dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  131  partner repository with the dataset identifier PXD031451. The UniProt SwissProt and TrEMBL databases were accessed via https://www. uniprot. org/, while the IEDB database and associated analysis resource tools NetMHCpan v4. 1 and MHC-NP were accessed via https://www. iedb. org/, http://tools. iedb. org/mhci/, and http://tools. iedb. org/mhcnp/, respectively. Data supporting the findings of this manuscript are available within the article, the Supplementary Data, the Supplementary Information and the Source Data files. Source data are provided with this paper.
Clinical gait analysis supports treatment decisions for patients with motor disorders. Measurement reproducibility is affected by extrinsic errors such as marker misplacement-considered the main factor in gait analysis variability. However, how marker placement affects output kinematics is not completely understood. The present study aimed to evaluate the Conventional Gait Model's sensitivity to marker placement. Using a dataset of kinematics for 20 children, eight lower-limb markers were virtually displaced by 10 mm in all four planes, and all the displacement combinations were recalculated. Root-mean-square deviation angles were calculated for each simulation with respect to the original kinematics. The marker movements with the greatest impact were for the femoral and tibial wands together with the lateral femoral epicondyle marker when displaced in the anteriorposterior axis.
Testing procedure. The present sensitivity analysis used a procedure similar to that of a previous study  13. All subjects were equipped with the CGM marker set 3 (14 mm) and walked barefoot at a self-selected speed along a 10 m walkway. Marker trajectories were tracked by a 12-camera motion capture system (Oqus7+, Qualisys, Göteborg, Sweden) at a frame rate of 100 Hz. Gait kinematics were processed using a Vicon Plug-in Gait software clone-provided as 'CGM 1. 1' by the PyCGM2 open-source library-that uses a static trial for calibration  15. The analysis used the eight markers required to define one of the lower limbs. The present study only considered the left leg. For the pelvis, we calculated a virtual marker at the midpoint of the posterior iliac spine (SACR, for sacrum), between the right and left anterior iliac spine (RASI and LASI, respectively) markers.
For each marker displacement simulation, every other marker position was defined and lower-limb kinematic data were computed together with the GPS. Due to the great computational resources required because of the high number of simulations planned (at around 10 s per simulation, or 45 days of computation per subject with a standard computer), calculations were performed using a high-performance, multi-core computing system suitable for parallel computation. The toolbox used for this study is available at https:// gitlab. unige. ch/ KLab/ multi-marker-mispl aceme nt. git.
Thus, an error in the placement of the knee-joint center marker impacts the definition of the ankle-joint center and consequently the foot progression angle. Overall, the calculated impact of a displaced marker could be noted in the two simulated displacements in opposite anatomical directions. Gait scores, like the GPS, are very good at classifying a patient's gait by comparing it with a reference database of a general asymptomatic population. As the calculation uses kinematic data, the variability noted because of marker displacement also introduces variability into the final gait classification and thus may also have a considerable impact on gait data interpretation. We therefore investigated the impact of marker displacement on overall gait scores. Marker displacement in one leg resulted in GPS variations of up to 7°.
Here we present an overview of the standardized datasets, including hydrographic profiles, remote sensing reflectance, temperature and salinity, particle absorption, nutrients, dissolved organic carbon, particulate organic carbon, particulate organic nitrogen, colored dissolved organic matter absorption, fluorescent dissolved organic matter intensity, suspended particulate matter, total particulate carbon, total particulate nitrogen, stable water isotopes, radon in water, bacterial abundance, and a string of phytoplankton pigments including total chlorophyll.
, 2021). While the MALINA expedition furthered our insights into the cycling of carbon across shelf-basin systems, it left knowledge gaps regarding the spatial dynamics of OM in the delta area of the Mackenzie, where the network of rivers empties its water and sediment into the Arctic Ocean. Furthermore, lacking from the otherwise extensive MALINA, CASES and ARDEX datasets were measurements of the seasonal variations in OM sources, types and fluxes, particularly during the spring freshet when river discharge rates peak. While conducting fieldwork in key areas of the Mackenzie Delta region is challenging due to limited accessibility, estimating the fluxes of organic carbon, including particulate (POC) and dissolved (DOC) forms, to arctic coastal waters via satellite observations is also challenging due to the complexity of the optical properties of marginal environments, frequent cloud cover and low sun elevation  (IOCCG, 2015).
Ultimately, the in situ characterization of OM (i. e. , its origin, age, reactivity, lability and optical properties), will feed the development of optical remote sensing algorithms and numerical modeling endeavors. In this article, we present an overview of the broad dataset acquired during the four field surveys conducted as part of the fourth Nunataryuk Work Package -WP4 Coastal Waters -in the Mackenzie Delta region. To our knowledge, the data collected in this difficult-to-access area and included in this paper are unprecedented, both in their spatial coverage of the fluvial-to-marine transition zone and their seasonal extent.
Data quality control and data processing Coherence and integrity of the data were first visually assessed to remove errors stemming from measurement procedures, methods and instruments. Cleaned data were grouped and structured into ASCII files, each constructed to gather variables of the same type (e. g. , nutrients). In each of these files, a minimum number of variables (columns) were always included to help the merging of different datasets (Table  1 ). More than 150 different variables were measured during the WP4 Nunataryuk 2019 field expeditions. The complete list of variables is presented in Table  2  (section 4. 4), along with statistical summaries such as average, standard deviation and ranges. The final processed, quality controlled datasets and related metadata are available in  Juhls et al.
2021 (including 13 associated datasets). In the following sections, a subset of these variables, along with the methods used to collect and measure them, are presented. Data visualization shown in this article was performed with R 4. 2. 0   , 2, 3  and 4 ). Note that the Arctic Red River hydrometric station (10LC014) used to retrieve discharge rates (Fig. 3A ) is shown. (R Core Team, 2022). The code used to process the figures and tables is publicly available (https://github. com/PMassicotte/ nunataryuk_data_paper) under the MIT licence. The purpose of this paper is to provide an overview of the availability of data 135 for use in future studies and not to offer an in-depth analysis of the measurements conducted. Note that the groundwater field survey was conducted during Leg 3 in the vicinity of Tuktoyaktuk (Kugmallit Bay), where 29 water samples were collected at the nearshore (see section 4.
To do so, values of atmospheric pressure measured by Environment and Climate Change Canada (ECCC) weather stations near the sampling locations (Aklavik, Inuvik, Shingle Point and Tuktoyaktuk) were used to calibrate the CTD measurements. The atmospheric values collected during deployments were compared to ECCC values to compute and apply an offset (see Fig. B1  in Appendix B). Data spikes were removed (median value on a five points wide running window), and data were further smoothed by a local polynomial regression (using the function "loess" from R), and very occasionally a spline when the loess was not satisfactory) to output the data on a homogeneous 0. 01 m step depth grid. Finally, multiplecast stations (mostly during Leg 1) were averaged. The raw files, metadata files, output files, code files, as well as a detailed processing summary are available on the GitHub public repository https://github.
, 2019). From the E d and L u profiles, apparent optical properties (AOP), such as the diffuse attenuation coefficient or the remote-sensing reflectance, were computed  (Bélanger et al. , 2017). The very shallow waters and the often strong currents made the usual free-fall deployment challenging and even risky for the instrument. A custom deployment method was therefore adopted using a negatively buoyant frame manually lowered with a horizontal telescoping mast and a block pulley. Deploying the sensors into the direction of the sun in highly turbid waters (photons mean free path of less than 1 m) avoided any optical pollution from the small ship hull. The processing of the radiometric data was conducted according to recent protocols implemented in an open source package in R available at https://github.
The HPLC was controlled by Agilent Chemstation software. Calibration was performed with individual pigment standards, whose concentrations have been determined spectrophotometrically using absorption coefficients in common Nunataryuk expeditions (Legs 1, 2, 3 and 4) in 2019. Boxes represent the interquartile range (IQR), the horizontal lines show the medians, while the vertical lines show data spread with min/max whiskers. Notice the y-log10 scale. Also note that a few data points below 100K (Leg 2) were excluded from this graphical presentation. with those used by most other laboratories  (Hooker et al. , 2005)  and the commercial vendor, DHI Water and Environment (Hørsholm, Denmark). Thirty-six peaks were individually quantified by HPLC, from which 26 pigments were reported (some pigments contained individual components that were summed and reported as one pigment).
, 2016; Walters et al. , 2016). Sequencing was performed on an Illumina MiSeq platform, and sequences were then analyzed using the dada2 pipeline v1. 16  (Callahan et al. , 2016)  in R v4. 1. 2 (R Core Team, 2021) and R-Studio (v1. 2. 1335). Taxonomy assignment was performed up to the Genhttps://www. overleaf. com/project/626fcc3ad4b482cb8a8e3272us level with SILVA reference database v138  (Quast et al. , 2012). Details on Amplicon Sequence Variants (ASVs) of bacterial communities are given in Table  2. For bacterial isolation, samples (0. 5 mL) were placed in cryotubes containing 500 µL of 70% sterile glycerol and stored at -80 • C before isolation at the laboratory. After thawing, 100 µL of each sample was spread in triplicate on R2A agar plates adjusted to the salinity of the samples, and the Petri dishes were incubated in the dark at 10 • C to allow the development of aerobic heterotrophic psychrotolerant marine bacteria.
, 2021). Sequences processing was performed following the R package DADA2 pipeline version 1. 16  (Callahan et al. , 2016)  with R software (version 4. 0. 5; R Core Team, 2014). Taxonomic assignment was performed using UNITE  (version 8. 3; Nilsson et al. 2019)  databases. Metadata related to fungal abundance and diversity can be found in Table  2. DNA sequences were deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under accession number PRJNA822885.
Before each analysis, the quartz cuvette was flushed first with 5% HCl, then with deionized water, and finally with the sample. Absorbance metrics were then extracted from the different scans. Concomitantly, CDOM fluorescence was measured using a Varian Cary Eclipse fluorometer and a 1-cm pathlength quartz cuvette. Emission wavelengths (λ Em ) ranged from 230 to 600 nm and excitation wavelengths (λ Ex ) from 220 to 450 nm. When the absorbance was higher than 0. 3 at 254 nm, the sample was diluted to avoid saturating the fluorometer  (Miller and McKnight, 2010). Fresh deionized water was used as a blank and absorbance measurements of the samples were used to correct the fluorescence data for inner filter effects. All data were corrected for the Raman and Rayleigh effect, using daily fresh deionized water signals, and for the inner filter effect, using absorbance spectra.
Moreover, because the low concentrations of Fe tot results in low total Fe:DOC molar ratios, on the order of 10 -2 , this suggested that the Fe effect on the absorbance and fluorescence of CDOM was negligible  (Poulin et al. , 2014). Therefore, no Fe effect correction was applied to the DOM concentration, absorbance, and fluorescence. The collected data were then described as excitation-emission matrices (EEMs) and different absorbance and fluorescence metrics were calculated (Fig. 13 ). This database was completed by 50 more samples collected in the same region in summer 2021 (see  Flamand et al. 2022  for details). Fluorescence spectroscopy was successfully used to determine the origin of the DOM pool. Fluorescence occurring at low excitation/emission wavelengths was associated to protein-like material originating from autochthonous production, whereas fluorescence happening at higher wavelengths was associated to humic-like material of higher molecular weight derived from terrestrial sources  (Coble, 1996; Murphy et al.
94 ppm by volume (ppmv) (Air Liquide) traceable to the National Institute of Standards and Technology, and the precision of the technique was ±4% (at 5 nmol L -1 ). Fundamental physical, optical, chemical and biological processes associated with reservoirs and fluxes of OMt were acquired, measured and processed during four WP4 Nunataryuk field campaigns in the Mackenzie Delta region and adjacent coastal waters of the Beaufort Sea (ISR, Northwest Territories, Canada). A subset of the broad dataset has been presented and described here. A full list of variables measured during the field expeditions can be found in Table  2 , including authorship information for these datasets. Several of the datasets have been fed into Pangaea data publisher for earth and environmental science, in order to promote the principles of the International Council for Science -World Data System (ICSU-WDS).
As far as we know, the data gathered and currently included in this paper are unparalleled in the breadth of their spatio-temporal coverage of a fluvial-to-marine transitional region in the Arctic. The scope of the dataset presented here offers contingencies towards its further exploitation in research. 412, 443, 490, 510, 551, 555, 560, 565, 620, 645, 667, 673, 683, 709, 745      Detailed metadata are associated with each file including the principal investigator's contact information. For specific questions, please contact the principal investigator associated with the data (see Table  2 ). Information about code availability. The code used to process the figures and tables is publicly available (https://github. com/PMassicotte/nunataryuk_data_paper) under the Earth System Science Data Discussions Preprint.
D Appendix D The ranges of IOPs (light absorption (a) and scattering (b) coefficients) encountered in the Mackenzie River delta zone greatly exceeded the ones considered by  Gordon and Ding (1992)  when developing a well-known correction for self-shading of inwater radiometric measurements. Also, the Monte Carlo code SimulO  (Leymarie et al. , 2010; Doxaran et al. , 2016; Voss et al. , 2021)  was used here to estimate and correct for the self-shading effects on in-water upwelling radiance measurements carried out using a Biospherical C-OPS radiometer deployed on the IcePro profiling platform. The exact dimensions of the IcePro Platform and radiometer (a cylinder with a radius and a length of 3. 5 and 36. 9 cm, respectively) were used as inputs in SimulO. The very wide ranges of a and b coefficients (up to 12 m -1 and 100 m -1 , respectively) were considered, together with two particle scattering phase functions: Fournier-Forand with backscattering ratios of 1.
Funding sources Financial support for this project was provided by the  European Research and Innovation programme Horizon 2020  to the  Nunataryuk  project (Grant agreement no. 773421 ), by the  Network of Centers of Excellence of Canada ArcticNet  ( P66-Nunataryuk ), by  Québec-Océan  funded through the  Fonds de Recherche du Québec -Nature et Technologies , by the  Sentinel North  initiative funded through the  Canada First Research Excellence Fund , by the  Aurora Research Institute -Aurora College , and through the  leadership of the Canada Excellence Research Chair in Remote Sensing of Canada's New Arctic Frontier. The  Department of Fisheries and Oceans Canada  supported the project through the  Arctic Science Funds program. Fungal and bacterial abundance analyses were supported by the  RESTORE  project funded by the  French National Research Agency  (contract n •  ANR-19-CE32-0013 ).
fr On request NA copies/mL Biosystems) Tisserand Water Fungal diversity x x x NA ASVs Illumina MiSeq sequencing Fabien Joux / Lucas joux@obs-banyuls. fr On request NA Tisserand Water Microbial respiration NA x x NA µmol/L/h Optodes (Presens) Fabien Joux / Lucas joux@obs-banyuls. fr On request NA Tisserand Water CDOM absorption x x x x 1/m UltraPath liquid waveguide system Atsushi Mastuoka atsushi. matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 (aCDOM) at 254, (World Precision Instruments, Inc. ) 350, 375, 443 nm Water CDOM absorption x x x x 1/nm UltraPath liquid waveguide system Atsushi Mastuoka atsushi. matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 spectral slope (World Precision Instruments, Inc. ) (350-500 nm) Water Dissolved organic x x x x mg/L High Temperature Catalytic Bennet Juhls bennet.
juhls@awi. de Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 carbon (DOC) Oxidation (Shimadzu TOC-VCPN) Water Fluorescent dissolved x x x x RU Aqualog fluorescence spectrometer Atsushi Matsuoka atsushi. matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 organic matter A Coble-peak (FDOM A-peak) Water Fluorescent dissolved x x x x dimensionless Aqualog fluorescence spectrometer Atsushi Matsuoka atsushi. matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 organic matter Biological index (FDOM BIX) Water Fluorescent dissolved x x x x RU Aqualog fluorescence spectrometer Atsushi Matsuoka atsushi. matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 organic matter C Coble-peak (FDOM C-peak).
matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 organic matter M Coble-peak (FDOM M-peak) Water Fluorescent dissolved x x x x RU Aqualog fluorescence spectrometer Atsushi Matsuoka atsushi. matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 organic matter T Coble-peak (FDOM T-peak) Water Intensity fluorescent x x x x RU Aqualog fluorescence spectrometer Atsushi Matsuoka atsushi. matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 dissolved organic matter component 1 (FDOM C1) Water Intensity fluorescent x x x x RU Aqualog fluorescence spectrometer Atsushi Matsuoka atsushi. matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 dissolved organic matter component 2 (FDOM C2) Water Intensity fluorescent x x x x RU Aqualog fluorescence spectrometer Atsushi Matsuoka atsushi.
matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 dissolved organic matter component 3 (FDOM C3) Water Absorption NA NA x x 1/m Measured with an AC9 sensor David Doxaran / Guislain doxaran@obs-vlfr. fr / In progress NA coefficient (a) at 440, (WetLabs/Seabird Scientific) Bécu guislain. becu@takuvik. ulaval. ca 555, 630, 715, 730, installed within an optical frame 750, 767, 820, 870 (profiling) nm Water Beam attenuation NA NA x x 1/m Measured with an AC9 sensor David Doxaran / Guislain doxaran@obs-vlfr. fr / In progress NA coefficient (c) at 440, (WetLabs/Seabird Scientific) Bécu guislain. becu@takuvik. ulaval. ca 555, 630, 715, 730, installed within an optical frame 750, 767, 820, 870 (profiling) nm Water Concentration of NA x x x ppm Estimated from FLBBCD eco-puck Guislain Bécu guislain.
de/10. 1594/PANGAEA. 937587 Maestro (Leg 1), CTD RBR Concerto (Leg 2,3,4) Water Salinity profile x x x x dimensionless in situ CTD RBR Maestro (Leg 1), Guislain Bécu guislain. becu@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 CTD RBR Concerto (Leg 2,3,4) Water Temperature x x x x °C in situ conductivity meter Guislain Bécu guislain. becu@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 Water Temperature at x x x x °C Extracted from in situ CTD RBR Guislain Bécu guislain. becu@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 surface Maestro (Leg 1), CTD RBR Concerto (Leg 2,3,4) Water Temperature profile x x x x °C in situ CTD RBR Maestro (Leg 1), Guislain Bécu guislain. becu@takuvik. ulaval. ca Available https://doi.
pangaea. de/10. 1594/PANGAEA. 937587 CTD RBR Concerto (Leg 2,3,4) Water Nitrate concentrations x x x x µmol/L Automated colorimetric procedure Martine Lizotte / martine. lizotte@takuvik. ulaval. ca / Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 [NO] Jean-Éric Tremblay jean-eric. tremblay@bio. ulaval. ca Water Nitrite concentrations x x x x µmol/L Automated colorimetric procedure Martine Lizotte / martine. lizotte@takuvik. ulaval. ca / Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 [NO] Jean-Éric Tremblay jean-eric. tremblay@bio. ulaval. ca Water Phosphate x x x x µmol/L Automated colorimetric procedure Martine Lizotte / martine. lizotte@takuvik. ulaval. ca / Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 concentrations Jean-Éric Tremblay jean-eric. tremblay@bio.
ulaval. ca [PO 3 ] Water Silicate x x x x µmol/L Automated colorimetric procedure Martine Lizotte / martine. lizotte@takuvik. ulaval. ca / Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 concentrations Jean-Éric Tremblay jean-eric. tremblay@bio. ulaval. ca [Si(OH)] Water Multispectral in-air NA x x x uW/cm 2 /nm Compact-Optical Profiling System Bennet Juhls bennet. juhls@awi. de On request NA downwelling (C-OPS), ICE-Pro frame irradiance (Ed0+) (Biospherical Instruments) Water Multispectral NA x x x uW/cm 2 /nm Compact-Optical Profiling System Bennet Juhls bennet. juhls@awi. de On request NA in-water downwelling (C-OPS), ICE-Pro frame irradiance (Ed) (Biospherical Instruments) Water Multispectral NA x x x uW/cm 2 /nm/sr Compact-Optical Profiling System Bennet Juhls bennet. juhls@awi.
de On request NA in-water upwelling (C-OPS), ICE-Pro frame radiance (Lu) (Biospherical Instruments) Water Multispectral Remote NA x x x 1/sr Calculated using Ed and Lu Bennet Juhls bennet. juhls@awi. de Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 sensing reflectance (Rrs) of the surface water Water aP at 360, 380, 400,.
pangaea. de/10. 1594/PANGAEA. 937587 carotenoids chromatography (HPLC) concentration Water Photosynthetic x x x x mg/m 3 High performance liquid Atsushi Matsuoka atsushi. matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 carotenoids chromatography (HPLC) concentration Water Photsynthetic x x x x mg/m 3 High performance liquid Atsushi Matsuoka atsushi. matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 pigments chromatography (HPLC) concentration Water Prasinoxanthin x x x x mg/m 3 High performance liquid Atsushi Matsuoka atsushi. matsuoka@takuvik. ulaval. ca Available https://doi. pangaea. de/10. 1594/PANGAEA. 937587 concentration chromatography (HPLC) Water Total accessory x x x x mg/m 3 High performance liquid Atsushi Matsuoka atsushi.