies. Ideally, all necessary installation instructions should be included in a single script, especially when the number of installation commands is large. Package managers such as pip or anaconda can potentially make this problem easier to solve.
3. Journals should incentivize authors to make code publicly available. Code and data sharing should be considered the main part of submitting a study, similar to how we  Extract publish dates of selected publications We created a key file with the paths to every XML file representing a publication. We then used a Python script utilizing the package ElementTree to systematically extract publication metadata from these XML files. To find the date the publication was published, our script considered each publication date that was listed in a publication's file (there were often multiple). When the year was missing from a date, we disregarded the date completely. When the month was missing, we recorded the publication month as December. When the day was missing, we recorded the publication day as the last day of the publication month.
Analysis of extracted information We also recorded various parameters such as the raw data availability, where the data is shared, the analytical code availability, code availability in supplementary files, code availability in repositories or webpages, presence of code and data availability statements, and the number of citations for each study. All parameters recorded were based on information provided by the authors in the publication. Amongst the 480 studies, we filtered out the studies that were conducting secondary analyses while looking into raw data availability. When we looked into the raw data availability in each article, we assigned 'Yes' and 'No' to indicate availability and unavailability of raw data. The data was considered available if the data and its corresponding dependencies could be stored on public repository operating systems, and the data can produce expected results from the input data with no errors.
We also recorded whether the data was shared on 'Gene Expression Omnibus (GEO)', 'Sequence Reads Archive (SRA)' or other repositories such as Bioproject, ENCyclopedia Of DNA Element (ENCODE), DNA Database of Japan (DDBJ), GitHub, ArrayExpress, European Nucleotide Archive (ENA). Those articles which shared data on either DDBJ or ENA were included under 'SRA'. Some articles had data shared or obtained from multiple datasets for different parts of their study and these articles were included under the category 'Multiple'. The category 'Multiple' not only refers to articles which have split their data on multiple repositories but also could refer to those which have duplicated datasets on different repositories. Similarly, we recorded the code availability in all the 480 articles and assigned 'Yes' or 'No' to indicate availability and unavailability of analytical code.
The goal of this study was not to check the completeness of analytical code but the presence and incentive of researchers to share code. We specifically looked for code used for analysis (not to be confused with software or source code for algorithms and tools) and considered code availability as a 'Yes' if any analytical code was shared. For those articles that shared code, we recorded if the code was hosted on GitHub, shared in the supplementary materials or hosted other repositories such as Zenodo or a webpage. Some articles that shared code in both supplementary materials and on Github were included under GitHub. Verifying the completeness and/or accuracy of the code shared was beyond the scope of this study. Additionally, we also recorded whether the articles had data availability statements and code availability statements mentioned in a separate section for easy accessibility.
Sharing code and ensuring reproducibility has extended beyond the non-biomedical publishing space; the Political Analysis journal wraps every papers' methods in Code Ocean as standard 18. Since 2012, the pioneering open science journal, GigaScience, has been mandating all papers to have open 19 data, supporting metadata and source code with Open Source Initiative (OSI) -approved licenses. The 20 journal also has its own repository, GigaDB , where authors can host data (not already open in a community approved repository, e. g. , NCBI), in the public domain under a CC0 license. To ensure reproducibility and stability of source code, the GigaDB Curators also take snapshots of all code and a dataset is produced with a citable Datacite DOI (Digital Object Identifier). There are new publishing efforts that aim to address transparency and reproducibility in research by moving beyond static publications.
consider ethical review and publication of study results. Journals and funding bodies should insist that authors make raw data and other supporting data available. Moreover, they should follow recommendations of the National Academies regarding data sharing and abide by the guidelines of 47 the Fort Lauderdale and Toronto agreements. Journals should mandate clear source code and data availability sections to be included in all manuscripts, where accessions for raw data in community- approved repositories are made clear, and other data supporting is cited. Other supporting data should be made openly available in a public server (eg. Zenodo or Figshare) and authors must enter a commitment that their code and data will remain for a long time in stable software archives (e. g. , Github, Sourceforge, Bioinformatics.
Eyes that underwent PPV were included as the study eyes. Exclusion criteria were recurrence of RRD, presence of tractional or post-traumatic RRD, presence of diabetic retinopathy, history of uveitis, glaucoma, functional amblyopia and any pre-existing macular pathology. In addition, eyes that presented retinal alterations such as epiretinal membrane, macular oedema and/or macular hole during the follow-up were excluded from the study. Finally, only patients with at least one interpretable OCT 1 and 12 months after the surgical procedure were included. The following data were extrapolated from medical records: age, sex, preoperative status of the retina (location and characteristics of retinal breaks, presence of peripheral retinal degenerations, presence of vitreous haemorrhage, presence of proliferative vitreoretinopathy), intraocular pressure, lens status, preoperative and postoperative BCVA in logMAR, preoperative and postoperative fundus examination and spectral domain OCT scan.
Acknowledgments: We thank  Alice Maiocchi  ( University of Milan ) for useful discussions about the choice of PROTAC synthons and linkers for Anle138b coupling. perature; SAR, structure-activity relationship; THF, tetrahydrofuran; ThT, thioflavin T; TLC, thin layer chromatography; UBQ, ubiquitin; UPLC, ultra-high performance liquid chromatography; UPS, ubiquitin-proteasome system.
Reverse transcription-quantitative polymerase chain reaction (RTqPCR) experiments Total RNAs were extracted from cells with Nucleospin RNA extraction Kit (Macherey-Nagel) and reverse transcribed using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, ThermoFisher Scienti c). qPCR assays were next performed using the uorescent dye SYBR Green methodology and a CFX384 Real-Time PCR detector (Bio-Rad Laboratories, Marnes-la-Coquette, France). The KiCqStart® SYBR® Green primers for human and mouse cDNA were provided by Sigma-Aldrich. The speci city of ampli ed genes was evaluated using the comparative cycle threshold method (CFX Manager Software). These mean Cq values were used to normalize the target mRNA concentrations to those of the 18S ribosomal protein by the 2 (-ΔΔCq) method.
Cell surface receptors and markers analyses by ow cytometry Phenotypic analysis of MDMs was performed using ow cytometric direct immuno uorescence. After washing and plastic detachment using AccutaseTM (BioLegends, Paris, France), cells were stained with Fixable Viability Stain 780 (BD Biosciences, Le Pont de Claix, France) for 10 min at room temperature to measure viability. MDM were rst blocked in phosphate-buffered saline (PBS) supplemented with 2 % FBS solution and with FcR blocking reagent (Miltenyi Biotec SAS, Paris, France) for 10 min at room temperature to avoid nonspeci c binding, and then re-suspended and incubated with speci c antibodies or appropriate isotype controls for 30 min at 4°C. Cells were washed with PBS, collected by centrifugation (2500 rpm for 5 min) and then analyzed on a LSR II cytometer (BD Biosciences, San Jose, CA, USA) and FlowLogicTM software (Miltenyi Biotec SAS, Paris, France).
Comparative effects of SFA and LFA bers on human MDM phenotype and functions As macrophages are one on the rst cell target of inhaled pollutants and as particle exposure may altered the phenotype (M0, M1 and M2) of human MDM (Jaguin et al. 2015), we analyzed the phenotype and the phagocytic functions of MDM exposed to SFA and LFA. The evaluation of ber toxicity towards human MDM reveals a signi cant decrease of cell viability in cells exposed for 24 h to 25 µg/cm 2 and 50 µg/cm 2 of SFA and FLA respectively (supp data 1). Based on these data, we selected 20 µg/cm 2 of SFA and LFA for the next in vitro experiments. A signi cant down-expression of the endocytic receptor for hemoglobin-haptoglobin complex (or CD163) well known as a M2 marker was found in MDM exposed to SFA and LFA similar to those observed in M1 polarized MDM (Fig.
Funding This work was supported by the  « Agence Nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail » (Anses)  ( MacFibOsis  project  N°2018/1/149 ), the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Université de Rennes (Univ Rennes) ,  « Actions de Recherche Concertées ,  Fédération Wallonie-Bruxelles  ( ARC 19/24-098 ,  CYTAID ),  « Fondation Contre le Cancer »  ( 2019-219 ), and  European Commission  under  H2020  project (Contract no. 874707 ,.
Continuous methane Methane measurements are a particularly powerful way of aligning the gas ages of different ice cores because they exhibit large (from tens to 200 ppb) and abrupt changes of concentration across millennial-scale Dansgaard-Oeschger events that recur throughout the last glacial period (e. g. Epifanio et al. , 2020). Using high-resolution continuous analysis it has also been shown that centennial and faster variability down to below 10 ppb amplitude is well-reproduced between cores  (Lee et al. , 2020; Mitchell et al. , 2013; Rhodes et al. , 2017). As methane is well-mixed in the Antarctic troposphere, not just the pattern but the absolute values should match with reference datasets within uncertainty. Our main dataset, the continuous one from CFA, is good at showing the highresolution variability, but has a large and unknown uncertainty in absolute values.
Nonetheless some of the remaining cracks and transitions between different bags provide an opportunity for the ingress of lab air as the ice melts, leading to spikes in methane concentration. Major short peaks and troughs were identified using the "ginput" MATLAB function, and removed from the dataset. Above 500 m ~25 spikes that were at least a factor 2 higher or lower than the mean of the dataset were removed. Below 500 m, the data became much noisier and ~100 deviations from the dataset were manually removed. Even after removal of the obvious spike artefacts the data remain more noisy than the data that are unaffected by such artefacts, suggesting that positive artefacts arising from inclusion of modern air remain in the dataset. This makes it trickier to clearly align data with a reference dataset in the deeper ice.
Discrete methane To validate and control that the absolute levels of our continuous CH4 record are consistent within uncertainty with the absolute values in reference data, we obtained some well-calibrated discrete measurements (Fig. 3 ), particularly in the deep ice and in the section impacted by the air leak (section 3. 1). Ten discrete samples were therefore measured at the University of Bern between 533-546 m, and a further 25 samples between 600 and 650 m depth. Details of the method have been published elsewhere  (Schmidely et al. , 2021). Concentrations ranged between 413 and 644 ppb, with an estimated precision (1 sigma) of 7 ppb (Table  S1 ). Note that the discrete data presented here have been corrected by -18 ppb  (Schmidely et al. , 2021)  to align them with previously published CH4 records.
These offsets are potentially due to different remnant solubility of CH4 in meltwater using different melt extraction methods in different labs. Taking the uncertainty of the correction into account, the total uncertainty is estimated at 12 ppb  (Schmidely et al. , 2021) , while that of the reference data is estimated at 10 ppb  (Loulergue et al. , 2008). Combining these uncertainties suggests that when comparing absolute values of methane (discrete data) with reference datasets we should allow an uncertainty of 16 ppb (much higher offsets are possible for the data derived by CFA, and there we mainly look for similar patterns to those in the reference data). The discrete data measured in Bern are displayed along with the continuous data in Fig. 3. A number of discrete measurements were also made between 84 and 144 m at Oregon State University which are described in the companion paper  (Hoffmann et al.
Reference datasets Since the basis for our age model is tying variations in our data to variations in well-dated ice cores, in this section we describe the reference datasets used. In order to use the more detailed variability that can be traced during the Holocene, we compared our methane data to the high resolution Roosevelt Island methane record between 2-7 ka bp. Between 7-68 ka we used the WAIS Divide record  (Buizert et al. , 2015; Rhodes et al. , 2017). Between 68 and 156 ka, we used the southern hemisphere methane spline generated from the EDC ice core  (Köhler et al. , 2017). To investigate possible matches with older ice we used the EDC data itself  (Loulergue et al. , 2008). As previously explained, the Roosevelt and WAIS Divide data are on the WD2014 age scale, but we eventually used a conversion table (based on  Buizert et al.
Ice phase: 10 Be across the Laschamps Event and terrestrial marker elements The clear pattern of the 10 Be record across the Laschamps Event has been shown to be closely replicated at several sites in Greenland and Antarctica  (Raisbeck et al. , 2017). For the synchronisation, we used the normalised stack that was recently created based on 3 Greenland and 3 Antarctic records  (Adolphi et al. , 2018). As the reference dataset for terrestrial deposition we used the nssCa record from EDML  (Fischer et al. , 2007) , because of its greater proximity to Skytrain in the Atlantic sector of Antarctica, with further validation using the record from EDC  (Wolff et al. , 2010). 6. Tie points to 100 ka 6. 1. Methane First, we note that the discrete methane data (Fig. 3 ) confirm that the methane concentrations in the section from 534-545 m are much too high.