dataesr/scientific-paragraphs-categorization huggingface dataset:
Specific PET-BIDS Considerations In order to construct the _pet. json sidecar file which details the PET experiment metadata, a description of a common PET experiment is necessary. In Fig. 1  we present an overview of a common PET experiment (which includes the sampling of blood data, including plasma, whole blood and metabolite data). The experiment is defined on a single time scale relative to a predefined "time zero". Notably, "time zero" will often be defined as time of injection or scan start, and the injected dose, the PET data, and blood data should optimally all be decay-corrected to time zero. However, because the time of injection does not always coincide with scan start, PET data may not be decay-corrected to the time of injection. Whether the image has been decay-corrected may be indicated in its metadata (using the fields: ImageDecayCorrected and ImageDecayCorrectionTime).
In these cases, the values for injected mass and specific activity may be set to "n/a" to indicate missing values. We note that for required metadata, this is currently only valid for injected mass and specific activity, although future releases of the PET-BIDS specification may tackle further challenges in use cases that deviate from the current guidelines. In the case of including MRI data with PET data, it is necessary to pay specific attention to the format the MR images are in, such as whether they have been unwarped to correct for gradient non-linearities. There is a specific metadata field in the BIDS specification for MRI  9  named NonlinearGradientCorrection which indicates this (please see Fig. 2  for an example). The main reason for the importance of this is that the MRI needs to be corrected for nonlinear gradients causing spatial distortions in order to have the same shape as the accompanying PET scans for co-registration  8.
Therefore, it is required to specify whether the corresponding MR images Fig. 2  Exemplary PET-BIDS dataset with a dataset description. This includes the adequate acknowledgements (1), previews of PET files (2,3), including blood (4,5) and MRI data (6). The left side shows a directory tree of a common PET-BIDS dataset, with files in the root directory describing the dataset (README and data description. json), a file with participant-specific information (participants. tsv), and a JSON sidecar file describing the metadata needed to understand the corresponding TSV file. Next to the files in the root directory, there are subject directories named sub-<label> for each study participant. In the subject directory lies all acquired data divided into modalities (anat and pet, for the structural MRI and PET, respectively).
Public PEt-BIDS Datasets Several example datasets (with zero-byte, i. e. , empty, NIfTI files) are publicly available in the BIDS-examples GitHub repository (https://github. com/bids-standard/bids-examples). The first two of these datasets (full version) are also openly available on OpenNeuro formatted using the PET-BIDS standard: • The CIMBI Database [ 11 C]DASB PET Example Dataset consists of test and retest measurements from two individuals to measure serotonin transporter availability  16. No blood data are available for this dataset. It was collected as a part of the CIMBI database (https://doi. org/10. 18112/openneuro. ds001420. v1. 0. 1)  17, 18. • The NRM2018 Grand Challenge dataset consists of baseline and intervention data from five individuals. No blood data are available for this dataset (https://doi.
Community tools For Data Sharing and analysis Data curated into the PET-BIDS standard can be validated for BIDS compliance by using the "bids-validator"  22  , a JavaScript application checking for the completeness and consistency of the data. The BIDS validator runs locally as a command line version (via Node. js), as a Docker container, or as a browser-based application (https://bids-standard. github. io/bids-validator/). Using this important validation software, PET researchers are provided with feedback about incompatibility errors as well as warnings if important pieces of metadata are missing. In addition to providing a version of the validator as open source software, we are collaborating with the software developers of major PET analysis tools (PMOD, SPM, MIAKAT and PETsurfer  23  ) to facilitate rapid adoption and support of this standard, and working with major PET centres to help PET researchers convert their data into the PET-BIDS standard.
Several software tools already exist to convert dicom files and ECAT data into BIDS, such as dcm2niix  24  (https://github. com/rordenlab/dcm2niix), however, the output may need post-hoc editing if required metadata are not available in the imaging header files. The BIDS starter kit is a tool to help researchers get started with the BIDS data structure (https://github. com/ bids-standard/ bids-starter-kit). It consists of a collection of community-driven guides, tutorials, helper scripts, and wiki resources. A tutorial that describes how to create a BIDS-compatible PET data set has been provided on the starter-kit wiki (https://github. com/bids-standard/bids-starter-kit/wiki), and MATLAB (bids-matlab; https://github. com/bids-standard/bids-matlab) and Python (pybids; https://github. com/bids-standard/pybids) packages are also available to produce and/or work with PET sidecar JSON and TSV files.
These packages are freely available on GitHub. According to the guidelines for the content and format of brain PET data  8  , acquired PET data are defined as PET data after reconstruction into 3D or 4D frames. These data may be shared in repositories such as OpenNeuro (https://openneuro. org), which is an open archive for analysis and sharing of public neuroimaging data spearheading the movement of best practices within MRI, MEG, EEG, iEEG, ECoG, ASL and now PET  25. The OpenNeuro platform is the successor of OpenfMRI (established in 2011, https://openfmri. org/), and the project enjoys relatively wide acceptance by the field and capitalizes on the BIDS standard. It has been running for almost a decade and is one of the fastest growing image databases, with about 12 new datasets being added per month.
Data analysis Pipelines and Sharing of Derived PEt Data BIDS also offers the possibility to build fully reproducible analysis workflows using the concept of BIDS applications  26. A BIDS application is a software container capturing all the dependencies of a neuroimaging analysis pipeline (e. g. , fMRIprep) that takes a BIDS formatted data set as input. Each BIDS application has the same core set of command line arguments, making them easy to run and integrate into automated platforms, allowing for full computational reproducibility. Several open-source initiatives for PET are currently under development to BIDS applications, including PETSurfer (https://surfer. nmr. mgh. harvard. edu/fswiki/PetSurfer), APPIAN (https://github. com/APPIAN-PET/APPIAN) and kinfitr (https://github. com/mathesong/kinfitr) providing tools to carry out preprocessing and/or pharmacokinetic modeling of PET data.
We also highly recommend the great resources by the TURKU PET centre (http://www. turkupetcentre. net/), which have been providing thorough documentation and analysis tools for PET for several decades. The resulting outputs from BIDS applications can be shared using BIDS derivatives standards, describing the outputs of common preprocessing pipelines and pharmacokinetic models. The specification for PET-BIDS derivatives (BIDS Extension Proposal 23) is currently under development, and will capture data and metadata sufficient for a researcher to understand and reuse the output of a common PET analysis pipeline, including preprocessing and pharmacokinetic modeling. This article describes the new PET extension to BIDS specifying a structured way of storing PET data and metadata. BIDS is a community-driven project, and the PET-BIDS specification was created in a joint effort made by the PET community (open and with community peer review) aligning with the consensus guidelines for the content and format of PET brain data in publications and archives  8.
• The CIMBI Database [ 11 C]CIMBI-36 PET Example Dataset consists of a single dynamic PET measurement of a pig to measure serotonin 2A receptor availability. Blood and metabolite data are available for this dataset. It was collected as a part of the CIMBI database (https://github. com/bids-standard/bids-examples/tree/master/ pet001). • The CIMBI Database [ 11 C]CIMBI-36 Intervention Example Dataset consists of a single dynamic PET measurement of a pig using bolus-infusion, and with a pharmacological ketanserin intervention during the scan. Blood and metabolite data are available for this dataset (https://github. com/bids-standard/bids-examples/ tree/master/pet004). • The CIMBI Database [ 11 C]AZ10419369 Visual Stimuli Example Dataset consists of two dynamic PET measurements of a single subject using combined PET/MRI.
Genome assembly generation The 10X Genomics Chromium libraries for the species listed above were assembled de novo with the dedicated assembler Supernova2 (70) and the pseudohaploid assembly versions were used for downstream analyses. The PacBio library obtained for Ptiloris intercedens was assembled into primary contigs by the diploid-aware assembler Falcon-unzip  (71). The primary contigs were polished with a round of Arrow (https://github. com/PacificBiosciences/GenomicConsensus), two rounds of Pilon  (72)  and scaffolded with ARCS  (73)  and LINKS (74) using the 10X Genomics Chromium library for the same individual following the methods and parameters in  (27). The PacBio HiFi library for Uraeginthus cyanocephalus was assembled using IPA (https://github. com/PacificBiosciences/pbipa). Uncollapsed haplotypes were removed from the assembly of both P.
SNP and SV diversity To investigate the diversity of SVs and SNPs at the population level, we calculated nucleotide diversity (pi) and structural variant diversity as well as SNP and SV densities. First, we ran pixy  (54)  to calculate the nucleotide diversity within the L. pyrrhopterus population. Pixy is a tool designed to minimise biases in the calculation due to missing genotypes or sites. To use pixy, we first mapped the 10X Genomics libraries of four L. pyrrhopterus females and one male to the L. pyrrhopterus reference genome assembly with Longranger. The obtained alignment files were combined with bcftools (77) to generate a comprehensive VCF file that included the invariant sites following the suggested code given by pixy (https://pixy. readthedocs. io/en/latest/index. html). The VCF file was then used to calculate the nucleotide diversity per window of 100 kb.
Solo/full-length LTR ratio and repetitive element turnover We used available full-length LTR annotations for L. pyrrhopterus and T. guttata from  (36)  and carried out the annotation of solo LTRs using the findSoloLTRs pipeline developed for this study (https://github. com/ValentinaBoP/Wevolution). The findSoloLTRs pipeline took as primary input the RepeatMasker output (. out) file and the genome assembly of interest. The RepeatMasker output file were filtered for retaining only hits belonging to the long terminal repeats of the ERVs that spanned the entire length of the matching consensus sequence (5 bp upstream and 5 bp downstream were given as threshold of completeness). All the hits belonging to the internal portion of the LTR elements, to elements labelled as incomplete (. inc) or "LTR?", and fragmented hits were discarded.
Acknowledgements We thank  Antje Bakker  for technical assistance,  David Witkowski  for animal care, and the  Suh laboratory, Johannesson laboratory ,  Matthias Weissensteiner ,  Reto Burri , and  Marco Ricci  for helpful discussions. This work was supported by the  Kungliga Fysiografiska Sällskapet i Lund (Nilsson-Ehle Donation  to V. P. ), the  Swedish Research Council Vetenskapsrådet  ( 2016-05139  to A. S. ;  621-2014-5113  and  2019-03900  to M. I. ), and the  Swedish Research Council Formas  ( 2017-01597  to A. S. ). Samples for this study were kindly provided by  Museums Victoria in Melbourne (MV) , the  Australian National Wildlife Collection in Canberra (ANWC) , the  Peabody Museum of Natural History and Yale University Museum in New Haven (YPM) , the  Swedish Museum ofs Natural History in Stockholm (NRM) , the  Natural History Museum of Denmark in Copenhagen (ZMUC) , and the  University of Kansas Biodiversity Institute (KU).
Owing to drifting snow processes, snow accumulation and surface density in polar environments are variable in space and time. We present new field data of manual measurements, repeat terrestrial laser scanning and snow micro-penetrometry from Dronning Maud Land, Antarctica, showing the density of new snow accumulations. We combine these data with published drifting snow mass flux observations, to evaluate the performance of the 1-D, detailed, physics-based snow cover model SNOWPACK in representing drifting snow and surface density. For two sites in East Antarctica with multiple years of data, we found a coefficient of determination for the simulated drifting snow of r 2 = 0. 42 and r 2 = 0. 50, respectively. The field observations show the existence of low-density snow accumulations during low wind conditions.
This likely is a result of the underrepresentation of high wind speed and low temperature regimes in those datasets. Therefore, parameterizations tailored to the polar regions have been developed  (Liston and others, 2007 ; Groot  Zwaaftink and others, 2013a). Drifting and blowing snow resulting in new depositions frequently occurs in the absence of precipitation  (Gossart and others, 2017; Palm and others, 2018) , where the source of the drifting snow particles is erosion from the upwind snow surface. This implies that only considering the effect of wind on new snow density during periods of precipitation is insufficient to describe the full influence of wind on surface density. It is also important to note that recent wind tunnel experiments have demonstrated that densification due to wind only occurs under the presence of drifting snow  (Sommer and others, 2017).
In the first part of Section 'Data and methods', we describe already published drifting snow field data we used, as well as newly obtained field data of snow accumulations and snow density. Section 'Data and methods' concludes with a description of the SNOWPACK model and the tested model setups, as well as the atmospheric forcing data. In Section 'Results and discussion', we discuss first the performance of SNOWPACK in diagnosing drifting snow, followed by observations of snow accumulation and a comparison of observed and simulated snow density for the field sites. Section 'Sensitivity study' investigates the sensitivity of SNOWPACK calculated drifting snow mass fluxes to poorly constrained parameters in the model.
Field sites Observational data used here were collected at four field sites. The first two field sites are drift stations D17 (66. 7°S, 139. 9°E, 450 m above sea level (a. s. l. ), 10 km from the coast) and D47 (67. 4°S, 138. 7°E, 1560 m a. s. l. , 110 km from the coast) near the Dumont d'Urville base in Adélie Land, East Antarctica  (Amory, 2020). Adélie Land is situated in a confluence zone of katabatic flow over steep slopes near the coast (see Fig. 1 ), making it among the windiest locations of Antarctica  (Parish and Bromwich, 2007)  with frequent drifting snow events  (Amory, 2020). The two other field sites are located in Dronning Maud Land, East Antarctica. The first of these is located near the Princess Elisabeth Antarctica station (PEA) at 71. 939°S, 23. 315°E and ∼1350 m a. s. l.
For site D47, measurements are available between 9 January 2010 and 27 December 2012. Site D17 was installed on 3 February 2010 and is still operational. Data included here end on 31 December 2018. At PEA, two drifting snow masts were installed 350 m apart, a few kilometres west of the PEA station. These stations measured, among others, wind speeds at two levels (∼1. 1 and 3. 6 m above the snow surface), air temperature and relative humidity (at ∼1. 1 m above the snow surface) and drifting snow, which was measured by Niigata Electric Snow Particle Counters (SPC-95) recording the number and size of saltating particles  (Sato and others, 1993). The SPCs can rotate and vanes on the devices ensure that the sensors remain aligned with the wind direction. Sommer and others (2018)  describe the data processing for these field sites in more detail.
The data from the drifting snow stations are published in  Wever and others (2018). SPC data are available between 15 December 2016 and 22 December 2017 with some data gaps. A 10 m wind speed time series was constructed in a similar procedure as for the D17 and D47 sites, but assuming a roughness length of 7. 5 × 10 -3 m, as reported for this field site  (Sommer and others, 2018) , instead of using the parameterized roughness length. As shown in Table  S1  in the Supplementary material, we also include wind speed data recorded by the automated weather station (AWS) installed at PEA by the Institute for Marine and Atmospheric research Utrecht (IMAU), the Netherlands (  Reijmer and Oerlemans, 2002; Jakobs and others, 2020) , to extend the length of the time series. When referring to in situ wind speed, we use the IMAU AWS data for the period from 13 January 2010 until 15 January 2018.
After the TLS scans on 4 and 11 January 2019, an undisturbed transect was chosen. Using a measurement tape, SMP measurements were taken at regular (∼90 cm) spacing. To exactly locate the SMP transect acquired on 4 January in the scan, the first and last SMP measurements were marked with bamboo poles. These bamboo poles are visible in the scan from 11 January and provide exact coordinates of the first and last SMP measurement of the transect. The second SMP transect was acquired after the last scan, but here, locating the SMP transect inside the TLS field was done by measuring the distance to reference bamboo poles put in before scanning. The laser scanning data from PEA and HAM are published in  Wever and others (2018)  and  Wever (2022) , respectively.
Atmospheric forcing We drive the simulations with hourly data of near-surface air temperature, relative humidity, wind speed, surface incoming shortwave and longwave radiation and precipitation, from the gridcell closest to the respective study sites from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2, Gelaro and others, 2017). Among other atmospheric reanalyses, MERRA-2 has relatively low bias in accumulation rates in Antarctica (  Gossart and others, 2019 ;  Medley and Thomas, 2019). Gossart and others (2019)  found that the accuracy of MERRA-2 wind speed is comparable to other models. Also,  Keenan and others (2021)  show that MERRA-2 does not tend to consistently over-or underestimate the highest wind speeds. Note that these evaluations have been performed using AWSs, which generally are installed in locations that are representative for a larger area, consistent with the relatively coarse spatial resolution of MERRA-2.
However, the area near PEA, for example, is characterized by strong topographic variability. Also ice rises exhibit small-scale variability in wind speed and accumulation when comparing windward and lee slopes, which could be captured in models with a higher resolution than MERRA-2 (  Lenaerts and others, 2014). To further illustrate this, Figures  3b, d  show that for PEA, MERRA-2 wind speed is higher than the observed wind speed and exhibits reduced temporal variability. This notion motivated us to also run simulations where we used in situ observed wind speed instead of MERRA-2 wind speed when available, while using MERRA-2 for the other variables and using MERRA-2 wind speed outside the observational period. Since observational data from Antarctica are relatively sparse, often reanalyses are used as meteorological forcing.
By comparing both MERRA-2 with in situ data-driven simulations, we provide a first estimate of the uncertainties introduced by using forcing data from an atmospheric model. Even though we discussed earlier that MERRA-2 represents annual SMB adequately, MERRA-2 modelled SMB is the result of several processes, for the Antarctic ice Sheet most importantly precipitation and surface sublimation. This means that a correct SMB can also be achieved when both precipitation and surface sublimation are correspondingly over-or underestimated. Additionally, MERRA-2 does not consider drifting snow, and the associated substantial drifting snow sublimation that can occur  (Schmidt, 1982b ;  Wever and others, 2009). Neglecting drifting snow sublimation is partly compensated for by a dryer atmospheric boundary layer and enhanced surface sublimation  (Bintanja, 2001).
This occurs at smaller scales than captured by the MERRA-2 grid. Similarly, it is expected that the heterogeneous surroundings of PEA at scales smaller than the MERRA-2 grid cause local deviations in accumulation. From literature, it is clear that this could be overcome by using downscaling techniques for wind speed (e. g. Reynolds and others, 2021) , combined with spatially explicit drifting snow models, such as SnowTran-3D (  Liston and others, 2007)  or Alpine3D with explicit drift (  Lehning and others, 2008). To avoid the complexity and added computational costs of such models, we choose to bias-correct MERRA-2 precipitation when available field data allows, as we will now discuss. For precipitation, our field data containing both depth and density allow to estimate accumulated mass (see Figs  4a  and 3a, c ).
In Figure  5 , we show periods without precipitation in MERRA-2 (defined as <0. 001 kg m -2 h -1 ) in orange colour and different marker. The results show that the highest saltation mass transport in SNOWPACK and the highest observed drifting snow mass transport are under the presence of precipitation. Furthermore, the SNOWPACK calculated saltation mass transport has lower correspondence with observed drifting snow mass transport for periods without precipitation than for all a b Fig. 4. Meteorological data for the HAM field site from 27 December 2018 to 12 January 2019 for (a) bias-corrected cumulative precipitation, observed accumulated mass from density and depth measured using, respectively, SMP and TLS, and air temperature from MERRA-2. (b) MERRA-2 wind speed and modelled eroded and subsequently redeposited snow.
Data. MeteoIO and SNOWPACK are software published under a GNU LGPLv3 license by the WSL Institute for Snow and Avalanche Research SLF at https://gitlabext. wsl. ch/snow-models. The repository used to develop the model code used in this study can be accessed via https://doi. org/10. 5281/ zenodo. 3891845. The exact model source code used in this study corresponds to commit 6fdc98f, and can be accessed via https://doi. org/10. 5281/zenodo. 6629078. Data from the drift stations D17 and D47 have been published in  Amory and others (2020). Data from the PEA field campaign have been published in  Wever and others (2018). The TLS data from the HAM field site have been published in  Wever (2022). The snow pits and SMP data obtained at the PEA and HAM field sites have been published in  Wever and others (2022).
Acknowledgements. We acknowledge the  International Polar Foundation  for excellent logistical support during the field campaigns at PEA and HAM. The drifting snow observations from  Adélie Land ,  East Antarctica  ( Amory and others, 2020) would not have been possible without the financial and logistical support of the  French Polar Institute IPEV  (programme  CALVA-1013 ). We acknowledge the  Polar Geospatial Center of the University of Minnesota  for providing the Antarctic DEM (REMA). We thank  Ghislain Picard ,  Jessica Lundquist  and three anonymous reviewers for their constructive comments that helped to improve the manuscript considerably. During the field campaign at HAM,  Nander Wever  was supported by the  Swiss National Science Foundation (SNSF)  grant number  P2ELP2 172299 , whereas the later data analysis was funded by the  BELSPO Research Contract , grant  BR/165/A2:Mass2Ant  and the  National Aeronautics and Space Administration  under Grant No.