dataesr/scientific-paragraphs-categorization huggingface dataset:
The archive of these statistics stored as JSON files  (Crockford, 2006; Crockford and Morningstar, 2017)  includes versioning details for all codes, and dependencies and data that were used for the calculations. These files provide the baseline results of the PMP (See the Code and Data Availability section for details). Advancements in model evaluation along with the number of models and complexity of simulations motivate more systematic documentation of performance summaries. With PMP workflow provenance information being https://doi. org/10. 5194/egusphere-2023-2720 Preprint. Discussion started: 24 November 2023 c Author(s) 2023. CC BY 4. 0 License. recorded and the model and observational data standards maintained by PCMDI and colleagues, PMP strives to make all its results reproducible.
Climatology Mean state metrics quantify how well models simulate observed climatological fields at a large scale, gauged by a suite of well-established statistics that have been used in climate research for decades. The focus is on the coupled "Historical" and atmospheric-only AMIP  (Gates et al. , 1999)  simulations which are well-suited for comparison with observations. The PMP extracts seasonally and annually averaged fields of multiple variables from large-scale observationally based datasets and results from model simulations. Different obs4MIPs-compliant reference datasets are used depending on the variable examined. When multiple reference datasets are available, one of them is considered as a "default" while others are identified as "alternatives". The default datasets are typically state-of-theart products, but in general, we lack definitive measures as to which is the most accurate, so the PMP metrics are routinely calculated with multiple products so that it can be determined what difference the selection of alternative observations makes to judgment made about model fidelity.
Sung et al. (2021)  examined the performance of models run at the Korea Meteorological Administration (K-ACE and UKESM1) in reproducing ETMoVs from their Historical simulations, and concluded that these models reasonably capture most ETMoVs. Lee et al. (2021b)  collectively evaluated ~130 models from CMIP3, 5, and 6 archive databases using their ~850 Historical and ~300 AMIP simulations, where they found the spatial pattern skill improved in CMIP6 compared to CMIP5 or CMIP3 for most modes and seasons, while the improvement in amplitude skill is not clear. Arcodia et al. (2023)  used the PMP to derive PDO and AMO to investigate their role in decadal variability of subseasonal predictability of precipitation over the western coast of North America and concluded that no significant relationship was found.
(2013). This error metric quantifies the spatiotemporal error in climatological cloud properties for clouds with optical depths greater than 3. 6, weighted by their net TOA radiative impact. The observational baseline against which the models are compared comes from the ISCCP HGG dataset  (Young et al. , 2018). Zelinka et al. (2022)  showed that models with smaller mean-state cloud errors tend to have stronger but not necessarily better (less erroneous) cloud feedback, which suggests that improving mean-state cloud properties does not guarantee improvement in the cloud response to warming. However, the models with the smallest errors in cloud feedback tend to also have less erroneous mean-state cloud properties, and no models with poor mean-state cloud properties have feedback in good agreement with expert judgment.
It allows the user to toggle between several metrics (e. g. , RMSE, bias, and correlation) and regions (e. g. , global, Northern/Southern Hemisphere, and Tropics), along with relevant provenance information. Users can click on the interactive plots to get dive-down diagnostics information for the model of interest which provides detailed analysis to better understand how the metric was calculated. PMP's interactive plots have been developed using Bokeh (https://bokeh. org/), a Python data visualization library that enables the creation of interactive plots and applications for web browsers.
, 2021) , University of California, Los Angeles  (Lee et al. , 2019b) , and the Community Integrated Earth System Model (CIESM) project  (Lin et al. , 2020). To make the PMP more accessible and useful for modeling groups, efforts are underway to broaden workflow options. Currently, a typical application involves computing a particular class of performance metrics (e. g. , mean climate) for all CMIP simulations available via ESGF. To facilitate the ability of modeling groups to routinely use the PMP during their development process, we are working to provide a customized workflow option to run all the PMP metrics more seamlessly on a single model, and to compare these results with a database of PMP results obtained from CMIP simulations (see Code and Data Availability section). Via the PMP-documented and pre-calculated metrics from simulations in the CMIP archive, it is possible to readily incorporate CMIP results into the assessment of new simulations, without retrieving all CMIP simulations and recomputing the results.
, 2019; Neelin et al. , 2023). The International Land Model Benchmarking (ILAMB) Software System  (Collier et al. , 2018)  led by Oak Ridge National Laboratory provides land surface and carbon cycle metrics with key state-ot-the art observational products, and similarly, the International Ocean Model Benchmarking (IOMB) Software System  (Fu et al. , 2022)  focuses on surface and upper ocean biogeochemical variables. The Climate Variability Diagnostics Package (CVDP;  Phillips et al. , 2014; Fasullo et al. , 2020)  developed at NCAR provides diagnosis of climate modes of variability. Analyzing Scales of Precipitation (ASoP;  Klingaman et al. , 2017; Martin et al. , 2017; Ordonez et al. , 2021)  focuses on analyzing precipitation scales across space and time. In parallel, the regional climate community also has actively developed metrics packages such as the Regional Climate Model Evaluation System (RCMES;  Lee et al.
g. , CMIP application of CF Metadata Conventions [http://cfconventions. org/];  Hassell et al. , 2017; Eaton et al. , 2022). It is worth noting that the comprehensive database of PMP results offers a resource for exploring the range of structural errors in CMIP class models and their interrelationships. For example, examination of cross-metric relationships between mean-state and variability biases can shed additional light on the propagation of errors (e. g. ,  Kang et al. , 2020; Lee et al. , 2021b). There continues to be interest in ranking models for specific applications (e. g. ,  Ashfaq et al. , 2022; Goldenson et al. , 2023; Longmate et al. , 2023; Papalexiou et al. , 2020)  or to "move beyond one model one vote" in multi-model analysis to reduce uncertainties in the spread of multi-model projections (e.
We expect that the current ongoing PMP code modernization effort to fully adapt the xCDAT will potentially galvanize greater community involvement. We will continue to maintain robust rigorousness in the calculation of statistics for the PMP metrics by staying tuned with the latest progress in the field, such as implementing the method for more rigorous conservation in horizontal interpolation  (Taylor, 2023). To improve clarity of key deliverable messages from multivariate data of PMP's metrics obtained from comprehensive ESM archives, we will consider implementing the advances in the high-dimensional data visualization field, such as the circular plot discussed in  Lee et al. (2018b)   and Atmospheric River detection metrics that are currently under development at LLNL. Both of these metrics suites were developed using the pattern detection capabilities in the latest TempestExtremes  (Ullrich et al.
, 2018)  and the CMIP6Plus projects, will further expand the utility of performance metrics in identifying problems associated with the forcing dataset and their application and use in reproducing the observed record of historical climate. Furthermore, as ESMs advance towards more operationalized configurations to meet the demands of decision-making processes  (Jakob et al. , 2023) , the PMP holds significant potential to provide interoperable ESM evaluation and benchmarking capabilities to the community.
Acknowledgment We acknowledge the  World Climate Research Programme , which, through its  Working Group on Coupled Modeling , coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the  Earth System Grid Federation (ESGF)  for archiving the data and providing access, and the multiple funding agencies that support  CMIP6  and ESGF. This work is performed under the auspices of the  U. S. DOE  by  Lawrence Livermore National Laboratory (LLNL)  under Contract No. DE-AC52-07NA27344. Efforts of JL, PJG, MA, AO, PU, KET, PD, CB, MDZ, LC, and BD were supported by the  Regional and Global Model Analysis (RGMA)   program  of the  U. S. Department of Energy (DOE) Office of Science (OS), Biological and Environmental Research (BER)  program.
MFW was supported by the Director, OS, BER of the  U. S. DOE  through the  RGMA program  under Contract No. DE340AC02-05CH11231. AGP was supported by  U. S. DOE  through  BER RGMA  through Award Number  DE-SC0022070  and via  National Science Foundation (NSF )  IA 1947282 , and by  National Center for Atmospheric Research (NCAR) , which is a major facility sponsored by the  NSF  under Cooperative Agreement No. 1852977. YYP and EG were supported by the  Agence Nationale de la Recherche ARISE  project, under Grant  ANR-18-CE01-0012 , and the  Belmont  project  GOTHAM , under Grant  ANR-15-JCLI-0004-01 , the  European Commission 's  H2020 Programme " Infrastructure for the European Network for Earth System Modelling Phase 3 (IS-ENES3) " project under Grant Agreement  824084. DK was supported by the  New Faculty Startup Fund  from  Seoul National University  and the  KMA R&D program  ( KMI2022-01313 ).
The authors thank  Program Manager   Renu Joseph  of the  U. S. DOE  for the support and advocacy for the  Program for Climate Model Diagnosis and Intercomparison (PCMDI)  project and the PMP. We thank  Stephen Klein  for his leadership for the PCMDI project from 2019 to 2022. We acknowledge contributions from our LLNL colleagues,  Lina Muryanto  and  Zeshawn Shaheen  (Now at  Google LLC ) during the early stage of the PMP, and  Sasha Ames ,  Jeff Painter ,  Chris Mauzey , and  Stephen Po-Chedley  for the PCMDI's CMIP database management. The authors also thank  Liping Zhang  for her comments during GFDL's internal review process.
Code and Data Availability The source code of PMP (Lee et al. , 2023b)  is available as an open-source Python package: https://github. com/PCMDI/pcmdi_metrics (last access: 21 November 2023) with versions archived on Zenodo DOI: https://doi. org/10. 5281/zenodo. 592790 (last access: 21 November 2023). The PMP results database (Lee et al. , 2023a) that includes calculated metrics is available on the GitHub repository at https://github. com/PCMDI/pcmdi_metrics_results_archive (last access: 21 November 2023) with versions archived on Zenodo DOI: https://doi. org/10. 5281/zenodo. 10181201. The interactive visualizations of the PMP results are available on the PCMDI website at https://pcmdi. llnl. gov/metrics (last access: 21 November 2023). The CMIP5 and CMIP6 model outputs and obs4MIPs datasets used in this paper are available via the Earth System Grid Federation at https://esgf-node.
Image analyses [timing: 2-3 days onwards] The images obtained from the 3D immuno-RNA/DNA FISH experiments can be analyzed by various available software packages. We have successfully used ImageJ/FIJI (free software from the NIH), Imaris (Oxford instruments) and Volocity (PerkinElmer) softwares. In order to map the exact location of a gene within the nucleus, in our lab we have been extensively using the Volocity 3D Visualization module which has the ability to visualize, identify, quantify and separate objects between them (ex. nuclei and IF or FISH signals). Based on the 3D reconstructed images, Volocity 3D Visualization module can determine the volume of the cells, define the exact location of the objects of interest (subnuclear compartments, genes/alleles etc. ), and distinguish the distances between the center of the fluorescent signal (alleles) and the edge of the nucleus as defined by DAPI staining (or IF signal like LaminB1).
Acknowledgments We would like to thank  Alexandra da Silva Babinet  and  Delphine Ndiaye-Lobry  for the help with experimental procedures and for critical discussing of the protocol. JC was supported by the  ATIP-Avenir program  from the  French government , the  Fondation ARC , the  Institut National du Cancer  ( PLBIO-2020-097 ) and the  Agence Nationale de la Recherche  ( ANR-20-CE15-0014 ). CS was supported by the  European Union (European Social Fund ESF ) and Greek national funds through the  Operational Program 'Education and Lifelong Learning ' of the National Strategic Reference Framework (NSRF) Research Funding Program ARISTEIA [MIRACLE 42] and by  FONDATION SANTE.
Context. The present study addresses a key question related to our understanding of the relation between void galaxies and their environment: the relationship between luminous and dark matter in and around voids. Aims. To explore the extent to which local Universe voids are empty of matter, we study the full (dark+luminous) matter content of seven nearby cosmic voids that are fully contained within the CosmicFlows-3 volume. Methods. We obtained the matter-density profiles of seven cosmic voids using two independent methods. These were built from the galaxy redshift space two-point correlation function in conjunction with peculiar velocity gradients from the CosmicFlows-3 dataset. Results. The results are striking, because when the redshift survey is used, all voids show a radial positive gradient of galaxies, while based on the dynamical analysis, only three of these voids display a clear underdensity of matter in their center.
CAVITY Calar Alto Observatory selected the CAVITY project as one of the three Legacy projects that will define the Calar Alto Observatory science and technology horizon for the coming years. The CAVITY project main goals are to determine the influence of the environment on the mass assembly of void galaxies, to establish how galaxy formation depends on the larger-scale environment, and to identify the main driver of galaxy transformation in voids. To carry out this detailed study, the CAVITY galaxy sample was selected using the Catalogue of Cosmic Voids based on SDSS DR7 data  (Pan et al. 2012). The selected redshift range is between 0. 005 and 0. 05 in order to obtain photometric and spectroscopic data reaching high-angular-resolution and faint galaxies in order to have a representative sample of galaxies within voids.
CosmicFlows-3 In order to assess the dynamics of the voids in the CAVITY sample, we use the matter distribution implied by the peculiar galaxy velocities in the CosmicFlows-3 (CF3) catalog. The third release of the CosmicFlows catalog compiles about 18 000 measurements of galaxy distances and provides a corresponding cosmography on a three-dimensional grid in supergalactic coordinates, which is used in this article. The recently released fourth version of the CosmicFlows catalog (CF4) delivers about 56 000 galaxies and about 1000 Type Ia supernovae distance measurements  (Tully et al. 2023). The combined measurements of galaxy luminosity distances and recessional velocities in the CF3 catalogue allows us to map the full matter overdensity field in the local Universe out to z < 0. 05. The observational data are combined within an iterative forward modeling analysis  (Graziani et al.
This explains why the galaxy counts may hint at a local cavity, while the CF3 velocity flow would suggest otherwise. Within the context of the hierarchically evolving void population, and the role of the surroundings in the evolution of voids, we may also find the merging of voids with overdense walls or filaments. A recent theoretical study  (Vallés-Pérez et al. 2021)  found that ∼10% of the mass in voids at z = 0 may be accreted from overdense regions, with this value even reaching beyond 35% for a significant fraction of voids. While the present analysis of a limited void sample demonstrates the potential for studying voids in relation to their large-scale environment, we expect exciting and statistically representative results for the Local Universe void population from the recently released CosmicFlows-4 dataset of galaxy distances, which includes the entire SDSS volume  (Courtois et al.
Acknowledgements. H. C. is grateful to the  Institut Universitaire de France  for its support. H. C. , M. A. , D. G. acknowledge support from the  CNES. Rvd. W. is grateful to  Miguel Aragon-Calvo ,  Job Feldbrugge ,  Roi Kugul ,  Bernard Jones ,  Georg Wilding  and  Raul Bermejo  on numerous discussions on the nature of voids and the role of topological bias. E. F. is grateful to the  Spanish 'Ministerio de Ciencia e Innovación  and from the  European Regional Development Fund (FEDER)  via grants  PID2020-224414GB-I00  and  PID2020-113689GB-I00 , and from the  'Junta de Andalucía' (Spain) local government  through the  FQM108  and  A-FQM-510-UGR20  projects. L. G. acknowledges financial support from the  Spanish Ministerio de Ciencia e Innovación (MCIN) , the  Agencia Estatal de Investigación (AEI)   10.
13039/501100011033 , and the  European Social Fund (ESF)  " Investing in your future " under the  2019 Ramón y Cajal program   RYC2019-027683-I  and the  PID2020-115253GA-I00   HOSTFLOWS  project, from  Centro Superior de Investigaciones Científicas (CSIC)  under the  PIE  project  20215AT016 , and the program  Unidad de Excelencia María de Maeztu   CEX2020-001058 -M. R. G. B. acknowledges financial support from the grants  CEX2021-001131 -S funded by  MCIN/AEI/10. 13039/501100011033and PID2019-109067-GB100. K. K. gratefully acknowledges funding from the  German Research Foundation (DFG)  in the form of an  Emmy Noether Research Group  (grant number  KR4598/2-1 , PI: Kreckel). S. P. and V. Q. acknowledge support by the  Agencia Estatal de Investigación Española (AEI ; grant  PID2019-107427GB-C33 ), by the  Ministerio de Ciencia e Innovación (MCIN)  within the  Plan de Recuperación, Transformación y Resiliencia del Gobierno de España  through the project  ASFAE/2022/001 , with funding from  European Union NextGenerationEU  ( PRTR-C17.
vaga, strongly suggests that it might be a key actor involved in bdelloid rotifer DNA repair in somatic nuclei. Analysis performed with our recently developed "Alienomics" pipeline  25  strongly suggests that the IR-induced DNA ligase of A. vaga is a horizontally transferred gene (Supplementary Table  S2 ). The four copies of the ligase gene have a slightly higher GC content than the A. vaga genome average (35. 1% vs 31. 0%), their read coverage is the same as the average coverage along the genome, and most importantly the BLASTp analysis showed that the most closely related sequences found in public databases do not belong to other metazoans but to members of other eukaryote groups (i. e. , Amoebozoa, fungi, Ciliophora and Chromista; Supplementary Table  S2 ). Detailed sequence analysis of the IR-induced DNA ligase of A.
vaga shows that it is most similar to the ligase E family of DNA ligases characterized by the presence of two functional domains, the ubiquitous ATP-dependent DNA ligase domain (pfam PF01068) and a specific OB-fold domain (pfam PF14743) (Fig. 2 )  34. Phylogenetic analyses revealed that over half of the members of this family are found in bacteria (>50% in Fig. 2  and >69% in Supplementary Fig. S2A ), but other representatives were also identified in several non-metazoan eukaryote groups and in few animals (Fig. 2  and Supplementary Fig. S2A ). In the phylogeny we observe that both homoeologous pairs A and B of A. vaga, named Fig. 1  | Upregulation of a DNA ligase upon irradiation of the bdelloid rotifer Adineta vaga at 1 kGy of X-ray. a Global proteomic analysis. Total proteins were extracted from around 100,000 rotifers at t0h, t4h, t24h, and t72h post-irradiation.
2  and Supplementary Fig. S2 ). DNA ligases E form an evolutionary distinct lineage from the eukaryotic DNA ligase 1, 3 and 4 families and the prokaryotic DNA ligase B, C, and D families  34, 35  (Fig. 2 ). Members of DNA ligase 1, 3, and 4 families were also found in the A. vaga genome, but only AvLigE-A and -B were identified as horizontally acquired and only AvLigE-B responded to IR in our proteomic analysis. DNA ligase K sequences from kinetoplastids were also added to the phylogenetic analysis because AvLigE was initially proposed to belong to this group of ligase K  30. The structural organization of ligase K (including the presence of the two core domains pfam PF01068 and PF14743) indeed suggests that it is related to ligase E, but it is more divergent from AvLigE-A and B than other members of the ligase E family.
2 ). This structural variation might be related to different activities of the proteins, AvLigE-B being strongly induced upon desiccation or radiation stress in A. vaga but AvLigE-A not or much less  30. The PBZ domains of both AvLigE-A and AvLigE-B are surrounded by proline-serine-threonine (PST)-rich repeats. Such PST-repeats have recently been shown to promote H2AX-independent interaction between DNA and the DNA repair protein MDC1, serving as docking platform for other repair proteins at the site of DNA damage  41. Fig. 2  | Phylogeny of the induced DNA ligase in Adineta vaga, deriving from DNA ligase E. The sequence of the DNA ligase over-represented in A. vaga following exposure to X-ray was compared to the available bdelloid rotifer sequences and to the uniref50 database using blastp (evalue 1e-05).
1% (v/v). The sample was loaded directly on the analytical column at a constant pressure of 800 bar. The digest (1 µl) was injected, and the organic content of the mobile phase was increased linearly from 2% B to 15% in 18 min, from 15% B to 25% in 9 min, from 25% B to 37% in 3 min and from 37% B to 95% in 5 min. Data acquisition on the tims TOF Pro was performed using Hystar 5. 0 and otof-Control 6. 0. tims TOF Pro data were acquired using 160 ms TIMS accumulation time, mobility (1/ K0) range from 0. 7 to 1. 4 Vs/cm². Mass-spectrometric analysis were carried out using the parallel accumulation serial fragmentation (PASEF) acquisition method. One MS spectra followed by six PASEF MSMS spectra per total cycle of 1. 16 s. Two injections per sample were done. Data analysis was performed using PEAKS Studio X+ with ion mobility module and Q module for label-free quantification(Bioinformatics Solutions Inc.
). Protein identifications were conducted using PEAKS search engine with 15 ppm as parent mass error tolerance and 0. 05 Da as fragment mass error tolerance. Carbamidomethylation was allowed as fixed modification, oxidation of methionine and acetylation (N-term) as variable modifications. Enzyme specificity was set to trypsin, and the maximum number of missed cleavages per peptide was set at 2. The peak lists were searched against the protein database from Adineta vaga (NCBI, assembly ASM2161353v1) containing 33,103 entries. Peptide spectrum matches and protein identifications were normalized to less than 1. 0% false discovery rate. Label-free quantitation (LFQ) method was based on expectation -maximization algorithm on the extracted Ion chromatograms of the three most abundant unique peptides of a protein to calculate the area under the curve.