dataesr/scientific-paragraphs-categorization huggingface dataset:
We produced non-interpolated DEMs with a spatial resolution of 4 m. We used a time series of 12 DEMs from 2012 to early 2020 (out of the 18 acquired, six were outside the seasonal boundaries). Except for the years 2014 and 2020 for which no images were acquired in autumn, we were able to estimate five annual mass balances (Table  1 ). We quantified four winter mass balances with an image near the end of the accumulation season, from 2017 to 2020. Due to a 50% gap in the DEM dated 13 May 2019, we also used the DEM dated 22 March 2019 to reduce the potential impact of the gap. The 2019 winter geodetic mass balance was thus the sum of the mass balances from 8 September 2018 to 22 March 2019 and 22 March 2019 to 13 May 2019. We then 3-D co-registered the DEMs over stable areas with a co-registration algorithm  (Nuth and others, 2011) , using an opensource python-based workflow  (Shean and others, 2016 ; https:// github.
Glaciological uncertainties on the annual mass balances The calculation uncertainty of the annual mass balances follows the analysis of  Wagnon and others (2020) , detailed in their supplements for non-linear models. The uncertainty formula for a mass balance inside calibration dates is the following: s M. B. an. glacio = s 2 M. B. geod. N + i S 2 i s 2 1 , (12) where σ M. B. geod. is the error of the calibrating geodetic mass balance, N the number of years between the DEMs, S i the relative area in proportion to the total glacier area of the altitude bins used in the mass balance calculation and σ ε the std dev. of the residuals of the non-linear method. As we lacked calculations details of the calibration of GLACIOCLIM glaciological mass balances for the period including 2012-19, we used the results of a 2003-2019 geodetic mass balance, whose DEMs processed from aerial data have been used for the GLACIOCLIM glaciological mass-balance calibration.
Acknowledgements. We thank  Etienne Berthier  ( CNRS, LEGOS ) for his involvement in the methodology exploration and comments on this work. We are grateful to  César Deschamps-Berger  ( CESBIO ) for sharing his experience and providing code for jitter correction. We thank  Helen Amanda Fricker , scientific editor of the paper, for her help improving the writing; as well as the anonymous referees whose reviews helped improving the quality of the paper. This study was carried out in the framework of the  Kalideos-Alpes  project (https://alpes. kalideos. fr)funded by the  French Space Agency  ( Centre National d'Etudes Spatiales, CNES ),  Investissements d'Avenir program :  Risk@UGA  ( ANR-15-IDEX-02 ) and of the  Service National d'Observation GLACIOCLIM  (https://glacioclim. osug. fr/). This work was supported by the  Labex   OSUG@2020 (Investissements d'Avenir - ANR10 LABX56 ).
Journal Policy Study: This study examined journal policies related to shortcut citations and methodological reporting. Each study examined three fields; biology, psychiatry and neuroscience, to improve generalizability. These fields were selected based on the study teams' expertise. The abstraction protocols, data and code for the prevalence study and journal policy studies were deposited on the Open Science Framework (RRID:SCR_003238; https://osf. io/d2sa3/?view_only=e130a3a44a9d4b148b8ea3ed61646e7e  [12] ). The following data were abstracted: 1. Is the paper related to the SARS-CoV-2 pandemic? 2. Does the paper include additional methodological details in the supplement? 3. Does the paper reference a repository or repositories as a shortcut for any method used? If so, what is the name of the repository? (Note that code used to analyze data was considered a method).
Case Series Selection of articles: In each field, papers from the prevalence study were divided into quintiles based on the total number of shortcuts (possible + probable). Ten articles from each quintile were randomly selected using a computer algorithm and placed on an ordered list. Potential reviewers for the case series identified articles on the list that fell within their area of expertise. We then chose the first article in each list that had a selfidentified expert evaluator. This approach was used to select 15 parent articles (one article per quintile per field). Data abstraction: Each reviewer carefully examined all papers or materials cited as shortcuts to determine whether they could locate the cited content. While articles were almost always accessible, reviewers were sometimes unable to access other types of cited resources.
Abstraction: Training was performed on the top 20 eligible journals for each category prior to data abstraction. Data were collected by two of the three independent abstractors (BS, KB, NS). When disagreements could not be resolved by consensus, discrepancies were resolved through discussion with the third reviewer. An additional study team member (TLW) was consulted to resolve complex discrepancies. A trained abstractor abstracted data from non-English journal webpages with the help of a native speaker. Webpages containing author and or submission guidelines were identified by looking for the following terms in the website menu, or by using the website search function: "policy", "policies", "author", "author/s guidelines", "author/s instructions", "submission guidelines", "submit your article", "recommendation", "about", "publish".
Data analysis: Code for data figures and color schemes was adapted from a previous paper  [15]. Summary statistics were calculated using Python (RRID:SCR_008394, version 3. 7. 7, libraries NumPy 1. 18. 5  [16] , Pandas 1. 2. 4  [17]  and Matplotlib 3. 4. 1  [18, 19] ). Charts were prepared with a Python-based Jupyter Notebook (Jupyter-client, RRID:SCR_018413  [20] , Python version 3. 7. 7, libraries NumPy 1. 18. 5  [16] , Pandas 1. 2. 4  [17]  and Matplotlib 3. 4. 1  [18, 19] ) and assembled into figures with vector graphic software.
Common examples of supplemental methods included tables that list primers or sequences or provided basic information on study participants. Methodological information in repositories included a mixture of study protocols and code for data analysis. The most common repository was GitHub, followed by ClinicalTrials. gov, OSF and FigShare (Table  S4 ). Other repositories were rarely used in this dataset. Shortcut citations: Methodological shortcut citations were common in all three fields, with 96% of neuroscience papers, 90% of biology papers and 92% of psychiatry papers containing at least one possible or probable shortcut. Figure  2b  (right panel) shows the median number of possible and probable shortcuts for each field. Summary statistics are reported in Table  S5. Reviewers assessed the age of the newest and oldest methodological shortcut citation in each paper.
Sequencing quality control Image analyses and base calling were performed using the NovaSeq Control Software and Real-Time Analysis component (Illumina, San Diego, CA, USA). Demultiplexing and trimming were performed using Illumina's conversion software (bcl2fastq 2. 20). The quality of the raw data was assessed using FastQC from the Babraham Institute and the Illumina software SAV (Sequencing Analysis Viewer). FastqScreen was used to estimate the potential level of contamination.
[136]. Differential expression analysis was performed with the DESeq2 package (Bioconductor)[135]using an adjusted p-value cut-off of 0. 01. The SRR accession numbers for the raw sequence data are SRR15222852-65. Cbln11 structural annotation was carried out manually in GenomeView[136]based on BlastHit of Cbln11 sequences available at NCBI and from mapped RNA-Seq reads. Gene structure was exported in gtf format and added to the current annotation. The datasets supporting the conclusions of this article are available in the SRA repository under accession numbers: SRR15222859-65 (https:// www. ncbi. nlm. nih. gov/ sra/? term= SRR15 222852) and SRR15222852-58 (https:// www. ncbi. nlm. nih. gov/ sra/? term= SRR15 222859).
Funding The is study was supported by the AWI-MARUM-IFREMER AMI Partnership Programme (DEADLY TRIO project), LabexMer (ANR-10LABX-0019, OASYS project), the Ministry of Ecological and Solidarity Transition and the Foundation for Biodiversity Research (Ocean Acidification Program, PACIO project), the Deutsche Forschungsgemeinschaft, PE 1157/8-1, MA4271/3-1 (the FITNESS project) and France Génomique National infrastructure, funded as part of "Investissement d' Avenir" program managed by Agence Nationale pour la Recherche (contract ANR-10-INBS-09).
53-months-old parents * F2: larvae ** F2: 18-months-old juveniles Rearing period 24 October 2013 -26 March 2018 1 April 2018 -1 June 2018 2 June 2018 -17 October 2019 Available Data period 8 February 2016 -6 March 2018 1 April 2018 -1 June 2018 2 June 2018 -12 February 2019 [136]. Differential expression analysis was performed with the DESeq2 package (Bioconductor)[135]using an adjusted p-value cut-off of 0. 01. The SRR accession numbers for the raw sequence data are SRR15222852-65. Cbln11 structural annotation was carried out manually in GenomeView[136]based on BlastHit of Cbln11 sequences available at NCBI and from mapped RNA-Seq reads. Gene structure was exported in gtf format and added to the current annotation. The datasets supporting the conclusions of this article are available in the SRA repository under accession numbers: SRR15222859-65 (https:// www.
Quantum chemical study of lanthanide plumbole bonding Density functional theory (DFT) calculations at level PBE0/def2-TZVP 30 were carried out with the program system TURBO-MOLE 31 in order to investigate the lanthanide plumbole bonding situation. Effective core potentials were employed for the inner electrons of the heavy metal atoms. For Pb the effective core potential covered the inner 60 electrons (ECP-60),  32  for La the inner 46 electrons (ECP-46)  33  and for Sm and Er the inner 28 electrons (ECP-28). 32 Additionally, the RI-J-approximation was applied. 34 Magnetically induced current densities were calculated with the GIMIC program, 35 using the (perturbed) densities from TURBOMOLE as input, which are available also for open-shell systems in a local version at present. For the optimized structures, the distances shown in Table 1 are consistently overestimated by $5 pm, thus the tendencies regarding changes (vide supra) are very well reproduced.
The CAMEMBERT Repositories The data resulting from the simulations and analysis will be uploaded to the CAMEMBERT permanent repository at https:// ckan. emac. gsfc. nasa. gov/organization/cuisines-camembert by participating scientists. These data will be made available for public access upon the publication of the results. Prepublication access can be requested by contacting the authors. Inputs described in this protocol and scripts related to the analysis of data and production of plots for the publications will be made available on the CAMEMBERT GitHub repository at https:// github. com/projectcuisines/camembert. Inputs will be available immediately while scripts to reproduce results will be made publicly available upon the publication of the results.
Environmental Impact While not related to the accuracy of the GCMs, we add as a part of the protocol the requirement that participating scientists include estimates of power consumption and CO 2 emissions associated with each production run included in the intercomparison. We include this requirement not as point of comparison between GCMs, as the environmental impact will primarily depend on the methods of energy generation in the local power grids. Instead, we include this to highlight the environmental impact of supercomputing and to encourage providers of supercomputing resources to transition to environmentally sustainable energy sources. These data will be reported in the first results paper.
CAMEMBERT belongs to the CUISINES metaframework, a Nexus for Exoplanet System Science (NExSS) science working group. The testing of the protocol was done using Met Office Software. The contributions of D. A. C. , N. J. M. , D. S. , and M. Z. made use of the ISCA High Performance Computing Service at the University of Exeter and the DiRAC Data Intensive service at Leicester, operated by the University of Leicester IT Services, which forms part of the STFC DiRAC HPC Facility (www. dirac. ac. uk). The equipment for DIRAC was funded by BEIS capital funding via STFC capital grants ST/K000373/1 and ST/R002363/1 and STFC DiRAC Operations grant.
Then the amount of dye color development in the water only well was subtracted out to quantify the metabolic activity due to the substrate itself. Negative control plates containing just buffer were used to verify the assays were not contaminated. We subsequently fit growth curves to both individual substrates and to the average color across the plate (mean well color development) using the growthcurver package  (Sprouffske & Wagner, 2016). Growth rates not significantly different from zero (p >. 05) were set to zero before all substrate-specific growth rates were used for calculating substrate class-specific growth rates and for principal coordinates analysis. Principal coordinates analysis was completed using the vegan package  (Oksanen et al. , 2019)  with a Hellinger-transformed relative growth rate matrix rather than the raw growth rate data so that relative substrate preference could be separated from the overall differences in growth rate under the assay conditions.
g. , 20 and 25°C for July and 15 and 20°C for October for both heated and control soils); measured the metabolomics and the metagenomes in a subset of data. Finally, we measured the soil organic matter quality with the rock-eval® thermal pyrolysis and quantified the dissolved organic carbon in soil extracts. slurry-only control (200 μl slurry, 50 μl buffer) and a standard curve (50 μl 0-500 μm in twofold dilution, in 200 μl buffer). A separate plate for mineral and organic soil quenches (200 μl slurry, 50 μl standard) and substrate-only controls (200 μl buffer, 50 μl substrate) was prepared for each temperature. Hydrolytic assay plates were measured at an excitation/emission wavelength pair of 360/450 nm immediately following substrate addition and after an additional 2, 4, and 8 h. Greatest activity was observed between 0 and 2 h, so we used data from this period in our analyses.
A 100 μl aliquot of the supernatant from oxidative assay plates was removed to a clear polystyrene plate after 4 h of incubation and read at 460 nm. We used a SpectraMax M2 plate reader controlled by the SoftMaxPro v. 5. 4 software for all measurements. Extracellular enzyme rates were calculated as previously  (German et al. , 2011)  and normalized by the sample-specific microbial biomass carbon (MBC) measurement (described below).
| Metagenome sequencing and annotation Metagenomes were obtained from organic soil collected in July from both sites, totaling 20 samples. DNA extracted from soils incubated at 20°C was sequenced at the Joint Genome Institute (Methods S1 and Table  S2 ). Reads were assembled and PFAMs were annotated in the assembled fraction of reads using the DOE-JGI Metagenome Annotation Pipeline  (Huntemann et al. , 2016). This led to an average of 1. 72 Gbp assembled and 2,807,085 genes predicted per metagenome. We then mapped PFAMs to CAZys  (Pold et al. , 2016)  and used mean mapping depth for CAZys relative to the mapping depth to the single copy rpoB gene (PF04563, PF04561, PF04565, PF10385, PF00562, PF04560) as our proxy of CAZy abundance across samples. We elected to focus on CAZymes as they are responsible for the polymerization and depolymerization of many of the dominant compounds found in leaf litter and microbial cell walls, including cellulose, lignin, and chitin.
| Data analysis Analysis was completed in R version 3. 6. 3 using packages re-shape2  (Wickham, 2007) , vegan  (Oksanen et al. , 2019) , lme4  (Pinheiro, et al. , R Core Team, 2019) , agricolae  (Mendiburu, 2019) , grofit  (Kahm et al. , 2010) , dplyr  (Wickham et al. , 2022) , lattice  (Sarkar, 2008) , MASS  (Venables & Ripley, 2002) , and permute  (Simpson, 2022). Variables were log-transformed where necessary to fit model assumptions. We calculated Hedge's G  (Cohen, 1988)  using the effect size package  (Torchiano, 2020)  to compare the effects of warming treatment and season on biotic and abiotic soil properties; we considered drivers to have a significant effect when the 90% confidence intervals surrounding the effect size estimate did not overlap zero (Figure  2 ). We elected to use an alpha of 0.
1 (and 90% confidence intervals) rather than 0. 05 (or 95% confidence intervals) because we were limited by the number of field replicates we could collect and a power analysis using means for microbial biomass, soil C stocks, and extracellular enzyme data collected from the site in 2014  (Pold et al. , 2017)  indicated this would be needed to detect changes in mean values of 25% with a power of 80%. Figures were made using the ggplot2 package  (Wickham, 2016).
We also completed SEM analysis on microbial CUE at 20°C. This model had a similar overall layout of the temperature sensitivity of respiration model, except that we additionally hypothesized that diversity of EcoPlate substrate consumption would be affected by season and carbon quality, and that respiration and growth would be impacted by carbon quantity and quality (Figure  S9 ). The SEM model path fit was performed using the piecewiseSEM package  (Lefcheck, 2016). Cumulative enzyme activity and temperature sensitivity are represented in the model as composite variables  (Lefcheck, 2016) , using the mean of BG, NAG, and oxidative enzymes for the former and BG alone for the latter. We kept the model that explained the most variation in temperature sensitivity of respiration and CUE, and had a non-significant Chi-squared test (p >.
DATA AVA I L A B I L I T Y S TAT E M E N T Metagenomes generated during this study are available at the JGI Gold portal with the ID Gs0149985 (Table  S2 ). All data and codes used in this study can be obtained in the Dryad platform (https://doi. org/10. 5061/dryad. sxksn 036t) or Open Science Framework Project (https://osf. io/zdhfb/), from the supplementary materials (Tables  S3-S6 ) and from the authors upon request.
Participants Thirty-three healthy full-term 3-to 4-month-old infants (15 girls and 18 boys, mean age = 3 months 24 days, range = 2 months and 27 days -4 months and 4 days) participated in the study. Data from fourteen infants were rejected due to fussiness and/or noisy electroencephalogram (EEG) recording (less than ten accepted trials in one experimental condition). A final sample of 19 infants were considered for the analyses (8 females and 11 males, mean age = 3 months 28 days, range = 3 months -4 months and 4 days). The mean number of years for maternal education was 15. 6 (range from 9 to 19 years). Participants were recruited by letters that were sent based on birth records provided by the municipality of the Paris city. Parents gave a written consent for participating in the study. The protocol was carried out in accordance with the ethical standards of the Declaration of Helsinki and approved by the Université Paris Cité Ethics Committee.
Data and Statistical Analyses Similarly to earlier studies (e. g. ,  [73] ), we extracted amplitudes from two time windows: 200 ms to 500 ms (early window) and 500 ms to 1000 ms (late window). A repeatedmeasures analysis of variance (ANOVA) included Trial Type (congruent/incongruent numerosity-hand's opening pairing), Recording Area (frontal/parietal), and Laterality (left/midline/right) as within-subject variables. Based on previous studies  [63, 66, 67] , the mean amplitudes extracted from 42 electrodes over the frontal and the parietal-occipital recording areas were included in the statistical analyses. The frontal and the parietal-occipital recording sites included the following electrode positions: 20, 24 (F3), 28 (FC5), 29 (FC3), 35, 36 (C3) and 30 (C1) (frontal left); 12, 5, 6 (FCZ), 13 (FC1), 112 (FC2), 7 and 106 (frontal midline); 104, 105 (C2), 110, 111 (FC4), 117 (FC6), 118 and 124 (F4) (frontal right); 59, 60 (P1), 66, 70 (O1), 65 (PO7), 69 and 73 (parietal-occipital left); 72 (POZ), 62 (PZ), 75 (OZ), 71, 74, 76, 82 (parietal-occipital midline); 85 (P2), 84, 83 (O2), 91, 90 (PO8), 89 and 88 (parietal-occipital right).
Acknowledgments: The authors would like to thank all the families who took part in the study. We also would like to thank  Mariana Anaya  for helping in the creation of the figures. This work was supported by a grant from the  Agence Nationale de la Recherche Scientifique  ( NUMACT -18-CE28-0017-01 ) to M. D. d. H. Finally, we thank the DIM Cerveau et Pensées 2013 MOBIBRAIN for funding the EEG system.
Solar Wind and Geomagnetic Indices In order to detect the solar contribution and coupling to ionospheric variability, solar wind and geomagnetic indices from the OMNIweb database (https://omniweb. gsfc. nasa. gov/, accessed date: 20 January 2021)  [44]  are used along with data from the International Service of Geomagnetic Indices (ISGI) website, which is available at http://isgi. unistra. fr/ data_download. php (accessed date: 29 January 2021). There is the use of the solar wind (SW) plasma speed, temperature, pressure, proton density, beta parameter, and density ratio between alpha and proton particle, and also the IMF B z component and total Bt IMF, the eastward zonal electric fields (E y ), the geomagnetic auroral indices AE and Am indices, and the equatorial SYM-H and Dst indices. Here, the symmetric disturbance field in H, SYM-H, is essentially the same as Sugiura's hourly Dst index.
Vertical TEC from GNSS Stations in Africa Slant TEC (sTEC) from ground-based GNSS Africa Geodetic Reference Network (AFREF, http://afrefdata. org, accessed on 29 January 2021) is used to study the low-latitude ionospheric variations in the African sector during the multiphase storms of February, 2014. AFREF stores input GNSS data for sTEC calculation in Receiver Independent Exchange Format (RINEX) [48] with 30 s cadences. To characterize the response to the storm, sTEC is converted into vTEC using GPS-TEC analysis software tools  [1]. Table  1  shows the coordinates of the GNSS stations. The median-average of February is used as a background vTEC for the analyses of disturbances to obtain the relative deviation of the daily values. The TEC deviation from the TEC background is referred to as relative TEC change (rTEC).
Then, the stations used to retrieve EEJ strength for the American, African, and Asian sectors are calculated as follows:    EEJ at America → EEJ(HUA) =∆H(HUA)-∆H(TRW) EEJ at Africa → EEJ(AEE) =∆H(AEE)-∆H(NAB) EEJ at Asia → EEJ(LKW) =∆H(LKW)-∆H(HLN) (4) The solar-quiet current intensity (CI) is estimated using the geomagnetic stations listed in Table  3. For the CI estimation, a baseline of D (declination) magnetic component values is firstly obtained by averaging hourly data centered on the local midnight data, i. e. , 23 h ≤ LST ≤ 2 h. Then, CI at night is assumed negligible due to the low electron density, and it results in an extremely low electric conductivity (e. g. ,  [55] ). Subsequently, after subtracting the baseline values from the D component, we calculated the hourly deviations that are approximately equal to the variation in ∆D components.
The quiet-time condition of the magnetospheric forcing was set in the model with Am = 6 for the experiment. In this way, the diurnal, annual, and solar cycle variations are eliminated, and the residuals mainly show the short-term variations due to magnetospheric forcing, i. e. , those variations mainly caused by geomagnetic storms. The Calabia & Jin model is available at Zenodo (https://zenodo. org/record/3563463, accessed date: 3 September 2021).