dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction It is well established that star formation takes place in dense molecular clumps embedded in large molecular clouds. The evo-This publication is based on data acquired with the Atacama Pathfinder Experiment (APEX), project 098. F-9304. APEX is a collaboration between the Max-Planck-Institut für Radioastronomie, the European Southern Observatory, and the Onsala Space Observatory. The temperature and column density maps are available in electronic form at the CDS via anonymous ftp to cdsarc. u-strasbg. fr (130. 79. 128. 5)  or via http://cdsweb. u-strasbg. fr/cgi-bin/qcat?J/A+A/ lution of these dense clumps towards the onset of gravitational collapse and the formation of solar-type stars is relatively well understood in outline, but several key questions remain unanswered, including how the star formation rate and efficiency depend on the environmental conditions and whether there are notable differences in the physical processes leading to the formation of low-mass and high-mass stars.
2011)  or in CO molecular lines  (Kong et al. 2018; Suri et al. 2019). The Orion Nebula Cluster (ONC) appears approximately projected on the middle of this dense filament. On large physical scales (0. 05 to 8. 5 pc),  Stutz & Gould (2016)  concluded from the analysis of Herschel data at ∼36 angular resolution that the radial distribution of the gas surface density near the ISF follows a power law. At much higher angular resolution (4. 5 ), using data in the high-density tracer N 2 H + (1-0) line obtained with the Atacama Large Millimeter/Submillimter Array (ALMA),  Hacar et al. (2018)  argued that the ISF contains a large number of fibres organised in a complex network. They measured emission profiles characterised by full widths at half maximum (FWHMs) in the range 0. 015-0. 065 pc, with a median value of 0.
ArTéMiS data reduction The ArTéMiS data at 350 and 450 µm were processed with the dedicated IDL pipeline, which takes care of converting the raw data to IDL-friendly data structures, applying flux calibration, and rejecting unusable pixels. Special care was taken to correct for possible pointing errors between individual maps before a combined map could be computed. Then, the data were processed with a tailored version of the Scanamorphos software adapted for ArTéMiS data  (Roussel 2013 (Roussel , 2018)). This pipeline makes use of the redundancy in the data to properly separate the various sources of noise (especially sky noise) from the real signal. This allows us to recover emission at scales that are usually filtered out by the typical data reduction of ground-based bolometer observations because such emission mimics the sky emission.
Conclusions We have presented 350 and 450 µm observations with ArTéMiS of the ISF in Orion, the nearest site of active high-and intermediate-mass star formation. With angular resolutions of 8 and 10 , the ArTéMiS data, combined with Herschel-SPIRE maps to recover large-scale emission, probe scales from 0. 015 pc to a few pc. By combining the new ArTéMiS data with Herschel data at shorter wavelengths, we were able to build, for the first time, high-resolution temperature and column density maps covering a large region around OMC-1 that are not affected by saturation; these maps are publicly available from the CDS 1. We extracted the radial profiles and intrinsic widths of several segments of the Orion ISF from the high-resolution column density map. Our conclusions can be summarised as follows.
Acknowledgements. We thank the anonymous referee for their detailed report, which helped us improving the robustness and the presentation of our results. We are very thankful for the continuous support provided by the  APEX  staff during ArTéMiS operations. FS acknowledges support from a  CEA/Marie Sklodowska-Curie Enhanced Eurotalents fellowship. DA acknowledges support by  FCT/MCTES  through national funds (PIDDAC) by the grant  UID/FIS/04434/2019. PP acknowledges support from  FCT  through the research grants  UIDB/04434/2020  and  UIDP/04434/2020. PP receives support from fellowship  SFRH/BPD/110176/2015  funded by  FCT (Portugal)  and  POPH/FSE  (EC). Part of this work has received support from the  European Research Council  under the European Union's  Seventh Framework Programme  ( ERC Advanced Grant  Agreement no.
291294 -ORISTARS ), from the  French National Research Agency  (Grant no. ANR-11-BS56-0010 -STARFICH ), and from  "Ile de France" regional funding (DIM-ACAV+ Program). We also acknowledge support from the  French national programs of CNRS/INSU  on stellar and ISM physics (PNPS and PCMI). The present study has made use of data from the  Herschel Gould Belt survey (HGBS)  project (http://gouldbelt-herschel. cea. fr). The HGBS is a Herschel Key Programme jointly carried out by SPIRE Specialist Astronomy Group 3 (SAG 3), scientists of several institutes in the  PACS Consortium (CEA Saclay, INAF-IFSI Rome and INAF-Arcetri ,  KU Leuven, MPIA Heidelberg ), and scientists of the  Herschel Science Center (HSC). This document was prepared using the Overleaf web application, which can be found at www.
'ACP' nations of sub-Saharan Africa, the Caribbean and the Pacific. Our project's funding runs from April 2021 to April 2023. At this stage of the project, we are working on defining the information technology (IT) architecture (Fig. 1 ) and selecting the tools that we will be using to achieve our goals. In the talk, we will present our conclusions through architecture schemas and tools demonstrations. Each of the 6 countries will have its own PostgreSQL database, storing its data. They will also have access to the RIHA data management platform (Réseau Informatique des Herbiers d'Afrique / Digital Network of African Herbaria). This is a web application, developed in PHP, allowing full management of the data by herbarium administrators (Fig. 2 ). An Integrated Publishing Toolkit (IPT) will fetch these herbaria data from the databases, create the Darwin Core archives, and connect these data automatically to gbif.
org on a periodic basis (Fig. 3 ). On the databases, we will use a PostgreSQL view to ease conversion from the RIHA data model to the Darwin Core model. On the IPT, we will create one dataset per country, linked to each PostgreSQL view. The SQL query will be configured to only fetch validated data, depending on the herbarium administrator's validation in the RIHA platform. The automatic and periodic data transmission to gbif. org is a feature available in the IPT, and recently improved by the GBIF France team, which contributes to the IPT development.
Overall architecture Another part of the automatic data workflow will be to feed a Living Atlases portal for the West and Central African herbaria. This web application will allow public users to search, display and download herbaria data from West and Central Africa (Fig. 4 ). Internally, this Living Atlases application will reuse open source modules developed by the Atlas of Living Australia (ALA). The application is mainly written in Java, uses JQuery/ Bootstrap for the interface and relies on SolR and Spark in the backend. It has been developed to be easily reusable, by only modifying configuration and doing web customization (HTML / CSS), hiding most of the backend technological complexity.
Herbarium data to GBIF The automatic data workflow will transfer datasets generated by the IPT, in Darwin Core Archive format, to the Living Atlases portal backend. A technical task orchestrator, yet to be selected, will implement this feature. Living Atlases subportals, limited to data of one participating country, could be easily set up, the existing backend resources (Fig. 5 ). One of the benefits of the Living Atlases portal is that we can easily deploy additional front end applications with limited data, configured by a filter (here, a filter on the data owner country). Only configuration and web customization (HTML / CSS) are required. All the.
, 2013; Amory et al. , 2015)  in coastal East Antarctica. Unpublished preliminary experiments with former physical parameterizations of drifting snow in MAR did not lead to a continent-wide agreement between model simulations and both drifting-snow and SMB observations. As a result, drifting snow has been kept disabled in recent decadelong investigations of the SMB of the Greenland (e. g. Fettweis et al. , 2017 Fettweis et al. , , 2020) )  and Antarctic (e. g. Kittel et al. , 2018 Kittel et al. , , 2021;; Agosta et al. , 2019)  ice sheets with MAR, despite the potentially missing aspects related to the important feedback mechanisms induced by drifting snow. In this paper a modified version of the original driftingsnow scheme implemented in MAR is assessed through a concurrent evaluation of the drifting-snow climate and SMB reproduced by the model against a multi-year database of drifting-snow mass fluxes and SMB estimates.
The evaluation focuses on the marginal slopes of Adélie Land, a katabatic wind region of East Antarctica which experiences drifting snow frequently  (Amory, 2020a)  and where the SMB exhibits a high spatial variability related to drifting-snow processes  (Agosta et al. , 2012). The coupled atmospheric and snowpack components of MAR are presented in Sect. 2. The drifting-snow scheme is described in Sect. 3 together with the new developments and main changes relative to the original version. Section 4 provides information on the study area, the available data and the evaluation strategy. The modelled nearsurface climate, drifting-snow frequency, mass transport and SMB are evaluated in Sect. 5. The sensitivity to the model version and input parameters of the drifting-snow scheme are discussed in Sect.
, 2015). In this study MAR version 3. 11 in its Antarctic setup  (Agosta et al. , 2019)  is used with the updates described in  Kittel et al. (2021) , simply referred to as MAR hereafter. The simulations are performed on a grid of 80 × 80 cells at 10 km horizontal resolution to reduce computational cost and facilitate development and sensitivity experiments. The time step is set to 60 s, for a computational cost of 72 CPU hours per year of simulation in the chosen configuration. The topography is obtained through aggregation of the 1 km Bedmap2 surface elevation dataset  (Fretwell et al. , 2013). The model is driven at its lateral boundaries (pressure, wind speed, temperature, specific humidity), at the top of the troposphere (temperature, wind speed) and at the ocean surface (sea ice concentration, sea surface temperature) by 6-hourly ERA5 reanalysis fields  (Hersbach et al.
, 2012). The performance of MAR in reproducing the driftingsnow climate of Adélie Land is evaluated against 2 m wind speed and direction, air temperature, and air relative humidity observations collected at two locations 100 km apart, D47 and D17 (Fig. 1 , Table  1 ). Data at half-hourly intervals are available from automatic weather stations installed at both sites and operated by the Institut des Géosciences de https://doi. org/10. 5194/gmd-14-3487-2021 Geosci. Model Dev. , 14, 3487-3510, 2021 l'Environnement (IGE) over the periods 2010-2012 for D47 and 2010-2018 for D17  (Amory et al. , 2020). All datasets are reported as quality-controlled. At each station relative humidity is originally given with respect to water and has been converted to be expressed with respect to ice in subfreezing conditions following  Goff and Gratch (1945)  formulae and using the 2 m temperature record in the conversion.
Climate variables extracted from the lowest model level (2 m) and the nearest grid cell to the observation location are used for comparison. Half-hourly meteorological data also include driftingsnow mass fluxes measured almost continuously over their respective observation periods using acoustic secondgeneration FlowCapt™ sensors (hereafter referred to as 2G-FlowCapt™). The instrument consists of a 1 m long tube containing electroacoustic transducers that convert the acoustic vibration caused by the drifting-snow particles colliding with the tube into a snow mass flux integrated over the exposed length of the tube. At D47, two 2G-FlowCapt™ sensors were installed and superimposed vertically to sample the first 2 m above ground and detect the onset of drifting snow. Site D17 was initially equipped with only one 2G-FlowCapt™ set up close to the surface and completed with a second instrument in December 2012 to match the configuration of D47.
As snow particles directly originating from clouds cannot be discriminated from saltating and/or suspended snow particles relocated from the ground, measured snow mass fluxes account for all forms of drifting snow along the sampling height. The IGE database then enables the evaluation of simulated drifting snow and the relative climate against time-averaged measurements of bulk flow and mass flux conditions, consistently with the steady-state driftingsnow physics implemented in MAR. An extensive description of the drifting-snow and meteorological equipment at D47 and D17 can be found in  Amory (2020a). Thorough evaluation of drifting snow requires consistency between observed and modelled drifting-snow mass fluxes. Near-surface snow mass fluxes simulated by MAR can be inferred from q s at the lowest vertical model level (2 m), which, similarly to the 2G-FlowCapt™, does not distinguish the origin of particles.
, 2015). Modelled SMB is compared with observations obtained from annual measurements of 91 snow stakes distributed every ∼ 1. 5 km along a 150 km long transect (see  Agosta et al. , 2012  for description) that extends from the coast up to 1800 m a. s. l. and crosses the locations of D17 and D47. The SMB dataset includes observations collected over the period 2004-2018 and covers the strong SMB gradient existing between the coast and relatively drier and colder conditions inland. All annual observed values contained in each model grid cell (10 × 10 km 2 ) are averaged to produce a mean observed SMB value per grid cell.
https://doi. org/10. 5194/gmd-14-3487-2021 Geosci. Model Dev. , 14, 3487-3510, 2021. Table  3. Observed and simulated total drifting-snow transport (10 6 kg m -2 ) from 0 to 2 m above surface at D47 and D17. A distinction is made between total snow transport computed from observed (OBS) and simulated (MAR obs ) near-surface driftingsnow mass fluxes cumulated over each drifting-snow event identified in the database and over each simulated drifting-snow event (MAR sim ). Station OBS MAR obs MAR sim D47 1. 51 1. 84 1. 88 D17 5. 25 4. 92 5. 4 Table  3  compares the total snow transport during observed drifting-snow events (OBS) to that estimated by the model during these observed events (MAR obs ) and during every simulated events (MAR sim ). Referring only to observed events, MAR respectively overestimates (+21.
Conclusion A dataset of drifting-snow and SMB observations collected in Adélie Land has been used to evaluate the latest version of the regional climate model MAR (v3. 11) equipped with a new drifting-snow scheme. Modifications and developments relative to the previous drifting-snow scheme mainly consisted in (i) computing the roughness length z 0 as a varying function of air temperature and disabling the drag partition scheme and its inhibiting effect on snow erosion, (ii) rewriting the parameterization of the threshold friction velocity for snow erosion u * t as a function of surface snow density only, (iii) differentiating snow density at deposition between precipitation and drifting snow, (iv) including a simple parameterization for drifting-snow compaction of the uppermost snowpack layer, and (v) restricting erosion within one model time step to the uppermost snowpack layer only.
Acknowledgements. This work would not have been possible without the financial and logistical support of the  French Polar Institute IPEV  (programme  CALVA-1013  and  GLACIOCLIM-SAMBA-411 ). The authors thank all the on-site personnel in  Dumont d'Urville and Cap Prud'homme  for their precious help in the field, in particular  Philippe Dordhain  for electronic and technical support. Luc Piard  and  Christophe Genthon  are also acknowledged for their investment in collecting data and maintaining the observation system in Adélie Land, as well as  Hubert Gallée  for fruitful discussions. Computational resources have been provided by the  Consortium des Équipements de Calcul Intensif (CÉCI) , funded by the  Fonds de la Recherche Scientifique de Belgique (F. R. S. -FNRS)  under grant no. 2. 5020.
11 ,and the Tier-1 supercomputer (Zenobe) of the  Fédération Wallonie-Bruxelles  infrastructure funded by the  Walloon Region  under grant agreement no. 1117545. This is PROTECT contribution no. 16. Financial support. This research has been supported by the  Fonds de la Recherche Scientifique de Belgique , the  TROIS-AS  project ( ANR-15-CE01-0005-01 ) and  PROTECT , a project which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 869304. Review statement. This paper was edited by  Steven Phipps  and reviewed by two anonymous referees.
Code and data availability. The meteorological and drifting-snow data can be downloaded at https://doi. org/10. 5281/zenodo. 3630497 (Amory et al. , 2020). The surface mass balance data are available at http://pp. ige-grenoble. fr/pageperso/faviervi/stakeline. php  (Favier, 2021). This service is provided by the National Observation Service GLACIOCLIM (https://glacioclim. osug. fr/?lang= en, last access: 23 May 2021) piloted by the OSUG (Observatories of Earth Sciences and Astronomy of Grenoble, France). The National Observation Service GLACIOCLIM have requested that we make the data available through the dedicated web page hosted by the OSUG website. However, we have also archived the specific version of the dataset used in this study together with the MAR source code and outputs for replication of this study at https://doi.
org/10. 5281/zenodo. 4314872 (Amory, 2020b). The MAR source code is tagged as v3. 11. 3 on https://gitlab. com/ Mar-Group/MARv3. 7 (MAR Team, 2021). See http://www. mar. cnrs. fr for more information about downloading MAR (last access: 23 May 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-14-3487-2021-supplement. Author contributions. ChA, CK and XF designed the study. ChA and VF collected data on the field. ChA implemented model developments and updates with the help of CK and XF. ChA ran the simulations, performed the analysis and wrote the first draft of the manuscript. ChA, CK, LLT, CeA, AD, VF and XF discussed and revised the manuscript.
Protein concentration was determined by BCA protein assay kit (Thermo Fisher Scientific/Pierce, Rockford, IL) following the manufacturer's instruction. A 25 µg aliquot of extracted proteins of each sample was then subjected to reduction, alkylation, and tryptic digestion. ABC (50 mM)-SDC (0. 5%) solution was first added to samples to maintain a volume of 50 µL. Proteins were thermally denatured at 80 • C for 10 min. The reduction of proteins was performed by adding a 1. 25 µL aliquot of 200 mM dithiothreitol (DTT) solution and incubating at 60 • C for 45 min. The reduced proteins were then alkylated by adding a 5 µL aliquot of 200 mM iodoacetamide (IAA) solution and incubation at 37 • C in the dark for 45 min. To quench the excessive IAA, a 1. 25 µL aliquot of DTT solution was added again, and samples were FIGURE 1 | Unsupervised analysis of all proteins in the dataset.
LC-MS/MS Data Analysis The raw data obtained from LC-MS/MS analysis were processed with the MaxQuant software version 1. 6. 3. 4. Database search was performed against UniProtKB/Swiss-Prot mouse database. The search included cysteine carbamidomethylation as a fixed modification and variable modifications, including methionine oxidation and acetylation of protein N-terminal. Trypsin was specified as the proteolysis enzyme, and a maximum of two missed cleavages were allowed. For identification, the peptide precursor mass tolerance was 20 pm in the first search and 4. 5 pm in the main search. As for fragments matching, a deviation of 0. 5 Da was allowed. Only peptides with a minimum length of seven amino acids were considered for identification. The false discovery rate was set to be 0. 01 at both peptide and protein levels.
Data Preparation The proteomics dataset, containing 2,902 protein identifiers, was preprocessed and normalized using Bioconductor's DEP package in R environment  (Zhang et al. , 2018). A total of 187 identifiers were omitted prior to normalization, including 30 decoy database hits, 19 contaminants, 26 non-specific identifiers (mapped to multiple proteins), and 112 unmapped identifiers (uniport ID not mapped to any gene in Bioconductor's Genome wide annotation for Mouse database). In addition, 1,126 proteins not present in all replicates of at least one condition were also removed (Supplementary Figures  1A-C ). The filtered dataset, containing 1,589 protein IDs, was background corrected and normalized by variance stabilizing transformation (Supplementary Figures  1D, E ). A heatmap of the presence status, for proteins with at least one missing value, showed a clustering pattern by condition (Supplementary Figure  2A ), and their intensity distribution and cumulative fraction plots showed that missing values are enriched in proteins with low signal (Supplementary Figure  2B ).
Enrichment Analysis Enrichment analysis on Gene Ontology (GO) biological process (BP), molecular function (MF), and cellular component (CC) terms was performed in R using topGO package 1. GO terms were considered as significant when having a fold of enrichment greater than 2 with weighted-Fisher p < 0. 05. Top enriched terms alongside corresponding proteins were visualized in Circos plots using circlize package in R environment  (Gu et al. , 2014). Enrichment analysis on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was conducted in R environment using BioConductor's clusterProfiler package  (Yu et al. , 2012).
Data Visualization Principal component analysis (PCA) plots and heatmaps were drawn in R using Bioconductor's DEP and ComplexHeatmap  (Gu et al. , 2016)  Packages, respectively. Boxplots were drawn using ggplot2 R package 2. The interaction network of all deregulated proteins and the network of enriched KEGG pathways were visualized in Cytoscape software  (Shannon et al. , 2003)  using stringApp  (Doncheva et al. , 2019) , and ClueGO  (Bindea et al. , 2009)  applications, respectively.
WGCNA Signed WGCNA with soft thresholding of 12 was implemented to construct a gene network using topological overlap dissimilarity measures. Identification of modules containing positively intercorrelated genes was done using dynamic tree cutting with deep slit relying on the shape of the branches in the dendrogram. Analysis was performed using the WGCNA package in R environment  (Langfelder and Horvath, 2008). The gene-gene interaction network was visualized using Cytoscape software  (Shannon et al. , 2003).
Pathways Contributing to Variance Among Conditions The proteome of a mouse model of PD was investigated after intranigral transplantation of fetal DA neurons in a 6-OHDA mouse model of PD, using LC-MS/MS. Proteins were analyzed in five nigrostriatal-related brain regions (CPu, MFB, NAcc, Thal, and VM) at 1 day (SNLTD1 group) or 7 days (SNLTD7 group) after cell grafting, in comparison to sham-transplanted mice (Figures  1A, B ). PCA was performed based on the expression level of all proteins in the dataset and showed that most of the variance between samples could be attributed to differences in tissue of origin (PC1), followed by time of transplantation (PC2) (Figure  1C ). Indeed, this was confirmed when performing hierarchical clustering where samples from the same tissue were found to cluster together (Figure  1D ).