dataesr/scientific-paragraphs-categorization huggingface dataset:
(J) Dot plot showing fold of enrichment (x-axis) and the -log 10 p-value (y-axis) of enriched pathways following KEGG database analysis. Hierarchical clustering and heatmap showing relative protein expression values (Z-score transformed LFQ protein intensities) for proteins with significantly different expression in WT and Mrc1 -/-mice (non-alcoholic fatty liver disease, NAFLD (K) and other relevant pathways (L),FDR < 0. 05). (M) Glucose tolerance test (GTT) performed at 20 weeks of HFD feeding in WT and Mrc1 -/-mice and calculated AUC. (N) Insulin tolerance test (ITT) performed at 20 weeks of HFD feeding in WT and Mrc1 -/-mice and calculated AUC. Blood glucose levels are shown. Glycaemia was measured before i. p. glucose or insulin injection (t = 0, baseline values) and after 20, 40, 60, and 120 min.
, 2006) , has long been known to cause major shifts in global weather patterns and therefore produce strong interannual variations in global sea-level, waves, rainfall and continental freshwater flux to the ocean  (Ranasinghe et al. , 2004; Harley et al. , 2010; Sprintall, et al. , 2020; Odériz, et al. ,2020) , even far from its dominant region of influence  (Yeh et al. , 2018; Taschetto, et al. , 2020). However, the linkages between ENSO and the key drivers of shoreline change at global scale has not yet been investigated in detail. Here, we aim to address this knowledge gap by combining a new global dataset of satellite derived monthly shorelines spanning around two decades (2000 to 2017) with global data sets of historical sea-level change (at the coastline), waves and fluvial inputs. Through this analysis, we gain unprecedented insights into the relative contributions of ENSO driven variations in these 3 drivers to observed annual shoreline change globally, and how these contributions vary regionally.
Shoreline change The global dataset used in this study was re-sampled equidistantly with transects spaced at 0. 5 o intervals, following the same vector as in  Almar et al. , 2021. The initial coastline dataset used is Global Self-consistent Hierarchical High-resolution Geography (GSHHG version 2. 3. 6 August 17;  Wessel and Smith, 1996)  to define locations along the world shorelines. The world was divided into computational regions using a series of GSHHG coastline polygons. The shoreline evolution dataset from 2000 to 2017 was extracted at 14,140 coastal points using monthly composites from multiple satellite acquisitions provided by the Landsat missions 5, 7 and 8. The extraction of these data was performed on the Google Earth Engine platform  (Gorelick et al. , 2017). Normal Difference Water Index (NDWI) maps were derived from satellite images and the NDWI threshold used was 0.
5  (McFeeters, 1996). The identified pixels correspond to sea for NDWI > 0. 5, and to land surfaces for NDWI< 0. 5. The coastline is then identified as the interface between the land and sea surfaces. The GEE computation was done over 30 ROIs of varying sizes, covering coastal areas worldwide (and 70% of the globe). Counting that we use T1_8DAY_NDWI collections from Landsat 5, 7 and 8 satellites, this represents monthly median composites of 10. 5 images (i. e. 3 or 4 images depending on the month x 3 satellites) of size 0. 70 x global area at a resolution of 30 m, an approximate 400 Megapixels were processed and considering arrays in 16 bit as are the Landsat data, that makes that we used approximately 3 Petaoctets of satellite data and 7200 hours of computation. Figure  5  shows a comparison between the closest satellite transect and different in situ measurements around the world representative of various beach types.
For Torrey Pines (California, USA, Figure  5b ) and Truc Vert (South West France, Figure  5c ) in situ data were based on topo-bathymetry measurements  (Castelle et al. , 2020; Ludka et al. , 2019) , while shorelines obtained from camera systems were used at the other study sites for Grand Popo (Benin, West Africa, Figure  5d ; from  et al, 2020)  and Nha Trang (Vietnam, Figure  5e ; from  Thuan et al. , 2019). For all sites, the ground truth data were interpolated to a monthly resolution, and comparisons were made for periods where no significant gaps were present in the in situ data. Despite the coarse resolution of our dataset (transect every 0. 5 o ), the comparison with the local measurement closest to our transect (varying from ∼5Km up to ∼22 Km) showed good agreement, being able to reproduce well seasonal to interannual patterns.
Sea level, waves and rivers Sea level was computed at the coastal points situated along the open coasts of the world. Regional sea level anomaly (SLA) was derived at each computational profile from satellite altimetry sea level time series using the SSALTO/DUACS multimission data  (Pujol et al. , 2016)  In addition, Dynamical Atmospheric Corrections (DAC, or storm surge) were taken from a global application of the hourly MOG2D-G non-structured grid model outputs  (Carrere et al. , 2016) , forced by surface winds and atmospheric pressure from the ERA-interim reanalysis  (Dee et al. , 2011). The offshore wave energy flux, proportional to and here directly taken as Hs 2 xTp where Hs is the significant wave height and Tp the swell peak period  (Mentaschi, et al. , 2017)  was extracted from ERA5  (Hersbach et al, 2020) , developed by the European Centre for Medium-Range Weather Forecasts model (ECMWF), at 0.
Every spin-up is a recycling of the preindustrial forcing over 1000 years,. We use driving datasets for historical concentrations of greenhouse gases  (Meinshausen et al. , 2017) , projected concentrations of greenhouse gases  (Esgf, 2018) , emissions  (Iiasa, 2018a; Gidden et al. , 2019; Hoesly et al. , 2018 ), land-use (Luh2, 2018) , solar activity  (Matthes et al. , 2017) , volcanic activity  (Zanchettin et al. , 2016)  and the land-only climate climatology for LUMIP experiments  (Lawrence et al. , 2016). The extensions of scenarios are not those that were initially foreseen  (O'neill et al. , 2016) , but those that have effectively been used during the CMIP6 exercise  (Meinshausen et al. , 2019). The volcanic aerosol optical depth has been treated to scale and extend AR5 volcanic radiative forcing  (Ipcc, 2013) , to comply with the requirement of OSCAR to have a radiative forcing as driver for this contribution.
, 2021)  from the HadCRUT 4. 6. 0. 0 dataset  (Morice et al. , 2012). To constrain the carbon cycle, the CMIP5 cumulative compatible fossil fuel emissions over the concentrations-driven historical and 4 RCPs are used  (Ciais et al. , 2013b). For now, the OSCAR v3. 1 model is calibrated on CMIP5, which motivates the use of these compatible emissions, and not those of CMIP6. Besides, an initial set of constraints based solely on observations had revealed that using projections helped the overall constraining round, thanks to the larger perturbation in the scenarios than in the historical period. To further constrain the partitioning of the carbon sinks between land and ocean, we use data on the cumulative net ocean to atmosphere flux of CO2 over 1750-2011  (Ciais et al. , 2013b). For every constraint, we extend a method already used with OSCAR but with only one constraint  (Gasser et al.
The land carbon stock in 2010 is dominated by the increased land sink and amounts to a small increase of -17 ± 13 PgC in the land. Comparing the latter value with the total change in land carbon in the reference experiment suggests starting simulations in 1850 instead of 1700 or 1750 introduces a non-negligible bias in the CMIP6 exercise. ScenarioMIP  (O'neill et al. , 2016)  chose eight particular SSPs taken from the SSP scenario database  (Riahi et al. , 2017)  to cover a range of socio-economic assumptions and climate targets, and then harmonised them to become the default CMIP6 scenarios to be run by ESMs  (Gidden et al. , 2019). ScenarioMIP mostly required concentration-driven simulations up to year 2100 or sometimes 2300, however, which was complemented in RCMIP by extending all scenarios up to 2500 and systematically running emission-driven simulations in addition  (Nicholls et al.
Acknowledgements TG and YQ acknowledge support from the  European Research Council Synergy  project " Imbalance-P " (grant  ERC-2013-SyG-610028 ). This work is also part of the  European Union 's  Horizon 2020 research and innovation project  " CONSTRAIN " (grant # 820829 ). This work was also partially funded by the  Austrian Science Fund FWF  project " ERM " (grant  P31796-N29 ). The simulations were performed on the ebro cluster at IIASA. We also thank  Zebedee Nicholls  for providing us with the timeseries of global surface air temperature of  CMIP6.
Code and data availability The source code of OSCAR is available at https://github. com/tgasser/OSCAR. Results for some of the experiments performed are available on the repository of RCMIP phase 2 (https://doi. org/10. 5281/zenodo. 4269711). Additional data and scripts are available upon request to the corresponding author. https://doi. org/10. 5194/gmd-2021-412 Preprint. Discussion started: 6 January 2022 c Author(s) 2022. CC BY 4. 0 License.
1B ). For example, the relative abundance of rRNA reads from the Betaproteobacteriales within the Gammaproteobacteria class (a modification in the SILVA 132 database) decreased greatly (27-fold difference at the extremes of the SWI), while Bacteroidetes increased with soil depth (13-fold difference at the extremes). We then examined the active bacterial community composition at the family and genus level for a higher taxonomic resolution (Figs. S2  and 1C ). Sequences representing 44 bacterial genera (each with a relative abundance ≥1%) accounted for 77% of all total sequence and were clustered into three different groups reflecting different distribution patterns (Fig. 1C ). Bacterial genera within group 1 generally had their lowest relative abundance of 16S rRNA transcripts in the top 0-6 mm and were dominated by strict or facultative anaerobic bacteria such as representatives of the Ruminococcaceae family, Anaeromyxobacter and Oxobacter genera.
ACKNOWLEDGEMENTS This work was supported by the  National Natural Science Foundation of China  (No. 41977320 ). We thank  Prof. John Boyle  ( School of Environmental Sciences, University of Liverpool, Liverpool, United Kingdom ) and  Dr. Xue-Ping Chen  ( School of Environmental and Chemical Engineering, Shanghai University, Shanghai, China ) for the valuable comments on this paper. We also thank our lab technicians  Ms. Xiao-Ping Xie ,  Ms. Wen Jiang , and  Ms. Xiao Zhou  for their assistance in the lab.
Value of the data • This dataset provides an overview of the impact of lignocellulosic filler size, composition and content on the final performance of PHBV-based biocomposites based on the characterization of a total of 88 formulations stemming from different research projects • Such compiled dataset allows to highlight which filler parameters are impacting the most the final performance of biocomposites, with greater robustness than in each isolated study • These data can be used to produce tailored-made biocomposites based on a reverse engineering approach • All the stakeholders of the biocomposites field can benefit from this data, from the manufacturers to the end-users • This dataset is described by a reusable domain ontology enriched with specific vocabulary from the biocomposites domain.
Data description Data are stored in two datasets (see Data accessibility in the Specification Table  above ): the first one contains the biocomposites food packaging description, and the second the domain ontology with a specific vocabulary used to structure the first. This dataset uses a vocabulary defined with experts and described by PO² (Process and Observation Ontology)  [1] , an ontology dedicated at its core to the representation of transformation processes through the definition of steps, relations between those and their associated observations. In our case, specific domain vocabulary has been elicited and used to define precisely the different technologies used for the biomass treatment and the biocomposites production. This vocabulary can be reused for others projects on the domain  [2, 3].
The first dataset presents four tables to provide a general overview of the itineraries used to produce biocomposites, and the associated scripts and/or queries to create them. All queries are expressed in the SPARQL language, dedicated to interrogate data structured by ontologies. Queries can either be directly executed using the accessible SPARQL endpoint (http://quantum. agroparistech. fr/graphdb/repositories/Composite_making_process), or formulated and executed through our web-service SPO²Q (http://quantum. agroparistech. fr/spoq) which also provides an interactive form to build queries without relying on SPARQL syntax. In a general manner, all queries introduced in this paper are presented in two ways: directly as a SPARQL query in a file, and as query executable from SPO²Q. In Table  1 , we present the 31 lignocellulosic fillers used through the four projects.
Given a biomass, steps are numbered to indicate the chronology; when two steps happen in parallel, they are distinguished by a second number. For instance, the UPRF sample of the Resurbis project is prepared from different subsamples produced in steps 1. , 1. 2, 1. 3, … which are mixed in step 2. For each step, the available observations' measures are indicated. Due to the complexity of the data presented in this table, we need to compile the outputs of a SPARQL query to organize the data in a readable and easy-to-use way. This script as well as the dedicated SPARQL query are presented in Script 1. It is written in Python, and give an example on how to integrate the SPARQL endpoint in a code for extracting and manipulating the data. To be noted, the USABLE project uses commercial lignocellulosic fillers of pure cellulose, which were already prepared, and thus are not represented in this table.
Acknowledgments Data were collected in the framework of the  MALICE  project co-financed by the  European Regional Development Fund (FEDER)  and the  Languedoc-Roussillon region ; the  EcoBioCAP  project, which was supported by the  European Commission  through the  Seventh Framework for Research & Technological Development  ( FP7/2011-2015 ) under the Grant Agreement  FP7-265669 ; the  NoAW  project, which was supported by the  European Commission  through the  Horizon 2020 research and innovation program  under the Grant Agreement No  688338 ; the  RESURBIS  project, which was supported by  European commission  through the  Horizon 2020 work program  for research & innovation under the grant agreement  730349 ; and the  USABLE  project funded by the  European Commission  through the  Horizon 2020 work program  for research & innovation under the grant number  836884.
The performance evaluation of individual building projects is made in relation to specific benchmarks, i. e. reference points against which comparisons can be made. These benchmarks are often specific to a national or sub-national context, determining the method, data and tools that apply for an evaluation to be representative and fair across several building projects  [2]. Despite differences between benchmarking systems, a range of general learning about methodological choices and points-of-attention exists. To enable more widespread creation and implementation of GWP benchmarks for buildings, core learnings from existing benchmark systems may provide valuable inspiration and knowledge for benchmark developers. More than 50 international experts in the Annex 72 international research project "Assessing Life Cycle Related Environmental Impacts Caused by Buildings" (2017-2022) of the IEA EBC (International Energy Agency Energy in Buildings and Communities Programme) have worked on harmonized methods, tools, processes, and reporting formats for LCA in buildings  [3].
Hence a large spread in use type. The majority of the building cases behind the benchmarks are built in the period of 2010-2020, although a few cases are 5-10 years older. Following the data collection of each benchmark system, expert sessions were conducted to further explore the methodological choices in play, and to uncover the issues and problems that the experts saw as part of the benchmark development. Expert sessions were conducted at two occasions as part of the scheduled, semi-annual, Annex 72 expert meetings. Feedback on the topics of reference unit, system boundaries, and calculation rules were recorded in minutes, and form the background of the discussion points in this paper. It is important to note that several of the Annex 72 benchmarks documented here are still under development, especially in the cases where regulations are currently being prepared.
This is for instance the case for several components of the technical installations. Energy use (B6) generally has a large impact on the overall results of a building LCA. It should be carefully specified which part(s) of the energy use is included. Recent definitions, included in the context of the revision of EN 15978 standards, provide the basic descriptions for declaring this. Inclusion of EoL modules C3-C4. In the cases where the nationally applied impact assessment data for the production of materials include the storage of biogenic carbon (e. g. wood), it is IOP Publishing doi:10. 1088/1755-1315/1078/1/012054 7 crucial to have the mass balances restored in a life cycle perspective. Hence, the inclusion of modules C3-C4 is needed to counterbalance the uptake of carbon during the production.
Concerning methodological choices for the inventory scope and the calculation rules, absolute transparency is recommended to ensure consistency in the way benchmark users apply the system. A mapping of the GWP limit, reference and target values for the 17 Annex 72 cases further provides benchmark actors with insights into the current state-of-art for GWP benchmark systems. The reported benchmark values are found to differ notably, especially for the full life cycle benchmarks (5-90 kg CO 2 e/m 2 /year). The large variations seen in GWP values from existing benchmark systems are partly due to differences in benchmark types, i. e. limit, reference, best-practice and target values. However, the variations also emphasize the uniqueness of each system; the specific methodological choices as well as the background data and the tools employed.
Reprocessed MOLA Data Set MOLA mapped the topography of Mars within footprints of approximately 160 m in diameter with a center-tocenter along-track spacing of around 300 m  (Zuber et al. , 1992). The MOLA Precision Experimental Data Record (PEDR) data set  (Smith et al. , 2003)  features more than 8,700 profiles acquired in the mapping and extended phases of MGS from March 1999 to June 2001, which spans more than one full Martian year (MY24 to MY25). The PEDR data set was geolocated using a MGS trajectory and a Mars rotational model dating back to 2003. Therefore, we have incorporated an updated orbit model from  Konopliv et al. (2006)  and IAU2015 Mars rotational model as in  Archinal et al. (2018)  to update the MOLA geolocation  (Xiao, Stark, Chen, & Oberst, 2022 , Xiao, Stark, Steinbrügge, et al.
These independent results can also be used to cross-validate the off-season increase and decrease as measured by the MOLA data set. for helpful discussions. H. Xiao thanks the China Scholarship Council (CSC) for financial support of his Ph. D. study in Germany. H. Xiao was also supported by a scholarship from the Deutsche Forschungsgemeinschaft (DFG) Grant SFB-TRR 170. Part of this research was carried out within the framework of the DLR BigData Querschnittsplattform. F. Schmidt acknowledges support from the "Institut National des Sciences de l'Univers" (INSU), the "Centre National de la Recherche Scientifique" (CNRS), and "Centre National d'Etudes Spatiales" (CNES) through the "Programme National de Planétologie. " We thank O. Aharonson (PSI & Weizmann Inst. ), T. N. Titus (USGS), and an anonymous reviewer for a constructive review which helped to improve this contribution.
Data Availability Statement The MOLA PEDR data set (Version L) is available from the Planetary Data System (PDS) Geosciences Node at https://pds-geosciences. wustl. edu/missions/mgs/pedr. html  (Smith et al. , 2003). While the MGS trajectory and attitude kernels can be obtained from NASA NAIF at https://naif. jpl. nasa. gov/pub/naif/pds/data/mgs-mspice-6-v1. 0/mgsp_1000/data/ (Acton, 1996). The generated level variation time series in each grid element and various map products are published under https://doi. org/10. 17632/z59b9nd6s9. 2 (Xiao, Stark, Steinbrügge,  et al. , 2021). The mass change rate measurements used for deriving the bulk density of the snow/ice are published under https://doi. org/10. 14768/8cba4407-d6a0-4d16-aeaf-d0ebfd2b480a (Schmidt, 2022).
For example, a mean weight loss of 4-5 kg in the LISA intervention and of 6% of baseline in the interventional arm of ENERGY were linked to a more likely preservation of physical function, greater increases in physical condition, and improvement in a multitude of functional and symptom domains  [45, 46]. More recently, the Alliance for Clinical Trials in Oncology Breast Cancer Weight Loss Trial, also known as A011401 (BWEL, NCT02750826, PI Jennifer A. Ligibel) started in the USA and Canada. BWEL is a National Cancer Institute-funded phase III randomized trial evaluating the effect of a weight loss program on cancer recurrence among over 3000 overweight and obese women with stage II to III breast cancer across over 1000 institutions. The BWEL weight loss intervention is standardized using the approaches developed as part of the Diabetes Prevention Program (DPP)  [45]  and the LISA study, focusing on diet, PA, and motivational factors  [47].
Study design MEDEA is a RCT evaluating the effect of health education alone vs. weight loss intervention plus health education on CRF in overweight and obese women with earlystage breast cancer. Patients are offered to participate in this study during routine consultations. Participants are randomized to a standard health education program (arm 1 control) vs. a 1-year personalized telephonebased health education weight loss follow-up (arm 2 interventional). A permuted block randomization is applied in a 1:1 randomization ratio using a block size of 4. Stratification occurs on the basis of BMI at diagnosis (overweight [BMI 25. 0-29. 9 kg/m 2 ] vs. obese [BMI ≥ 30. 0 kg/m 2 ]), age (≤ 50 vs. > 50 years), and receipt of endocrine therapy (yes vs. no) (Fig. 1 ). To ensure allocation concealment, patients who have signed the informed consent and fulfilled all eligibility criteria will be randomized directly online using the Trialmaster randomization module.
Lifestyle coaches individualize the call content as necessary to address individual problems (ensuring that key aspects of each call are covered) and are trained to address motivational and emotional issues as they arise, partnering with participants to overcome barriers to success. Data on compliance, changes in diet, PA, and weight reported by participants are captured and discussed with coaches during the calls. Coaches prepare a brief written summary of motivational, behavioral, diet, and exercise issues discussed during each call. A toolbox approach that allows for tailoring the intervention to the individual participant is available to facilitate reaching intervention goals and losing weight. Toolbox solutions also aim at optimizing the intervention to meet the needs of specific ethnic, socioeconomic, or other patient populations.