dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This study is based on rock wall loggers installed by  Tobias Hipp  and  Bas Altena  within  CRYOLINK  project in 2011, together with rock wall loggers installed later during the project ' CryoWALL -Permafrost slopes in 885 Norway ' ( 243784/CLE ). Both projects were funded by the  Research Council of Norway. Installation and data retrieval from the loggers were actively supported by the  Geological Survey of Norway (NGU)  and the  Norwegian Water and Energy Directorate (NVE). Particular thanks are due to  Reginald L. Hermanns  ( NGU ) and  Lars Harald Blikra (NVE). Paula Hilger ,  Thorben Dunse  (both the  University College of Western Norway ),  Ove Brynhildsvoll ,  Trond Eiken ,  Jaroslav Obu ,  Bas Altena ,  Juditha Aga ,  Harald Wathne Hestad  and  Erling Thokle Hovden  (all 890  University of Oslo ) helped with retrieving rock wall logger data.
Drought events are projected to become more severe and frequent across many regions in the future, but their impacts will likely differ among ecosystems depending on their ability to maintain functioning during droughts, i. e. , ecosystem resistance. Plant species have diverse strategies to cope with drought. As a result, divergent responses of different vegetation types for similar levels of drought severity have been observed. It remains unclear whether such divergence can be explained by different drought duration, co-occurring compounding effects, e. g. , of heat stress or memory effects, management practices, etc. Here, we provide a global synthesis of vegetation resistance to drought and heat using different proxies for vegetation condition, namely the Vegetation Optical Depth (SMOS L-VOD) data from ESA's Soil Moisture and Ocean Salinity (SMOS) passive L-band mission and EVI and kNDVI from NASA MODIS.
Satellite data Table  1  presents an overview of the vegetation datasets included in this study. We used vegetation optical depth (VOD) data from the SMOS low-frequency microwave satellite (SMOS L-VOD) as an indicator of vegetation dynamics  (Wigneron et al. , 2021). Vegetation optical depth (VOD) parameterizes the attenuation of the microwave radiation when passing through the vegetation layer accounting for the effects of both the woody and leafy components of the vegetation canopy. VOD varies with both the mass of water contained in the canopy and the canopy structure. Different VOD datasets have been interpreted to be dominantly sensitive to vegetation biomass at an annual scale, based on the assumption that relative water content (RWC) in vegetation is stable from year to year  (Brandt et al.
, 2018). Therefore, VOD has been used as a proxy for biomass  (Brandt et al. , 2018; Fan et al. , 2019). Since it is independent of any vegetation index, VOD is robust for application in ecology and climate change studies. Compared to traditional vegetation indices (NDVI, EVI) and Ku-VOD, X-VOD, C-VOD, L band VOD (L-VOD) is less sensitive to saturation effects at high biomass densities and considers the entire canopy, not just the top layer as for high-frequency VOD  (Fan et al. , 2019). The product of L-VOD for the period of 2010-2021 afterwards was filtered strictly  (Yang et al. , in review) , in order to remove the effects of radio frequency interference (RFI) on signals in some regions of the northern hemisphere. The strictly filtered L-VOD data with good quality then were reconstructed using a curve-fitting method used for CO2 measurements  (Thoning et al.
, 1989). The specific process is as follows: First, the L-VOD data were fitted using both a harmonic function (reflecting seasonal cycle) and a polynomial function (reflecting long-term variability). Second, the residuals of the fitting were further transformed using a Fast Fourier Transform, and then filtered using low pass filters, to track the remaining seasonal oscillation and long-term variations. The reconstructed L-VOD data can be separated into two time series including the long-term variability only and the seasonal oscillation only. The former mainly reflects vegetation carbon dynamics, while the latter is more affected by the seasonality in the vegetation water content. SMOS L-VOD data are more complex to interpret when the ground is frozen (e. g. , ice, snow), hence, we removed observations where the MODIS snow or ice cover fraction in the specified pixel is larger than zero.
, 2021). This method resulted in 11 yearly values of L-VOD from 2010 to 2020. Two optical vegetation indices, NDVI and EVI data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra satellite were used to compare with the SMOS L-VOD product. The MODIS-derived NDVI and EVI datasets are temporally and spatially consistent as they are obtained from a single platform and sensor and are regarded as state-of-art proxies for green vegetation cover. We further calculated kNDVI, a nonlinear generalization of the NDVI following Eq. (  1 ): 𝑘𝑁𝐷𝑉𝐼 = 𝑡𝑎𝑛ℎ(𝑁𝐷𝑉𝐼 ! ) , (1) kNDVI has been shown to perform better than NDVI and NIRv with stronger correlations with flux tower estimates of gross primary productivity and satellite retrievals of sun-induced fluorescence.
Land cover and land management data Given the uncertainties in the land-cover mapping  (Hartley et al. , 2017; Li et al. , 2018) , we used three global land cover maps in our study presented in Table  2. These were resampled from their original resolution to 0. 25º spatial resolution for the 2010-2020 period to match the spatial resolution of L-VOD. et al. (2015). To better account for the intrinsic bias and classification differences among the three products, we further aggregated the original classes into four biomes (forests, shrublands, grasslands and croplands) according to Table  3. We categorized land cover in bins of 25% fraction of each biome and, for each pixel, assigned the land cover information with the highest agreement across datasets (>= 2 agree). To guarantee that our results are not biased by the land cover change (e.
g. , deforestation), we excluded those pixels showing changes in the 25% bins during 2010-2020. The LUH2 v2h dataset also provides land management information, for example, forested primary land and potentially forested secondary land (Figure  1 ). We also used the global map of irrigation areas around 2005 at 0. 0833º spatial resolution from FAO  (Siebert et al. , 2013). The latter map provides areas equipped for irrigation as the percentage of total grid cell area (fe) and the percentage of area equipped for irrigation that was actually irrigated (fa), based on national census surveys or irrigation sector studies. We then assumed that the irrigation equipment is totally located in cropland and calculated the percentage of cropland actually irrigated fac as fa*fe/fc, where fc is cropland percentage of grid cell area (Figure  1 ).
To explore the influence of tree characteristics on their resistance to droughts, we used a global forest age map estimated from forest inventories, biomass and climate data at 0. 00833º spatial resolution  (Besnard et al. , 2021). All above datasets were resampled to 0. 25º to make them comparable to the relatively coarse L-VOD data. For the global forest age map, we calculate the average of the forest age in pixels at 0. 25º.
In the tropics, the forest regions and non-forest regions show divergent resistance, with forest regions showing mostly positive resistance to temperature, and negative values predominating in grassland or savannas. L-VOD shows a large deviation relative to kNDVI and EVI in boreal regions, mostly because of missing data for L-VOD in those regions. The standard error is also relatively higher for the Northern Hemisphere with higher land cover fraction but more pixels with missing values. We summarize the results for each of the IPCC AR6 sub-regions in Figure  3. 27 regions coloured red or blue in Fig. 3a  have a mean ecosystem resistance that is significantly different from zero on a two-tailed Student t-test (P-value < 0. 05), the medians of ecosystem resistance range from -0. 20 to 0. 07 month -1 and the averages of 𝛼 range from -0.
The roles of forest management, crop irrigation and forest ages in modulating ecosystem resistance The above results suggest that a transition between dominant vegetation types modifies ecosystem resistance. Other anthropogenic disturbances, such as forest management, have the potential to influence the ability of forest ecosystems to maintain their functioning during drought-heat extremes by directly affecting tree species, age distribution, cover density, rooting depth and primary productivity. For croplands, irrigation is also an essential element to maintain and increase yields during droughts. To detect such effects, we analysed ecosystem resistance to droughts 𝛼 for primary and potential secondary forests from the LUH2 v2h land cover dataset (Figure  5a ). The comparison between primary and potential secondary forests shows that the averaged ecosystem resistance 𝛼 calculated from L-VOD is significantly higher in primary forests than in secondary forests.
Contrasting ecosystem resistance to drought and temperature in forests and cropland Forests and cropland respond differently to extreme drought events. Ecosystem resistance increases with increased forest and decreased cropland fraction. This is also observed in previous studies based on GPP datasets over Europe  (Zhang et al. , 2016; Bastos et al. , 2020)  and SIF  (Walther et al. , 2019). The increasing pattern in L-VOD for dominant forests might be a result of increased insolation and photosynthetic activity light, and weak changes in greenness during the drought period  (Zhu et al. , 2018; Walther et al. , 2019). Besides, the light use efficiency (LUE) decreased less with lower soil moisture contents in forests compared to non-forest vegetation, which could be linked to deeper and more extensive root systems with higher access to available soil water  (Walther et al.
Limitations and outlook Our method assumes a close relationship between L-VOD and vegetation biomass at an annual scale, as other studies using VOD products  (Tian et al. , 2017; Brandt et al. , 2018; Fan et al. , 2019; Wigneron et al. , 2020; Qin et al. , 2021). This assumption is questioned by  Konings et al. (2021)  and they showed a weak temporal correlation between biomass anomalies calculated from a new biomass dataset from  Xu et al. (2021)  and L-VOD anomalies. Yet, the ability of the new dataset to represent the inter-annual variability of biomass has not been tested. Several studies also reported a strong near-linear relationship between L-VOD and biomass for woody vegetation independent of the year  (Rodríguez-Fernández et al. , 2018; Brandt et al. , 2018) , indicating a relatively constant RWC.
Acknowledgements. Chenwei Xiao  acknowledges support from the  International Max Planck Research School for Global Biogeochemical Cycles. We would like to thank  Christiane Schmullius  for helpful and stimulating discussions. We thank  Nuno Carvalhais  for initial discussions about the use of vegetation proxies. We thank  Ulrich Weber  for the preparation of ERA5 data, MODIS land cover, NDVI and EVI data. https://doi. org/10. 5194/egusphere-2023-304 Preprint. Discussion started: 28 March 2023 c Author(s) 2023. CC BY 4. 0 License.
Code and data availability. The reconstructed SMOS L-VOD data are available upon request from Dr. Yang (huiyang@bgcjena. mpg. de). MODIS EVI and NDVI data are freely available from https://modis. gsfc. nasa. gov/data/dataprod/mod13. php. ERA5 reanalysis data are freely available from https://www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/era5. MCD12Q1 land cover product is freely available from https://lpdaac. usgs. gov/products/mcd12q1v006/. ESA Land Cover CCI product is freely available from https://www. esa-landcover-cci. org/?q=node/164. LUH2 v2h land cover product is freely available from https://luh. umd. edu/data. shtml. The definitions of the AR6 reference sub-regions regions, the code and the spatially aggregated datasets are available at the GitHub ATLAS repository: https://github.
com/SantanderMetGroup/ATLAS, https://doi. org/10. 5281/zenodo. 3998463 (Iturbide et al. , 2020) under the Creative Commons Attribution (CC-BY) 4. 0 licence. The global map of irrigation areas is freely available from https://www. fao. org/aquastat/ru/geospatial-information/global-maps-irrigated-areas/latest-version/. The global forest age map is freely available from https://www. bgc-jena. mpg. de/geodb/projects/Home. php. The code used in this study is available upon request.
Genome-wide identification of super-enhancers in postmigratory CNCC subpopulations To identify SEs that might play regulatory roles in CNCC subpopulations of distinct developing facial prominences, we analyzed H3K27ac ChIP-seq datasets from E10. 5 mouse CNCCs of Hox-negative frontonasal process (FNP), maxillary (Mx) and mandibular (Md) components of PA1, and of Hoxa2-expressing PA2  16  (Fig. 1a ). We first merged the H3K27ac ChIP-seq reads from these four CNCC subpopulations for peak calling and then largely followed the workflow of the ROSE algorithm to identify SEs  44, 46  (Methods). Briefly, individual H3K27ac peaks within 12. 5 kb or less were merged into larger regions, and, in each CNCC subpopulation, the H3K27ac signal was quantified to distinguish SEs from typical enhancers 44 (Supplementary Fig.
5 HIRE2 del/del mutant fetuses, and the presence of mild malformations in E18. 5 HIRE2 del/wt ;Hoxa2 EGFP/wt mutant fetuses (Figs. 5, 6  and Supplementary Figs. 9, 10). By contrast, in HIRE1 del/del and HIRE1 del/wt ;Hoxa2 EGFP/wt mutant embryos, we found a drastic reduction of Hoxa2 expression, with about 0-3% and 0-4% of Hoxa2 transcripts, respectively, as compared to WT, thus explaining the finding that HIRE1 deletion phenocopied the full homeotic Hoxa2 knockout phenotype in PA2 CNCCs (Figs. 5, 6  and Supplementary Figs. 9, 10). Next, we carried out whole-mount and tissue section in situ hybridization for Hoxa2 and Hoxa3 in HIRE1 del/del embryos. At E10. 5, Hoxa2 expression was undetectable in PA2, in keeping with the qRT-PCR data (Supplementary Fig. 9 ), and highly reduced in more posterior pharyngeal arches (Fig.
Identification of transcription factor binding motifs in HIRE1/ HIRE2 and involvement of Hoxa2 in its own long-range regulation To investigate which transcription factors may be involved in binding HIRE1 and HIRE2 and potentially regulate long-range interactions with Hoxa2, we performed a motif enrichment analysis using the ATAC-seq data from PA2 and pinna CNCCs at E10. 5 and E12. 5, respectively. We first called the peaks from the datasets of both stages, merged them, and extracted putative enhancer peaks to obtain a total of 106,587 peaks. Thirty-one ATAC-seq peaks overlapped the region spanning from HIRE2 to HIRE1 (mouse GRCm38/mm10 chr6:50,789,172-51,087,888) (Supplementary Fig. 12a ). We then clustered these 31 peaks according to their relative accessibility at E10. 5 and E12. 5 (cluster 1-3) (Supplementary Fig.
Analysis of published ChIP-seq datasets for Hoxa2, Pbx, and Meis in PA2 at E11. 5  57, 58  , revealed that these factors indeed showed enriched binding at HIRE1 and HIRE2 (Fig. 3a  and Supplementary Fig. 4 ). These latter findings indicated that Hoxa2 may play a role in long-range inter-TAD interactions with its own promoter. Notably, PCHi-C on E10. 5 PA2 CNCCs in Hoxa2 EGFP/EGFP knockout embryos  49  revealed that the strength of the interactions of Hoxa2 with HIRE2 in E10. 5 Hoxa2 EGFP/EGFP knockout embryos was reduced by 27% (indeed, note that with CHiCAGO score ≥5, no arc is visible between Hoxa2 and HIRE2 in Hoxa2 EGFP/EGFP vs. WT; Fig. 9d  and Supplementary Fig. 3 ). These data strongly suggest that Hoxa2 is partially involved in its own long-range transcriptional regulation likely with cofactors such as Pbx and Meis.
Use of previously published datasets Some datasets have been used from our previously published work  16  , as indicated in the manuscript, and are published in the SuperSeries GSE89437. A number of RNA-seq, ChIP-seq, and ATAC-seq samples published in this series have been re-processed for this study (Supplementary Data 7). available through GEO were used in this study: ChIP-seq, RNA-seq, and ATAC-seq data from mouse E10. 5 cranial neural crest cell subpopulations (GSE89437). FACS gating strategies are presented in Supplementary Fig. 3. ChIP-seq peaks for Hoxa2 in PA2 at E11. 5 were obtained from ref. 57, and ChIP-seq peak data for Pbx and Meis in PA2 at E11. 5 were obtained from ref. 58. Further public databases used in this study are UCSC (mm10 reference genome assembly, gene annotation), JAS-PAR2018 (vertebrate transcription factor motifs)  133  , JASPAR 2022 (vertebrate transcription factor motifs)  127  , and the ENCODE Blacklist 131.
Acknowledgements We thank  S. Smallwood ,  François Spetz , and  FMI  facilities for excellent technical support and  T. Kitazawa ,  D. Machlab , and members of the  Rijli group  for discussion. We thank  T. Sexton  for valuable input on capture HiC technology. We thank  L. Giorgetti  for the E14 mESCs line. O. J. was supported by an  EMBO Long-Term  fellowship. F. M. R. was supported by the  Swiss National Science Foundation  ( CRSII5_173868  and  31003A_175776 ) and the  Novartis Research Foundation. This project has also received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  810111-EpiCrest2Reg ).
Data availability All raw sequencing data and processed data generated in this study have been deposited and are publicly available in the Gene Expression Omnibus (GEO) under GEO Series accession number GSE211904 (all data SuperSeries), GSE211899 (ATAC-seq), GSE211900(ChIP-seq), GSE211901 (Hi-C), GSE211902 (PCHi-C), and GSE211903 (RNA-seq). The following public sequencing datasets were published in ref. 16. and.
We propose a semi-automatic Scan-to-BIM reconstruction approach, making the most of Artificial Intelligence (AI) techniques, for the classification of digital architectural heritage data. Nowadays, Heritage-or Historic-Building Information Modeling (H-BIM) reconstruction from laser scanning or photogrammetric surveys is a manual, time-consuming, overly subjective process, but the emergence of AI techniques, applied to the realm of existing architectural heritage, is offering new ways to interpret, process and elaborate raw digital surveying data, as point clouds. The proposed methodological approach for higher-level automation in Scan-to-BIM reconstruction is threaded as follows: (i) semantic segmentation via Random Forest and import of annotated data in 3D modeling environment, broken down class by class; (ii) reconstruction of template geometries of classes of architectural elements; (iii) propagation of template reconstructed geometries to all elements belonging to a typological class.
Introduction In recent years, the Building Information Modeling (BIM) methodology was transferred from the realm of new construction to that of the built heritage. Since the early studies by Murphy and Dore  [1, 2] , the scientific literature on Heritage or Historic-BIM (H-BIM) has expanded  [3] [4] [5] [6] [7] [8] , aiming to illustrate how geometrical data can be linked to: architectural grammar and styles  [9] [10] [11] , material characterization  [12] , degradation patterns  [13] , façade interventions and historical layers  [14, 15] , structural damage and FEM analysis  [16] [17] [18] , data collection and simulation of environmental parameters  [19] , archival photographs  [20]  and text documents  [21, 22]. Hichri et al. [8]  and Macher et al. [23]  emphasized that H-BIM techniques require the transition from the existing condition of the object to the modeling environment.
The shift from the as-built condition (registration of a building after construction) to the as-is representation (registration of its current condition) implies reference to surveying data, as point clouds acquired via laser scanning or photogrammetry  [24]  and reverse engineering techniques. On the one hand, the elaboration of 3D surveying for the construction of BIM models, known as Scan-to-BIM  [21] , is seen as a manual, time-consuming and subjective process  [3, 5] ; on the other hand, the emergence of Artificial Intelligence (AI) techniques in the architectural heritage domain  [25] [26] [27]  is reshaping the approach of heritage experts towards the interpretation, recognition and classification of building components on raw surveying information. Based on this consideration, this work proposes a semi-automated procedure to enable the construction of BIM models of heritage objects and sites, starting from 3D survey data that are classified via a supervised Machine Learning (ML) method.
Reconstruction by generative modelling. In this case, the reconstruction is again guided by the formalization of architectural knowledge, and VPLs are considered to manipulate each geometry by interactively programming, via a graphical coding language made up of nodes and wires, the set of modeling procedures, primitive adjustments and duplication operations performed in 3D space  [33, 53, 55]. Grasshopper, a visual programming interface for Rhino3D, and Dynamo, a plug-in for Autodesk Revit, are commonly used for these tasks in the case of new constructions. By contrast, VPLs are rarely exploited for existing monuments and sites. The 3D content could be created, based on surveying data  [17, 48, [55] [56] [57] , by a series of graphic generation instructions, repeated rules and algorithms  [58].
State-of-the-Art AI-Based Semantic Segmentation In the digital heritage field, ML and Deep Learning (DL) techniques emerge to help digital data interpretation, semantic structuring and enrichment of a studied object  [25]  e. g. , to assist the identification of architectural components  [59] , the re-assembly of dismantled parts  [60] , the recognition of hidden or damaged wall regions  [61] , and the mapping of spatial and temporal distributions of historical phenomena  [62]. In the architectural heritage domain, AI techniques have proven to be crucial in streamlining the so-called semantic segmentation process, understood as the reasoned subdivision of a building into its architectural components (e. g. , roof, wall, window, molding, etc. ), starting from surveying data. With respect to other common computer vision tasks exploiting AI, such as object recognition, instance localization and segmentation, the semantic segmentation process classifies pixels or points as belonging to a certain label and performs this operation for multiple objects of the 2D image or of the 3D unstructured scene (Figure  4 ).
Methods The proposed methodological approach for Scan-to-BIM classification and reconstruction is divided into two macro-parts: in the first one, a supervised ML algorithm, the RF, is exploited to annotate the architectural components of a building from a point cloud survey. The workflow for this first phase was previously presented in reference  [76] , to which the reader could refer for further details and in-depth discussion on the ML-based data processing step. The focus of this paper is rather on the second part of the workflow, concerning Scan-to-BIM reconstruction based on semantically annotated data. The latter takes place following the successive steps of: import of annotated data into the BIM environment; reconstruction and propagation, via VPL, of template geometries based on knowledge derived from historical treatises of architecture; and transfer of template geometries into BIM software as Autodesk Revit (Figure  6 ).