dataesr/scientific-paragraphs-categorization huggingface dataset:
The Generalized Retrieval of Aerosol and Surface Properties is a new-generation algorithm developed for deriving extensive aerosol properties from POLDER-like observations. The overall concept of the algorithm is described by Dubovik et al. [79]  , while specific aspects are detailed in Dubovik et al. [28]. An open-source GRASP-OPEN software version and documentation are available from https://www. grasp-open. com. The algorithm is based on highly advanced statistically optimized fitting implemented as multi-term least square minimization  [76]  that had earlier been successfully implemented (e. g. , see Refs. [75, 78, 80]  ) for aerosol retrievals from ground-based AERONET radiometers. POLDER/GRASP shares its methodology with AERONET retrievals. For example, for each individual pixel it uses multiple a priori constraints such as smoothness limitations on the retrieved continuous functions including the size distribution, spectral dependencies of the refractive index, and BRDF parameters.
As a result, GRASP provides reliable retrievals of detailed aerosol properties that have traditionally been difficult to obtain from satellites, such as aerosol absorption and type. GRASP utilizes radiances in six wavelengths and polarized radiances in three wavelengths and performs radiative-transfer modeling fully accounting for multiple interactions of the scattered solar light in the atmosphere at the native POLDER-1 and -2 ∼7 km and POLDER-3 ∼6-km resolution. Since these complex computations are done online, significant effort s have been f ocused on the optimization and acceleration of the GRASP routines and on adapting the code for operational processing of voluminous datasets. Presently, 18 months of POLDER-1 and -2 and nine years of POLDER-3 observations have been processed and two versions of the retrieval product have been archived at the AERIS/ICARE Data and Services Center ( http://www.
icare. univ-lille1. fr ). Some information is also available from the GRASP-OPEN site ( https:// www. grasp-open. com ). These aerosol products have been evaluated against AERONET data. A detailed description of the POLDER/GRASP product can be found in Dubovik et al. [73]. Also, following the positive POLDER/GRASP retrieval experience, versions 3MI/GRASP, HARP/GRASP, SGLI/GRASP, PCF/GRASP, and Aerosol-UA/GRASP are under development. Aerosols above clouds with POLDER-3 on PARASOL. Waquet et al. [248, 249]  have developed an advanced LUT method for retrieving the properties of aerosols above clouds from POLDER/PARASOL polarization measurements. The method for retrieving the abovecloud AOT consists of a comparison between the polarized radiances measured by POLDER at 670 and 865 nm and polarized radiances pre-computed with a successive-order-scattering computer program  [135]  for different aerosol models.
PI-Neph Nephelometers such as the PI-Neph provide direct measurements of the aerosol scattering matrix, total scattering, and absorption. Such measurements are not affected by complex multiplescattering interactions of light and therefore can be used for validating and verifying aerosol models used in satellite and ground-based retrievals. PI-Neph data can be used for deriving a detailed size distribution, complex refractive index, and information about particle nonsphericity. The retrieval of these properties from PI-Neph data was first demonstrated on laboratory measurements of monodisperse polystyrene spheres by Dolgos and Martins  [72]. The GRASP software was later used to extend these retrievals to airborne measurements of polydisperse particles in studies by Espinosa et al. [81, 82].
Li et al. demonstrated the applicability of this algorithm to POLDER-3 and AERONET data. It was shown that other aerosol parameters retrieved from POLDER-3 data, such as the spectral AOT and SSA derived using the GRASP/composition approach, are determined comparably well or even better than when using the retrieval of a spectrally dependent refractive index. This approach can yield not only a new type of parameter but also serve as an improved constraint on the solution. Also, the tool for rigorous calculation of radiative fluxes and aerosol forcing based on the retrieval result has been implemented as and optional module in the GRASP code  [60]. Yet one should recognize that aerosol typing as well as the retrieval of chemical composition always contain ambiguities due to limitations of the information content of satellite observations.
By now, several aerosol datasets have been generated from nine years worth of archived PARASOL data which are used for in-depth analyses of the global variability of aerosol and validation of products derived from other observations. Presently, several more satellite polarimetric missions are being flown, including two Chinese polarimeters CAPI/TanSat and DPC/GF-5 as well as the SGLI/GCOM-C launched by Japan. The fleet of polarimeters should increase significantly in next decade following the planned launches of the 3MI/EPS-SG EU instrument, MAP/CO2M EU/Copernicus mission, several USA missions (MAIA, HARP/CubeSat, HARP2/PACE, and SpexOne/PACE), four Chinese instruments (POSP, SMAC, PCF, and DPC-Lidar), and the Ukranian Aerosol-UA mission, all by 2023. Airborne prototypes are being developed to parallel these satellite instruments.
Acknowledgments We thank  M. Alexandrov ,  J. Chowdhary , and  B. van Diedenhoven  for useful discussions. P. Goloub ,  O. Dubovik ,  F. Parol ,  D. Tanré , and  F. Waquet  appreciate support from the  Chemical and Physical Properties  of the  Atmosphere  Project funded by the  French National Research Agency  through the  Programme d'Investissement d'Avenir  under contract  ANR-11-LABX-0 0 05-01  , the  Regional Council "Hauts-de-France", and the "European Funds for Regional Economic Development". B. Cairns  and  M. Mishchenko  acknowledge funding from the  NASA   Radiation Sciences Program  and the  NASA   ACE Program. Z. Li ,  W. How ,  L. Li , and  X. Sun  appreciate funding from the  National Key R&D Program of China  (  2016YFE0201400  ). Part of this research (specifically, on MISR, AirMSPI, AirMSPI-2, and MAIA) is carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under contract with  NASA.
Significant improvements have been made in the efficiency and accuracy of RNA 3D structure prediction methods during the succeeding challenges of RNA-Puzzles, a community-wide effort on the assessment of blind prediction of RNA tertiary structures. The RNA-Puzzles contest has shown, among others, that the development and validation of computational methods for RNA fold prediction strongly depend on the benchmark datasets and the structure comparison algorithms. Yet, there has been no systematic benchmark set or decoy structures available for the 3D structure prediction of RNA, hindering the standardization of comparative tests in the modeling of RNA structure. Furthermore, there has not been a unified set of tools that allows deep and complete RNA structure analysis, and at the same time, that is easy to use.
Here, we present RNA-Puzzles toolkit, a computational resource including (i) decoy sets generated by different RNA 3D structure pre-diction methods (raw, for-evaluation and standardized datasets), (ii) 3D structure normalization, analysis, manipulation, visualization tools (RNA format, RNA normalizer, rna-tools) and (iii) 3D structure comparison metric tools (RNAQUA, MCQ4Structures). This resource provides a full list of computational tools as well as a standard RNA 3D structure prediction assessment protocol for the community.
However, the formation of a 3D structure requires, in addition, non-Watson-Crick base pairs  (10) , structural modules  (11) , and sometimes pseudoknots  (12). Thus, the secondary structure description of RNA structure is insufficient. Precise sequence and covariation analysis  (13) , and/or chemical/enzymatic probing  (14, 15)  are therefore necessary to predict relevant 3D structures. In RNA-Puzzles, we highlight the fact that 3D structure models can severely deviate from the reference structures even if the model retains perfect secondary structure (100% correct in terms of cis-Watson-Crick base pairing)  (6)  (see Supplementary Figure  S1 ). In this context, RNA 3D structure prediction needs independent benchmarking systems that include both datasets and assessment metrics. With the progress in protein structure prediction, many benchmark datasets and assessment metrics have been curated and developed  (16).
One available dataset for RNA structure benchmarking is the non-redundant dataset maintained by  Leontis and Zirbel (17). Alternatively, the Rfam database, which links RNA sequence families with crystallographic structures when available, can also be used in prediction benchmarking  (18). However, only 99 Rfam families have their 3D structures available. Such benchmarks are not blind and are biased towards RNAs with many homologous sequences. This is not always the case in prediction: some rare RNA structures do not necessarily have homologous sequence available, e. g. Varkud satellite ribozyme  (19) , in which case sequence alignment-dependent prediction methods may not be helpful. The RNA-Puzzles benchmark sets have been successfully used in developing RNA quality assessment methods  (20)  to identify the models similar to experimental structures without reference.
Potentially, they will also serve as decoy sets for proposing structure-based force field or scoring functions, RNA design and other utilities. Reliable evaluation of dozens of RNA 3D models cannot be performed manually and is usually preceded by normalization to comply with a common 3D structure representation. Since the start of RNA-Puzzles, a good number of RNA structure manipulation tools and structure comparison metrics, some of which are being used by the RNA-Puzzles community, have been conceived and designed. They are helpful in various ways, including structure analysis, comparison, and function inference. Here, we gather and summarize a computational resource 'RNA-Puzzles toolkit' that includes a set of datasets and various computational tools accumulated in the practice of RNA-Puzzles, which cover important aspects to understand RNA structure.
Datasets We provide three datasets derived from RNA-Puzzles: (i) raw dataset -a dataset of raw submissions, which were generated by various prediction methods, (ii) forevaluation dataset -dataset used for official evaluation of the prediction methods in RNA-Puzzles, which does not change the coordinates of the predicted structures or add missing atoms, and (iii) standardized dataset -a standardized dataset optimized with rna-tools, which not only unified the residue and atom names but also completed the missing atoms in incomplete RNA structures to standardize all the structures to the same format. All the datasets follow the same rules to name the structural files, which is a combination of the RNA-Puzzles identifier, prediction group name, and the structure model number, e. g. 19 RNAComposer 3.
RNA format, RNA normalizer and RNA assessment RNA format, RNA normalizer and RNA assessment constitute a set of computational tools for the data formatting, processing and evaluation in RNA-Puzzles. They are implemented as Python packages making use of the BioPython  (22)  structure I/O library. The algorithms to compute RMSD, P-value  (23) , Deformation Profile, and Interaction Network Fidelity  (24)  are implemented in the Python package RNA assessment, which makes use of BioPython, MC-Annotate  (25)  and NumPy  (26). Deformation Profile was also implemented as an independent Python package.
RNAQUA RNAQUA (RNA QUality Assessment tool) is a RESTful web service client developed in Java using Jersey (https: //jersey. github. io/). It provides services for RNA 3D structure normalization and comparison, including the metrics of RMSD, P-value  (23) , Deformation Profile, Interaction Network Fidelity  (24)  and clash score  (29). It uses selected functions from RNAlyzer  (30)  and RNAssess  (31) , both of which are in the RNApolis platform  (32).
MCQ4Structures MCQ4Structures is a set of computational tools for RNA 3D structure comparison in the torsion angle space. It includes algorithms to compute Mean of Circular Quantities (MCQ)  (33)  and Longest Continuous Segments in Torsion Angle space (LCS-TA) (34) that compare structures, compute structure similarity, cluster and visualize the results, identify similar structural fragments, and rank the structural models. The package is implemented in Java, while functional modules of structure I/O and geometric statistics, on which both MCQ and LCS-TA depend, are implemented as separate packages of BioCommons (https://github. com/tzok/BioCommons) and Circular (https://github. com/tzok/Circular).
RESULTS The overview of the resource Our computational resource includes (i) the benchmark datasets from RNA-Puzzles, (ii) structure analysis, manipulation, visualization, clustering and normalization tools, (iii) and 3D structure comparison metrics (Figure  1 ). Considering an RNA structure comparison workflow given both a list of predicted structures and several reference structures, it is first necessary to standardize the predicted and reference structures to the same length and the same format. Structural features, such as clash score, which is based on the structure model, can be calculated and compared with the scores derived from the reference structures. Furthermore, our resource provides a set of tools for RNA structure manipulation and visualization, which can greatly facilitate manual inspection of the structures.
Benchmark datasets of RNA 3D structure In a structure prediction scenario, a good predictor should be robust in predicting structures of different types accounting for the characteristics of each prediction target. Therefore, a good benchmark must cover diverse structures (Figure  2A ). The datasets from RNA-Puzzles, as listed in Supplementary Table  S2 , cover crucial aspects for the selection of puzzles, such as symmetry  (35) , ion binding  (36) , ligand binding  (37, 38) , protein binding  (39) , the conformational change  (40) , and structural modules  (7). Our datasets include 972 decoy RNA structures for 20 RNAs. They can be used as: (i) a standard dataset to compare with existing prediction methods, e. g. (  41 ); (ii) a decoy dataset to develop effective structure scoring function, e.
g. (20). The theoretical models were generated by the best existing RNA 3D structure prediction programs  (21, (42) (43) (44) (45) (46). The similarities of these theoretical models to crystal structures range from low quality to the near-native (cf. Figure  2  and Supplementary Table  S2 ), which provides a wide range of decoy structures that exist during structure modeling. The presented benchmark dataset can benefit the development of energy function or scoring function to discriminate the near-native structures from those far away decoys. This is an important step to identify high-quality prediction when the reference structure is unknown. In RNA-Puzzles, each group (or each prediction method) provides five candidate models (in the first 17 challenges, up to 10 models were allowed) and ranks these models according to its own prediction reliability index.
Other available datasets of the same kind are: (i) RASP (  48 ) dataset, which includes 85 RNAs with 500 decoys for each structure and (ii) the KB (49) dataset, which includes 23 950 decoys for 20 RNAs. However, the decoy structures in these datasets were generated using only a couple of prediction methods, while our dataset covers a much wider variability in RNA structure prediction. Standardizing the structure format considering all types of variations is the first step of a fair structure comparison. Different prediction methods result in a wide range of variations in the format of the predicted structures, ranging from nomenclature (chain names, residue names, atom names and their ordering) to structural variations (i. e. the structure at the 5 and 3 ends). For example, some prediction methods may use the molecular dynamics force field to minimize the energy of the predicted structure at their final steps, thus the output format depends on the force field used.
Besides, the predicted structures need to be normalized according to the reference structure allowing unsolved fragments. The RNA-Puzzles dataset can be used as (i) a standard dataset to benchmark with existing prediction methods; (ii) a decoy dataset to develop and test effective structure scoring function. To fulfill these two tasks, we provide standardized dataset including structural data standardized and missing atoms completed using rna-tools. rna-tools was used to (i) add the missing atoms, especially at the 5 and 3 ends; (ii) mutate variant nucleotides in the predictions to make them consistent with the sequence of the reference structure. All the steps of processing and the detailed analysis of the differences between predicted models and the references, such as gaps, mismatches, etc.
are described in the README files provided with the structures. The standardized dataset is under active maintenance. The advanced users can also use rna-tools to process their own datasets. The first step is to standardize the formatting of analyzed structural models. Then, the user can run an analysis for a single model, such as Clash Score evaluation or base pair identification using various methods; or, for multiple models, various comparison methods are implemented. The tools can be accessed via command-line or Jupyter. The toolkit can be also executed as a Docker image that can be easily used.
rna-tools rna-tools includes a set of tools dedicated to (i) RNA structural handling and manipulating, i. e. rebuilding missing atoms, (ii) structure clustering, (iii) standardization of RNA structures, (iv) visualization of secondary RNA structures, i. e. drawing RNA arc diagrams of secondary structure, (v) visualization of RNA sequence alignments, and more. The core library shared with the tools. The core part of the rna-tools package is the 'rna pdb toolsx. py' program that was used to prepare the standardized dataset. The program facilitates many tedious operations on structural files. For example, one tool is the 'get-rnapuzzle-ready', which is used to get a standardized naming of atoms, residues, chains to be compatible with the format required by RNA-Puzzles. All structures from the standardized dataset are compatible with this format, which makes it easy to compare them and use for further analysis.
Another example of structure manipulation is introducing mutations. The rna-tools package uses ModeRNA  (25)  to introduce single or double mutations in structures. But it overcomes ModeRNA's limitation in processing only one chain at the time (Figure  3A ). Multiple mutations in multiple chains can be introduced. Furthermore, rna-tools includes tools operating on various levels of RNA data: sequences, secondary structures, alignments, and 3D structures. rna-tools includes a collection of almost one hundred functionalities that facilitate common operations in RNA structural bioinformatics. It can be easily imported into 3rd party programs or pipelines. The full list of functionalities can be found in Supplementary Table  S3. RNA sequence tools. The first group of tools deals with RNA sequences.
The tools help to perform searches us-ing both Blast (50) on the PDB database and Infernal (51) on the Rfam database  (52). Furthermore, multiple wrapper tools of RNA secondary structure prediction are implemented (Figure  3f ), including RNAsubopt, RNAeval, RNAfold from ViennaRNA  (45) , CentroidFold (46), Con-textFold  (47) , MC-Fold  (53)  and IPknot  (54). All tools are compatible with Jupyter Notebook. RNA secondary structure tools. The second group of tools aims to facilitate operations on RNA secondary structure that can be executed from Jupyter Notebooks (Figure  3F ). The functionalities include visualization of a sequence and a structure with VARNA (55), evaluation of free energy, parsing secondary structure into a list of pairs, and various tools for secondary structure format conversions, etc.
RNA 3D structure tools. The last group of tools operates on RNA 3D structure. This group includes (i) tools for the analysis of 3D models (such as contact classifications) and (ii) tools for RNA 3D structure prediction, including the whole pipeline of structure prediction. First, to perform contact classifications, we provide two wrappers, that are ClaRNA  (28)  and 3DNA/DSSR  (57). Using the wrappers together with the PyMOL4RNA tool in rna-tools, it is possible to perform contact classifications in PyMOL for a selected set of residues (Figure  3B ). Second, the package contains scripts to help the RNA 3D structure prediction processes, both for SimRNA (42) (including SimRNAweb (58)), and Rosetta  (59). Tools for SimRNA and Rosetta help to prepare input files, run modeling, cluster results, and extract models from trajectory files.
Moreover, the program for SimRNAweb allows the users to download SimR-NAweb prediction models and trajectory files. For processing trajectories of SimRNA, a Python interface is provided to parse trajectories into atoms, residues, simulation frames to prepare for further analysis. At the final step of a structure modeling process, a user can run the RNA refinement procedure implemented in a wrapper of QRNAS  (60). Auxiliary tools. In the package, there is a set of auxiliary tools of novel functions. One of them is diffpdb. It is a simple tool to compare two files of PDB format to identify the difference in the annotation of atoms, missing atoms, missing fragments (Figure  3e ). Another standalone tool implemented in rna-tools is Clanstix. Clanstix can be used to interactively visualize the clustering results from CLANS  (49).
CLANS uses the Fruchterman-Reingold graph layout algorithm to visualize pairwise sequence similarities in either two-dimensional or three-dimensional space. The program was initially designed to calculate pairwise attraction values to compare protein sequences. However, it is possible to load a matrix of precomputed attraction values and thereby display any type of data based on pairwise interactions. Therefore, the Clanstix program from the rna-tools package can convert the all-vs-all distance (e. g. , Root Mean Square Deviation) matrix into an input file for CLANS. An example of Clanstix is shown in Figure  3C , which is the result for RNA-Puzzle Puzzle 8. Models with a pairwise distance of RMSD lower than 8 Å are connected. The reference structure was added to this clustering. Interestingly, the reference structure was mapped to the small clus-ter with two models from Das's group and two models from Bujnicki's group.
The visualization can provide useful insights into a set of analyzed models or models obtained from a simulation trajectory. Another example of the usage of Clanstix can be found in the publication of EvoClus-tRNA  (61) , which shows how 3D models of various homologous sequences are clustered with respect to each other and the reference models. The documentation with step-by-step tutorials. The description in this publication only briefly reports functionalities implemented in rna-tools. To facilitate the finding of the right tool, the package is well documented in both online documentation and tutorials that will walk the users through various use cases. The step-by-step tutorial that explains how to prepare files for the submission to RNA-Puzzles is also included. Extensibility by design.
The rna-tools package was developed with the goal in mind of providing a framework for various tools specifically to support extensibility. A new script can be easily drafted just by copying-pasting to a new folder in 'rna tools/tools/<new tool>'. Many core functionalities are coded in the 'rna tools lib. py' file that is shared between scripts; hence, the functions can be imported to new scripts. This design speeds up the development of new programs since many of them need some lowlevel common functionalities, e. g. , Python engine for parsing selection of residues, atoms, parsing/converting various types of data.