dataesr/scientific-paragraphs-categorization huggingface dataset:
A superposition-free method for comparing alternative ensembles has also been worked out  (41) , and allosteric regulatory mechanisms operating in the heterogeneous ensemble of IDPs/IDRs (multistery) have been elaborated  (42, 43). The appreciation of the importance of structural disorder in the novel field of liquid-liquid phase separation (LLPS) is on the rise (44) and persistent structural disorder of phase-separating proteins even in the condensed state has been reported  (45, 46). Last, but not least, many ensembles have been solved  (24, 47)  but not made publicly available. This rapid progress in the protein disorder field mandates a basic upgrade and significant update of PED. To meet this goal, PED 4. 0 was completely redesigned and extended with several new functionalities. To set a higher standard for the quality of data, a new submission process is now carried out through a web interface that enables automated validation of the ensemble deposited by the authors and manual curation steps with the assistance of the database biocurators.
Database structure and implementation One of the major changes since the previous version is the whole new deposition process, which includes an automatic data validation step and a curation step. The validation has been introduced to standardize the data and improve its quality by providing a number of structural indicators, while the manual curation step provides metadata for better data accessibility. A team of biocurators standardize the description of the experimental methodology using terms from a controlled vocabulary and identify cross-references to third-party databases. Curators also scan the literature to collect ensembles that have not yet been deposited into PED. In PED, an entry is identified by the PED prefix and 5 digits (e. g. PED00001), which corresponds to an experiment on a protein (or protein complex), while a PED ensemble (e.
The backend of the PED server processes each entry submission. The server executes a collection of scripts developed in-house that generate summary statistics (solvent accessibility, secondary structure populations, radius of gyration and maximum dimension). Secondary structure and solvent accessibility are calculated by DSSP (50,51), while MolProbity (52) provides quality descriptors (torsion-angle outliers, covalent bond-length and angle outliers, betacarbon deviations and steric clashes). For each entry, the pipeline generates a report, which can be used to assess a submission. Since the same approach is used for all entries, it is possible to make comparisons across the entire database and generate meaningful statistics. The report is available for download as a PDF document for all entries.
New entries The number of entries in PED 4. 0 has increased six-fold compared to the previous release. Some entries have been deposited after literature curation, while others have been directly provided by the experimentalists who generated the data (data owners). Previous entries were manually reviewed and re-annotated. Old entries that included different experiments were split up. The mapping from old to new identifiers is reported on the website (URL: https:// proteinensemble. org/help#mapping). For new entries, PED curators focused on biologically interesting protein regions with conformational ensembles, or more often, a set of ensembles determined under different conditions (different construct or mutant, different pH, denaturants etc. ) or using different types of experimental datasets and modeling methodology.
As sensitivity to conditions is well-known for IDPs, these alternate ensembles might provide very valuable insights into the conditional disorder of these proteins  (41). Furthermore, multiple ensembles for a region measured under very similar condi- tions may highlight the biases in the modeling protocols and procedures  (41). The curation efforts focused primarily on the submission of larger conformational ensembles (min. 40 conformers for a given protein region) of preferentially IDRs determined using experimental constraints. The Dis-Prot database was harnessed to make sure many of the additions correspond to bona fide experimentally determined IDPs/IDRs. This was complemented by an analysis of the radii of gyration (R g ) of the protein chains.
Statistics Statistical analysis of the PED entries (Figure  1 ) shows an increment in all classes of determination methods and in all sizes of ensembles (i. e. number of models ranging from a dozen to thousands). It also highlights that while NMR remains as the most highly represented method used to model ensembles of usually <100 conformers, other state-of-theart methods such as SAXS and smFRET are also represented in the new dataset. Large ensembles (number of conformers >50) determined by SAXS only are absent in PED, as currently most SAXS-based ensemble modeling tools are known to represent ensembles through several equivalent data sets, reducing the number of representative models in each set to a range of 10-50  (15, 53). However, larger ensembles generated by a combination of SAXS, smFRET and molecular dynamics methods are present.
Entry views The Entry page (Figure  4 ) provides the title of the experiment, authors, and the corresponding publication when available. PED does not include primary data, like structural constraints, but instead provides cross-references to primary databases; when available (PDB (4), BMRB  (48)  and SASBDB (  49 )). MobiDB  (36)  and DisProt  (35)  are cross-referenced in order to link evidence about the intrinsic disorder of the protein region. For each entry, the PED biocurators generate a detailed description of the ensemble determination. This description about experimental and computational protocols is organized into three different blocks (experimental procedure, structural ensemble calculation and, if applicable, MD calculations), each including a narrative and a set of terms selected from a controlled vocabulary (CV).
Browse and search Browse and advance search are implemented on the same page. A customizable table lists all entries with information about the protein, types of measurements, number of ensembles and conformers. Each row represents a chain of an ensemble or a fragment in cases when the ensemble is calculated on an engineered construct. The corresponding UniProt accessions are provided for the majority of the PED entries. A search box allows the user to look up specific words in a free-text form or to search PED and all crossreferenced identifiers. Moreover, it is possible to search all the terms from the controlled vocabulary and to build complex queries or exploit regular expressions. Simple search is also available on the Main page, while programmatic search and data access (or download) is implemented via a RESTful API.
CONCLUSION After several years of steadily diminishing activity, PED has finally come to new life. First, it has been transferred to a stable location that ensures continuous maintenance and regular updates, hopefully stimulating the de-velopment of novel approaches--experimental and computational tools--for developing and depositing ever more accurate ensembles. Second, it has been significantly extended in size and has a greatly improved representation of ensemble-generation methodologies and of functionally validated 'bona fide' IDRs, thanks to a community-wide curation effort. The number of entries has increased from 24 to 152, whereas the number of ensembles has grown from 60 to 215. In all, the total number of 'conformers' stored in the database now exceed 290,532 PDB models (versus 24 615 in the old PED).
PED has also been profoundly upgraded in a quest for better consistency. The most important novel feature is the implementation of a new deposition process aimed at improving the quality of the entire database. PED now includes a web submission system. Each deposition is subjected to an automatic validation step, which generates a report on model quality, and a manual curation step, in which a submission is manually evaluated and integrated with structured metadata. The automatic validation step includes statistics on bond angles and lengths, backbone torsion angles and steric clashes. Whereas statistics on 'outliers' in the various geometric categories do not entail the rejection of deposition, it gives the user the option of selecting only ensembles that meet certain preset quality criteria.
The biocurator submission interface will soon be made available to the public with the idea of providing a tool similar to the OneDep system of the wwPDB (4) in the near future. Contributing new ensembles is highly encouraged, and for that, information about submission inquiries are available on the Deposition page. Additional novel features of PED 4. 0 include a completely new implementation of the website and database schema. PED stores ensemble weights representing conformational probabilities. Even though these are not taken into account in the calculation of ensemble properties due to a lack of standardization, they will be extensively integrated in the future. The web interface has both a protein-and experiment-centric view, an advanced search engine and a well-documented API for programmatic access.
The quick and significant growth of PED is due to the steady activity of experimentalists generating disordered ensembles that accumulated large amounts of data in the past years, and the perseverance of database curators. This signals the vitality of the concept of protein disorder and the strength of the disorder community working on integrating structural, functional and medical aspects of structural disorder. We expect that the new database will foster a significant conceptual leap in the field. Even today, after more than two decades of research that has brought solidification of the basic concept, we still tend to perceive structural disorder as a binary classifier, thinking of proteins or protein regions as either ordered or disordered. Structural disorder, however, is not a simple, homogeneous structural state, it rather represents a continuum of states from fully ordered to fully disordered  (58).
PED is currently the only database focused on representing the diversity of IDP protein ensembles, which are not stored in databases focused on the deposition of primary data (SASDB, BMRB, PDB), creating an extremely valuable resource for the IDP community. The analysis of ensembles in PED 4. 0 will enable us to better un-derstand determinants of these various sub-states in terms of compactness, secondary structure content and dynamics, which will definitely help correctly interpret the functional consequences of intrinsic structural disorder. Given the prevalence of structural disorder in disease  (59) , the insight expected from structural ensembles in PED 4. 0 will also give a new impetus to efforts of structure-based drug discovery against IDPs. The renewal and relocation of the database reflects on the ambition of the IDP community to actively maintain the database and, more ambitiously, also to integrate it into DisProt's IDP-specific complex ecosystem of databases and computational tools  (60).
Significant further developments in the near future, such as mirroring the database among multiple locations and contacting journals to recommend ensemble deposition into PED, are also planned. Continuous maintenance and implementation of these and other future plans are ensured by the IDPcentral, MSCA-RISE IDPfun and ELIXIR IDP community groups. To ensure communication with users about recent growth of the database and new features, PED now will have a more active social media presence on Twitter with the original @Pro-teinEnsemble Twitter account.
FUNDING Italian Ministry of University and Research (MIUR)  to SCET [2017483NH8]; European Union's  Horizon 2020  to SCET [778247];  Hungarian Scientific Research Fund (OTKA)  to PT [ K124670 ,  K131702 ];  Universidad Nacional de Quilmes  to GP [ PUNQ 1309/19 ];  National Agency for the Promotion of Science and Technology (ANPCyT)  to GP [PICT-2014-3430] and to LBC [PICT-2017-1924];  Fondation pour la Recherche Médicale  to SRB and SDV [DBI20141231336];  Natural Sciences and Engineering Research Council of Canada  to  CCG [RGPIN 2017-06030 ];  Agence Nationale de la Recherche (ANR)  to  PB  [ ANR-10-LABX-12-01 ];  National Institutes of Health (NIH)  to JFK and THG [ 5R01GM127627-03 ];  German Ministry of Science and Education (SAS-BSOFT)  to DS [16QK10A];  EU   Horizon 2020 programme (iNEXT-Discovery ) to DS [871037];  Vrije Universiteit Brussel (VUB)  to PT [SRP51]; EM-P, NG, NM, JM are PhD students, AJVR, TES are Postdocs and GP, CM-B, JI, LBC and MSF are researchers of the  National Research Council (CONICET) of Argentina.
Introduction The use of satellite observations in data assimilation systems has greatly advanced numerical weather prediction (NWP) models. In particular, observations from infrared sounders have significantly improved the quality of weather forecasts (e. g. Hilton et al. , 2012; Guidard et al. , 2011; Collard and McNally, 2009). The Infrared Atmospheric Sounding Interferometer (IASI) is one of the most important satellite instruments supporting NWP centres. This sounder was jointly developed by the Centre National d'Études Spatiales (CNES) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). The IASI spectrum ranges from 645 to 2760 cm -1 with a spectral sampling of 0. 25 cm -1 , leading to a set of 8461 radiance measurements with a spectral resolution of 0.
5 cm -1 after Gaussian apodization. The high volume of data resulting from hyperspectral infrared sounders such as IASI presents many challenges, particularly in the areas of data storage, computational cost, information redundancy and information content, for example. The methods for reducing the data volume are channel selection, spatial sampling or principle component analysis. Channel selection is an effective approach to reduce the amount of observations to be assimilated. One of the most widely used methods is derived from the theory by  Rodgers (1996 Rodgers ( , 2000)) , which describes an iterative method to determine an optimal set of channels based on their information content. A study by  Rabier et al. (2002)  has highlighted an iterative method that sequentially selects the channels with the highest information content.
, 2000)  for the Action de Recherche Petite Échelle Grande Échelle (ARPEGE) global model  (Courtier et al. , 1991). The 4D-Var method consists of correcting a background from a short-range forecast  (Lorenc, 1986; Courtier et al. , 1994)  by observations along an assimilation window, allowing users to estimate the atmospheric state. This "analysis" state is thus used as initial condition in the NWP models. Assimilated radiances from IASI (a subset of 124 channels from Collard's selection) represent more than 60 % of all assimilated observations (conventional and satellite) in the 4D-Var data assimilation process. The contribution of an observation to the variational data assimilation system is strongly influenced by the observation error. So far, observation errors have usually been assumed to be uncorrelated horizontally (thinning) and spectrally.
In order to ensure a robust selection for NWP, specific attention has been paid to the estimation of the observation-and backgrounderror covariance matrices and to the consideration of various atmospheric scenarios. These selections were evaluated in one-dimensional variational (1D-Var) data assimilation experiments. Section 1 describes the methodology for this study, including information on the data, the models used and some theoretical reminders; Sect. 2 presents the preliminary and main results for the selection of channels (observation-, background-error covariance and Jacobian matrices); then Sect. 3 proposes a new selection of IASI channels and finally conclusions and perspectives are provided in Sect. 4.
In summary, the 6123 profiles are used in the following study for the estimation of the observation-error covariance matrix and at the end to evaluate the channel selections in the 1D-Var data assimilation system. Channel selection was performed from a subset of 60 profiles (and pixels) empirically selected from the 6123 profiles. These 60 profiles were chosen to have a variability close to the set of 6123 profiles. The location of these profiles is shown in Fig. 1. To ensure sufficient variability in our set of 60 profiles, we have calculated, and illustrated in Fig. 2 , the mean (black solid line) plus and minus standard deviation (shaded area) and the minimum and maximum values (black dashed line) of the temperature (a), humidity (b) and ozone (c) profiles. There is significant variability that is similar to that obtained with the profiles in the database available in the RTTOV RTM  (Chevallier et al.
Assimilation system The NWP SAF one-dimensional variational (1D-Var) data assimilation algorithm  (Smith, 2016)  is based on the optimal estimation method (OEM)  (Rodgers, 2000). The unidimensionality makes this algorithm fast, flexible and suitable for research purposes close to NWP operational frameworks. Similar to other variational data assimilation algorithms (e. g. 3D-VAR and 4D-Var), the objective of the 1D-Var is to minimize both the observational and background deviation by minimizing a cost function J. Assuming that the background error is not correlated to the observation error and the errors have a Gaussian distribution, we retrieve state x by minimizing the cost function such as J (x) = 1 2 x -x b T B -1 x -x b + 1 2 y -H(x) T R -1 y -H(x) , (6) where x b is the background profiles, y is the IASI observations, H(x) represents the BTs which are simulated by RT-TOV, and B and R are the background-and observationerror covariance matrices, respectively.
IASI observation errors A correct estimation of observation errors is essential in the data assimilation process. Until a few years ago, only the variances of these errors were taken into account (diagonal R matrix). Then innovative techniques to determine these errors and their correlations more accurately by deriving estimates of the real observation error from the departure statistics from assimilation systems emerged to be used for operational NWP (e. g. Hollingsworth and Lönnberg, 1986; Desroziers et al. , 2005). Several research works have successfully applied these methods to infrared hyperspectral instruments in order to estimate their total observation errors (instrumental noise, spatial representativeness error, error in the calculation of radiative transfer, etc. ). For IASI, many NWP centres are starting to use R matrices that take into account cross-channel error correlations with significant benefits in terms of forecast impact.
This is the case at the Met Office  (Stewart et al. , 2014; Weston et al. , 2014) , the Environment and Climate Change Canada  (Heilliette and Garand, 2015) , Météo-France (Vincent Guidard, personal communication, 2019) and the European Centre for Medium-Range Weather Forecasts (ECMWF)  (Bormann et al. , 2016). However, these observation errors have been estimated for already selected IASI channels. Considering the significance that inter-channel error correlations can have in the data assimilation process, they should also have a particular influence on the selection of the most informative channels. Some works have consequently carried out new selections of IASI channels using R matrices that take into account interchannel observation-error correlations (e. g. Migliorini, 2015; Ventress and Dudhia, 2014).
A large part of the possible maximum total DFS is reached quickly since 90 % of the maximum total DFS over the 400 channels is achieved with only 172 channels. The maximum skin temperature DFS is obtained very quickly as only three channels are sufficient to provide more than 90 % of the maximum skin temperature DFS over the 400 selected channels. The humidity DFS also increases very quickly. Finally the total DFS with 400 selected channels consists of 50. 3 % temperhttps://doi. org/10. 5194/amt-13-2659-2020 Atmos. Meas. Tech. , 13, 2659-2680, 2020 Figure  7. Observation-error standard deviation from FG departures standard deviation with a red line, diagnosed observation errors from Desroziers' method using 1D-Var data assimilation system with a blue line and instrumental noise at 280 K in grey with respect to 5499 IASI channel number and wavenumber (cm -1 ) (bands 1 and 2 without channel 1194) over the set of 6123 atmospheric profiles (a).
Evaluation We evaluated CS275, NS275 and a selection of 400 channels (named NS400) by assimilating them into the 1D-Var (Eq. 6). We used the diagnosed observation error covariance matrices with the appropriate number of channels for each selection. Data assimilation experiments were performed on the 6123 profiles in order to closely approximate the variability of the operational NWP models. In a first step, the DFS mean values of the 6123 profiles for the three selections were calculated. The mean vertical profiles of the DFS for the three selections (Collard in black, the new selection with 275   3. Compared to CS275 and the equivalent number of channels the NS275 increases the information content since the DFS for temperature is increased by 0. 62, for humidity by 0. 23 and for ozone by 0.
In these studies, the R matrix was estimated using a bottom-up method which represents the R matrix as a sum of random and spectrally correlated components. The  Desroziers et al. (2005)  diagnostic is an efficient method to estimate the observation-error covariances accurately. We used this method as a top-down method that uses first guess and analysis departure statistics to diagnose variances and covariances of observation error. It is this method we have chosen to use here to diagnose our R matrix for the 5499 IASI channels considered. We used the 1D-Var data assimilation algorithm to perform assimilation experiments on 6123 atmospheric profiles (and IASI pixels) in order to have a statistically robust sample to diagnose the R matrix and to approximate the possible variabilities that can be found in an operational setting.
However, none are sensitive to CO (2100-2150 cm -1 ) since the selection was limited to channels up to 2019. 75 cm -1 (more details in Appendix A). The use of inter-channel error correlations exploits the multi-informative potential of the available channels to the atmospheric window and ozone channels. These results can bring significant improvements in the use of IASI observations by data assimilation systems and be useful for weather forecasting. In the near future, CS275 and NS275 will be evaluated in the 4D-Var data assimilation of the ARPEGE NWP global model and possibly the NS400 selection. The set of 400 selected channels is given in Appendix B.
Code availability. The radiative transfer model RTTOV (Saunders  et al. , 2018)  and the unidimensional data assimilation system 1D-Var  (Smith, 2016)  are developed within the framework of the EU-METSAT Satellite Application Facility on Numerical Weather Prediction (NWP SAF). The partners in NWP SAF are the Met Office, ECMWF, DWD and Météo-France. They are available from the NWP SAF website https://www. nwpsaf. eu/site/software (NW-PSAF, 2020). Data availability. IASI data are available from EUMETSAT https: //www. eumetsat. int (EUMETSAT, 2020). All other data used for this study can be obtained by emailing olivier. coopmann@umrcnrm. fr.