dataesr/scientific-paragraphs-categorization huggingface dataset:
We used the 'Wavelet Methods for Time Series Analysis' in MATLAB's 'Wavelet Toolkit' to decompose the data  (Cornish et al 2003). Further details on the application of this method may be found in the supplement and Sturtevant et al  (2016). Finally, to quantify potential sample bias from infrequent sampling and identify optimal sampling approaches, a random sample analysis was conducted on the gap-filled oligotrophic (US-Too; 2012), mesotrophic (FI-Van; 2016), and eutrophic (DE-Zrk;  2014) lake/reservoir data with the smallest data gaps. One thousand random samples without replacement were taken for each of the following times: daytimeonly (DT), daytime/nighttime-only (DT/NT), summer mid-day (SMD), growing season (GS), and annual. DT and NT were defined as 10:00-15:30 h and 22:00-03:30 h (local times), respectively.
León-Palmero et al (2020) noted reservoir CO 2 fluxes to be dependent on lithology, with a CO 2 source and sink being reported for calcareous and siliceous watersheds, respectively. The work also found an impact of reservoir mean depth on methane fluxes, which highlights that these influences are not limited to CO 2. Water chemistry and carbon isotope data would also provide great insights on the contributions of dissolved inorganic carbon (DIC) transformation and organic carbon mineralization (Zhong et al 2018), especially in cases of damming where DIC transport is affected (Wang et al 2020). These processes have been documented to play an important role in CO 2 outgassing, but the limitations of our dataset prevented us from conducting further analysis on these processes. As a result, future studies must take these characteristics and processes into account due to their prominent role in influencing lake and reservoir greenhouse gas flux magnitudes across multiple temporal scales.
the support from the ACCC Flagship funded by the Academy of Finland (337549 and 337552) and ICOS-Finland by University of Helsinki and the Ministry of Transport and Communication. GAW was financially supported by the Swedish Research Council (VR: Grant Nos. 2016-04153 and 2020-03222). The deployment of the EC at the Nam Theun 2 Reservoir (Lao PDR) was funded by Electricité de France (EDF) and Nam Theun Power Company (NTPC). TS and FK were supported by the Helmholtz Association of German Research Centres through grants to TS (Grant VH-NG-821) and FK (Grant PD-129), the Helmholtz Climate Initiative REKLIM (Regional Climate Change), and infrastructure funding through the Terrestrial Environmental Observatories Network (TER-ENO). TS and FK thank the (staff of the) Department Chemical Analytics and Biogeochemistry at the Leibniz-Institute of Freshwater Ecology and Inland Fisheries (Berlin) for providing water chemistry data for DE-Zrk.
WE and GK acknowledge support from NSF-DEB 1637459 and OPP 1936769. The deployment of the flux tower at CA-Eastmain was supported by Hydro Quebec. Flux observations at US-OWC were funded by the Ohio Department of Natural Resources, by NOAA's National Estuarine Research Reserves' Davidson Fellowship, and by US Department of Energy awards DE-SC0021067 and DE-SC0022191. The co-authors express gratitude for the kindness and contributions of posthumous co-author Werner Eugster.
Acknowledgments We thank all project participants for kindly sharing data and time. All flux tower data and code for processing data will be publicly available in the Environmental Data Initiative depository (DOI pending). MG and ARD acknowledge support from the  U. S. National Science Foundation North Temperate Lakes LTER (NSF   DEB-1440297 , NTL LTER). Funding for US-UM3 was provided by the  U. S. Department of Energy's Office of Science. IM and TV thank the support by the  EU-Horizon Europe  project  101056921-GreenFeedBack  and  Academy Professor projects  ( 312571  and  282842 ). IM, TV and AL thank.
The reviews encompassed many evidence sources identified using diverse strategies including keyword searches, and natural language processing of 48,781 peer-reviewed papers on nature valuation. The evidence reviewed in depth included 1,163 valuation studies, 1,270 study-site units reporting on values-based outcomes for 217 case studies, 838 documents from the 'grey literature' of environmental and development policy (for example, reports from governmental, non-governmental organizations and valuation initiatives), 26 specific contributions from Indigenous and local knowledge holders and experts, 460 futures scenarios, 37 policy instruments, 217 country-specific datasets (for example, Aichi target 2 progress and UN System of Environmental-Economic Accounting-Ecosystem Accounting implementation) and 134 valuesbased behavioural theories (Supplementary Information Section B).
Acknowledgements We are grateful to the IPBES, whose 139-member states commissioned the  Values Assessment  and approved its Summary for Policymakers. We are also grateful for the contributions to the assessment's review editors:  S. Anderson ,  S. Baker ,  J. Camilo Cardenas ,  J. Cariño ,  K. Chan ,  J. Farley ,  C. Okereke ,  L. Pereira ,  E. Raez ,  H. Vessuri  and  R. Watson ; the members of the management committee:  B. Vila ,  A. Diáz-de-Leoń ,  C. Diaw ,  M. Avdibegovic ,  J. Marton-Lefevre  and  R. Allahverdiyev , and the more than 200 contributing authors who provided specific input to the full report. We express our gratitude to IPBES Executive Secretary  A. Larigauderie  and  IPBES Chair A. M. Hernández  for their strategic vision and continued advice. We received no specific funding for this work; all authors involved in IPBES do so on a voluntary basis.
c. Ocean heat content and observational datasets From the model simulations, ocean heat content in a volume of water B is calculated as OHC B (t) 5 C p. 0 ∑ x, y, z2B u[x , y , z(t), t] V[x , y , z(t), t], where u is the potential temperature in an individual grid cell, and V is its volume. The heat capacity C p 5 3991. 868 J kg 21 K 21 and the reference density of seawater r 0 5 1026 kg m 23 are constants in NEMO as used for these CMIP6 simulations. The summation over the grid cells uses the time-varying cell thickness z(t) in NEMO ORCA1. The summation volume B could be the entire World Ocean (WO), an ocean basin, or a specific layer. OHU is simply defined as the change in OHC relative to a given initial value, for instance, the increase in OHC since 1991, diagnosed in 2014. Time series of global OHC change in the upper 2000 m are estimated based on five different ocean temperature datasets, as shown in Table  2.
e. , the uncertainty specified by each dataset originator) across the ensemble to estimate the total uncertainty. Structural and internal uncertainty are assumed to be independent and combined in quadrature to estimate the total uncertainty. Central estimate time series for each layer are constructed from the ensemble average and have the advantage of reducing sampling noise compared to individual datasets  (Gulev et al. 2021). Further information on the 0-700 and 700-2000 m ensemble OHC change estimates is available in the supplementary material (Figs. S1 and S2 in the online supplemental material). For the observational estimates of global OHC below 2000 m we assume a linear increase in OHC of 1. 15 6 0. 57 ZJ yr 21 from 1992 onward, following  Purkey and Johnson (2010), and von Schuckmann et al.
1  Similar basin time series were provided by L. Cheng (2019, personal communication)  for the  Cheng et al. (2017)  dataset (Table  2 ). These data products were used to estimate the total uncertainty for basin ocean heat content changes, for the time periods 1971-91 and 1991-2014, following the approach of  Palmer et al. (2021). The method for calculating the ensemble mean and the error of the basinwise OHC is the same as for the global time series (see also detail in section 4). In addition to observational time series of OHC change we also make use of vertical profiles of temperature change based on the NCEI, EN4, and Cheng et al. datasets. While for the NCEI data basinwise averages are provided, spatially complete monthly gridded fields of ocean temperature for EN4 and Cheng were used to generate decadal averages for the periods 1965-74 and 2005-14.
Regional ocean heat content change An analysis of the OHC change in the 0-700 and 700-2000 m layers by ocean basin and time period is shown in Fig. 7. We chose periods before and after the Pinatubo eruption because of the substantial change in OHU rate in the U. K. models. OHC change over these periods was calculated as the difference in OHC between the last and the first annual mean of the period. For the models, we calculated this difference for each ensemble member individually and then plotted the mean value, using one standard deviation as the structural error estimate (i. e. , a measure of ensemble spread). The model ensembles are compared with an observational ensemble of two datasets, NCEI and CHG (cf. Table  2 ). Not all OHC observational datasets are available for individual basins, and below 2000 m no basinwise estimates of OHC change exist.
As an estimate of the standard error of the individual datasets, we used the "se" variable of the annual means in the case of NCEI and the provided error variable of CHG. The CHG data came in monthly means. For the error of the annual means, we assumed perfectly correlated error across the monthly means and used the annual average. When taking the difference of two annual means for the bar height we used the larger of two individual standard errors as the standard error of this bar. For the standard error s std of the observational ensemble we used again the larger of the two individual standard errors. An estimate of the structural error s struct of the ensemble is obtained from the standard deviation of the ensemble. Last, the total error s tot is calculated as s tot 5 s 2 std 1 s 2 struct.
Depth-time structure of ocean temperature trends The previous sections have shown that the time evolution of OHU significantly differs across layers and basins. We explore the structure of the OHU in depth and time in more detail by calculating the trend of temperature change in each basin and on each level separately. Observational estimates of these trends are obtained from three datasets}World Ocean Atlas 2018 (WOA18), ISH  (Ishii et al. 2017) , and CHG}as the difference between two decadal means  (1995-2004 and 1965-74). The scarcity of ocean temperature observations below 2000 m and before the Argo period does not allow for a higher time resolution measure like statistical trend estimates from time series of annual means. For the same reason we did not calculate error estimates: these are only available at locations and times where in situ measurements were taken.
It is beyond the scope of this paper to extend such error estimates to basinwide averages. Instead, the spread of the three different datasets provides a rough estimate of the structural error. The most recent "decadal" mean available from WOA18 covers the 13 years from 2005 to 2017 inclusive. Because the CMIP6 historical model simulations finish with the year 2014 we could not use this last WOA18 decade for comparison. Figure  8  shows the temperature change over the last 30 years of the twentieth century in six ocean basins and the global ocean ("WO") at every depth level for the four models and the three observations-based datasets. The trend (8C decade 21 ) was calculated from the difference between two decadal averages,  1995-2004 minus 1965-74. As in the previous plots we have applied quadratic detrending of the individual simulations, here at each level and each basin individually.
) In the same depth range 300-1000 m, the CNRM models simulate a significant warming trend that indicates strong OHU in the subsurface SA, which is not in line with two of the three observational products. In the top 200 m of the SA, we find too-strong in all models, and more so for the U. K. models. Note that the CHG dataset (dotted) significantly deviates from the other two observations-based datasets in the SA basin (0-2000 m) and the SI basin (100-400 m). The CHG data relax to the CMIP model mean in absence of observational coverage  (Cheng et al. 2019). Hence, the often sparse in situ data coverage in the Southern Ocean could explain this discrepancy. The pattern of subsurface cooling in the U. K. models and subsurface warming in the CNRM models is not only found in the SA but also in the SP.
Here the U. K. models appear to be somewhat closer to an observed slight cooling. Furthermore, a similar discrepancy between the two model families is visible in the SI and NP basins, with the CNRM models being closer to observations in these cases. Overall, the CNRM models show a preference to store FIG. 8. Late-twentieth-century decadal temperature trend at individual layers, horizontally averaged over the labeled domains. Trend estimate between the decades  1965-74 and 1995-2004. Dark blue: UKESM1. 0 historical ensemble mean (solid) and ensemble envelope (shading); likewise for the other models: GC3. 1-LL (light blue), CNRM-CM6-1 (yellow), and CNRM-ESM2-1 (red). Three different observational datasets are plotted in black (solid, dashed, dotted). UKESM1. 0 ensemble member r17 is highlighted in teal, and CNRM-CM6-1 ensemble member r4 in brown.
The datasets are each plotted on their native depth levels. Note the different horizontal scale in the two panels for each basin. heat in the layers, while the U. K. models store heat below 2000 m. A further outstanding feature in the UKESM1. 0 simulations is the large warming trend and the large variance in Antarctic Bottom Water (AABW). This is particularly strong in the SP (below 4000 m) where the observations suggest a zero trend, as reproduced by the CNRM models, but the UKESM1. 0 ensemble mean has trends of up to 0. 0158C decade 21 , with the largest values in the deepest layers. We suggest that this strong trend and the large variance stem from interdecadal or centennial variability in AABW formation and the associated OHU. We have not related our analysis of the pre-industrial cold bias in the abyssal SP, followed by a historical warming trend, to the results of  Bourgeois et al.
Acknowledgments. T. K. was funded by the  National Environmental Research Council (NERC)  national capability grant for the U. K. Earth System Modelling project, Grant  NE/N017951/1. T. K. and A. V. were also supported by the  EU   Horizon 2020 Research Programme CRESCENDO project , Grant Agreement  641816. A. V. was funded by the  EU Horizon 2020   CONSTRAIN  project under Grant Agreement 820829. R. K. and M. P. were supported by the  Met Office Hadley Centre Climate Programme  funded by the  BEIS and Defra. We thank  Lijing Cheng  for providing us with updated and extended versions of his datasets. We are grateful to  Catia Domingues  for preprocessing of global OHC time series. Data availability statement. The U. K. simulation data are archived at the Met Office and are available for research purposes through the  JASMIN platform  (www.
Formation of the D compartment after DSB induction Previous research identified the existence of two main, spatially distinct, self-segregated, chromatin compartments in mammalian nuclei  30. These chromatin compartments were determined by principal component analysis (PCA) of Hi-C chromosomal contact maps in which the first principal component enabled the identification of loci that share similar interaction patterns, and that can be visualized linearly using eigenvectors. Further correlations with epigenomic features revealed that these two spatially segregated compartments correspond to active chromatin (the A compartment or euchromatin) and inactive chromatin (the B compartment or heterochromatin)  30. A/B compartment identification using our Hi-C datasets revealed that DSB induction does not trigger major changes in genome compartmentalization into euchromatin versus heterochromatin (Extended Data Fig.
6b  (green track)), DDR-upregulated genes targeted to the D compartment (for example, GADD45A, PLK3 and JUN) displayed high levels of R-loops (Extended Data Fig. 6b  (blue track)) compared with genes that are not targeted to the D compartment (for example, CDC42, LPHN2 and ITGB3BP). On average, genes with high R-loop levels showed a higher D-compartment signal compared with genes with low levels of R-loops (Extended Data Fig. 6c ). By contrast, D genes displayed higher R-loop levels compared with non-D genes (Fig. 3f ). To carefully evaluate whether R-loops accumulate after DSB induction on D genes, we performed quantitative and strand-specific R-loop mapping (qDRIP-seq  39  ) in undamaged cells as well as 4 h and 24 h after DSB induction. As observed using our previous DRIP-seq dataset, GADD45A displayed R-loops (Extended Data Fig.
Immunofluorescence DIvA cells were grown on glass coverslips and fixed with 4% paraformaldehyde during 15 min at room temperature. A permeabilization step was performed by treating cells with 0. 5% Triton X-100 in PBS for 10 min, then cells were blocked with PBS-BSA 3% for 30 min. Primary antibodies against γH2AX ( JBW301, Millipore Sigma, 05-636) were diluted 1:1,000 in PBS-BSA 3% and incubated with cells overnight at 4 °C. After washes in 1× PBS, cells were incubated with anti-mouse secondary antibodies (conjugated to Alexa 594 or Alexa 488, Invitrogen), diluted 1:1000 in PBS-BSA 3%, for 1 h at room temperature. After staining with DAPI, Citifluor (Citifluor, AF-1) was used for coverslip mounting. Images were acquired with the software MetaMorph, using the ×100 objective of a wide-field microscope (Leica, DM6000) equipped with a camera (DR-328G-C01-SIL-505, ANDOR Technology).
High-content microscopy Transfected DIvA and U2OS cells were plated in 96-well Cell Carrier plates (Perkin Elmer). Cells were subjected to OHT or etoposide treatments and an immunofluorescence as described above using anti-mouse Alexa 488 secondary antibodies (Invitrogen) for γH2AX detection. Cells were stained with 5 µg ml -1 Hoechst 33342 (Invitrogen) for 30 min at room temperature. γH2AX foci were analysed using the Operetta CLS High-Content Imaging System (Perkin Elmer) and Harmony software (v. 4. 9). For quantitative image analysis, 81 fields per well were acquired with a ×40 objective lens to visualize around 4,000 cells per well in triplicate. Subsequent analyses were performed with Columbus software (v. 2. 8. 2). Determination of the number (y axis) and size of γH2AX foci (mean area on the x axis) in each nucleus was been used to infer clustering (clustering leads to a low number of large foci) (>8,000 nuclei analysed per sample).
3D RIM super-resolution acquisition and reconstruction 3D + t live RIM was performed on AID-DIvA cells expressing 53BP1-GFP  20  using an upgrade of the system and method described previously  49. In brief, 60 3D images were acquired during 47 min using an inverted microscope (TEi Nikon) equipped with a ×100 magnification, 1. 49 NA objective (CFI SR APO 100XH ON 1. 49 NIKON) and SCMOS camera (ORCA-Fusion, Hamamatsu). The temporal resolution of all of the Supplementary Videos is 40 s. A commercial acquisition software (INSCOPER SA) enables a whole-cell single-timepoint 3DRIM acquisition in only 6 s under a low-photobleaching regime (1 W cm -2 ). Fast diode lasers (Oxxius) with the wavelengths centred at 488 nm (LBX-488-200-CSB) were used to produce a TEM00 2. 2-mm-diameter beam. The polarization beam was rotated with an angle of 5° before hitting a X4 Beam Expander beam (GBE04-A) and produced a 8.
3D film editing Bleaching correction was performed after RIM reconstruction using open-source FIJI software (https://imagej. net/software/fiji/) based on exponential FIT from the background signal. The 3D drift correction FIJI plugin was performed for 3D registration (https://github. com/fiji/ Correct_3D_Drift). 3D + t video rendering was generated using the VTK library implemented in ICY software (https://icy. bioimageanalysis. org/) from a 3D crop of the area of interest.
FRAP measurements FRAP experiments were performed on 53BP1-GFP foci using a Zeiss LSM 710 confocal light scanning microscope (Carl Zeiss) equipped with a ×63/1. 2 NA oil-immersion objective. Typically, images were acquired at 512 × 256 px at a scan speed corresponding to 200 ms per image, and 300 images were acquired over 2 min, with an interval of 500 ms between subsequent images. Before photobleaching half of the foci, three images were recorded. For each experiment, a custom R script was used to segment the image, track the bleached foci and retrieve the average intensity of the bleached half (I B ), the non-bleached half (I NB ), the background (I BG ), and the rest of the nucleus (I REF ) at each frame. These intensity values were used to calculate FRAP curves for the bleached half (FRAP B ) and the non-bleached half (FRAP NB ), according to ref.
Signal-amplification steps were performed, followed by the development of the HRP-C1 and HRP-C2 signals, using Opal dye 620 and 690 (Akoya Biosciences) diluted to 1:750. Finally, immunofluorescence was performed as described above, without an additional permeabilization step and using γH2AX ( JBW301, Millipore) and DAPI staining. Images were acquired with the software Micro-Manager, using the ×40 or ×60 objective of a spinning-disk/ high-speed widefield CSU-W1 microscope, equipped with an Andor Zyla sCMOS camera. The colocalization between γH2AX foci and RNA foci was measured using Cell Profiler. Two foci were considered to be colocalizing when part of their areas was overlapping and if their respective centroids were separated by less than 1 µm.
RNA-seq RNA-seq in DIvA cells was performed as described previously  38. Raw sequencing data were mapped in paired-end to a custom human genome (hg19 merged with ERCC92) using STAR. Count matrices were extracted using htseq-count with union as resolution-mode and reverse-strand mode. Differential expression analysis was performed on the count matrix using edgeR with two replicates per condition (with or without 4 h OHT treatment) and differential genes were determined using log-ratio test. Whole-genome coverage was computed using the bamCoverage command form deeptools to generate bigwig from BAM files (without PCR duplicate suppression). Differential coverage between two conditions was performed using BamCompare from deeptools with setting the binsize parameter at 50 bp. The log 2 -transformed fold change was calculated using edgeR in differential expression analysis.
5% SDS, 0. 3 µg µl -1 proteinase K) for 45 min at 55 °C. The samples were subjected to phenolchloroform extraction and ethanol precipitation, and were resuspended in low EDTA TE. Sequencing libraries were prepared using the Swift ACELL-NGS 1S Plus kit according to the manufacturer's instructions using 12 PCR cycles. Libraries were pooled at equimolar concentrations and sequenced using the Illumina NextSeq 500 system with 75 paired-end reads. qDRIP-seq data were processed using a custom pipeline taking into account stranded and spike-in library preparation. In brief, reads were trimmed using Trimmomatic (v. 0. 39)  53  to remove remaining primers from the library. BWA-MEM was used for mapping reads to a custom reference genome merging hg19 and dm6 (spike-in) chromosomes. Samtools was used to generate BAM files with reads based on their mapping location (hg19 or dm6).
Amplicon-seq AID-DIvA cells were treated with or without 300 nM OHT for 4 h followed by treatment with indole-3-acetic acid for 14 h to degrade AsiSI  23. The genomic DNA was extracted from 5 × 10 6 cells using the DNeasy Blood & Tissue Kit (Qiagen) according to the manufacturer's instructions. Genomic DNA was then used in a multiplex PCR reaction that amplified 25 target sites: 20 AsiSI cut sites and 5 uncut control sites (Extended Data Table  1 ). Amplicons were size-selected using SPRIselect beads (Beckman, B23318) and processed for DNA library preparation using the NEBNext Ultra II kit (NEB, E7645L). Libraries were pooled at equimolar concentrations and sequenced using the Illumina NextSeq 500 system with paired-end 150 cycles. The data were analysed using our custom tool mProfile, available at GitHub (https://github.